switch to gemini-flash

2 files changed, 970 insertions, 365 deletions

diff --git a/genecup_synthesis_prompt.txt b/genecup_synthesis_prompt.txt
new file mode 100644
index 0000000..75af3af
--- /dev/null
+++ b/genecup_synthesis_prompt.txt
@@ -0,0 +1,67 @@
+Is GeneSymbol {{gene}} a likely causal gene? 

+

+Below is a prompt you can paste into a large language model to answer this question. Please make sure you edit the research context section to fit your study.

+

+1. Research Context:

+

+Study: Genome-Wide Association Study 

+Organism: 

+Phenotype:

+Candidate Gene: {{gene}}

+

+Goal: To critically evaluate [Candidate Gene] as a plausible causal gene for the [Phenotype] by analyzing the literature excerpts provided at the end of this prompt with appropriate scientific caution.

+

+2. Required Analysis:

+Please perform the following four-step analysis based on the Source Information provided at the end. Your evaluation must be rigorous and avoid overstating claims. Acknowledge the limitations of interpreting isolated sentences and prioritize a nuanced perspective.

+

+A. Term Disambiguation

+

+For each sentence provided in the "Source Information" section, confirm if the term "[Candidate Gene]" unambiguously refers to the intended gene. If the term is used ambiguously or refers to another scientific concept, state this and exclude the sentence from further analysis. Proceed only with the confirmed sentences.

+

+B. Synthesis of Function and Experimental Context

+

+From the sentences confirmed in Step A, synthesize the known biological functions of [Candidate Gene]. Do not create a single, flattened narrative. Instead, structure your summary to reflect the nuance of the findings:

+Characterize Each Function: For each reported function, describe what the gene does.

+Note the Experimental System: Specify the context for each finding. Was it observed in vivo (e.g., in a mouse model), in vitro (e.g., in a specific cell line like HEK293), or is it a finding from a computational prediction? (Cite PMID/ID).

+Distinguish Strength of Claims: Differentiate between established, speculative, or indirect roles. For example, note if the source text uses cautious language like "may regulate," "is associated with," or "is thought to be involved in." (Cite PMID/ID).

+Acknowledge Inconsistencies: If any sentences suggest conflicting or different roles for the gene, highlight these discrepancies rather than ignoring them.

+

+C. Critical Evaluation of Causal Gene Plausibility (with In-text Citations)

+

+Construct a detailed scientific evaluation of [Candidate Gene]'s plausibility for [Phenotype]. Your argument must be built cautiously, explicitly weighing the evidence for and against the gene's candidacy. Every claim you make must be immediately followed by its source (PMID/ID).

+

+Start with an Initial Caveat that acknowledges the inherent limitations of this analysis, such as the small number of excerpts and the lack of full experimental details.

+

+Then, structure your evaluation by critically assessing the following lines of evidence:

+

+Assessment of Functional Plausibility:

+

+How might the gene's known functions (from step B) mechanistically influence the biology of the phenotype? (Cite PMID/ID).

+Critically assess the strength of this link. Is the function a general cellular process (e.g., "cell proliferation"), which would be less compelling, or is it a specific process highly relevant to the phenotype (e.g., "dopaminergic synapse regulation")?

+Explicitly state if the connection is direct and supported by strong evidence, or if it is speculative and requires inferential leaps.

+

+Assessment of Tissue/Cell Type Relevance:

+

+Does the literature state that the gene is expressed in tissues or cell types known to be relevant to the phenotype? (Cite PMID/ID).

+Evaluate the specificity of this expression. Is it broadly expressed everywhere, or is its expression enriched in a key tissue (e.g., the nucleus accumbens for an addiction phenotype)? A lack of specificity weakens the argument.

+If no expression data is provided, state this as a critical knowledge gap.

+

+Assessment of Pathway and Network Involvement:

+

+Does the literature place the gene within specific biological pathways or interaction networks? (Cite PMID/ID).

+Are these pathways/networks already strongly implicated in the phenotype? Provide this context.

+Assess the gene's position in the pathway. A downstream effector might be a stronger candidate than a vaguely associated upstream component. If the nature of the involvement is unclear, state this.

+

+Assessment of Existing Disease/Trait Associations:

+

+Has the gene been previously associated with the target phenotype, or with mechanistically related traits or diseases in any organism? (Cite PMID/ID).

+Evaluate the nature of these prior associations. Are they from robust genetic studies (like other GWAS) or from smaller-scale molecular studies? An association from a large human GWAS is more compelling than a preliminary finding in a cell culture model.

+

+D. Balanced Concluding Assessment

+

+Conclude with a brief, balanced summary that encapsulates the strength of the evidence. This conclusion must reflect the cautious and critical nature of your analysis.

+Summarize Supporting Evidence: Briefly state the strongest, most direct lines of evidence that support [Candidate Gene] as a plausible candidate, citing the key PMIDs.

+Summarize Limitations and Gaps: Crucially, summarize the most significant weaknesses in the argument. This includes any identified knowledge gaps, lack of specificity, reliance on non-ideal experimental models, or speculative functional links.

+Final Judgment on Plausibility: Provide a final, nuanced statement on whether [Candidate Gene] is a weak, plausible, or strong candidate based only on the provided information. Avoid definitive conclusions and frame the outcome in terms of what further research would be needed to solidify the connection.

+

+3. Source Information:

diff --git a/server.py b/server.py
index 9d34bf9..19d7486 100755
--- a/server.py
+++ b/server.py
@@ -13,32 +13,44 @@ from os import listdir
 
 import bcrypt
 import nltk
-import numpy as np
+# import numpy as np # Removed
 import pandas as pd
 import pytz
 from flask import (Flask, Response, flash, jsonify, redirect, render_template,
                    request, session, url_for)
 from flask_sqlalchemy import SQLAlchemy
-from numpy import array
+# from numpy import array # Removed
 
-nltk.download('punkt')
-import pickle
+from dotenv import load_dotenv
+load_dotenv()
+import os
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+
+nltk.download('punkt', quiet=True)
+# import pickle # Removed
 from collections import Counter
 from datetime import datetime
 
-import tensorflow
-import tensorflow.keras
-from nltk.corpus import stopwords
-from nltk.stem.porter import PorterStemmer
-from tensorflow.keras import backend as K
-from tensorflow.keras import metrics, optimizers
-from tensorflow.keras.layers import *
-from tensorflow.keras.layers import Dense, Embedding, Flatten
-from tensorflow.keras.models import Model, Sequential
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from tensorflow.keras.preprocessing.text import Tokenizer
+# Gemini API related imports
+import google.generativeai as genai
 
+import re
+import ast
 from more_functions import *
+from nltk.tokenize import sent_tokenize 
+from more_functions import getabstracts, undic, gene_category 
+
+GENECUP_PROMPT_TEMPLATE = ""
+try:
+    with open("genecup_synthesis_prompt.txt", "r") as f:
+        GENECUP_PROMPT_TEMPLATE = f.read()
+except FileNotFoundError:
+    print("Warning: genecup_synthesis_prompt.txt not found.  LLM prompts will be incomplete.")
+except Exception as e:
+    print(f"Error loading genecup_synthesis_prompt.txt: {e}. LLM prompts will be affected.")
+
+
+
 
 app=Flask(__name__)
 #datadir="/export/ratspub/"
@@ -48,8 +60,125 @@ datadir="./"
 app.config['SECRET_KEY'] = '#DtfrL98G5t1dC*4'
 app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///'+datadir+'userspub.sqlite'
 db = SQLAlchemy(app)
+
+
+def get_sentences_from_file(file_path, gene_name, category_name=None):
+    """Reads a sentence file and returns sentences matching a gene and category."""
+    matching_sentences = []
+    try:
+        with open(file_path, "r") as f:
+            for line in f:
+                if not line.strip():
+                    continue
+                try:
+                    (gene, nouse, cat, pmid, text) = line.split("\t")
+                    cat_match = (category_name is None) or (cat.strip().upper() == category_name.strip().upper())
+                    if (gene.strip().upper() == gene_name.strip().upper() and cat_match):
+                        matching_sentences.append({'pmid': pmid, 'text': text, 'category': cat})
+                except ValueError:
+                    continue
+    except FileNotFoundError:
+        print(f"Sentence file not found: {file_path}")
+    except Exception as e:
+        print(f"Error reading sentence file {file_path}: {e}")
+    return matching_sentences
+
+
 nltk.data.path.append("./nlp/")
 
+# Initialize database within application context
+with app.app_context():
+    db.create_all()
+
+# Configure Gemini API Key
+# IMPORTANT: Set the GEMINI_API_KEY environment variable
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+if not GEMINI_API_KEY:
+    print("Warning: GEMINI_API_KEY environment variable not set. Stress classification via Gemini will not work.")
+else:
+    try:
+        genai.configure(api_key=GEMINI_API_KEY)
+    except Exception as e:
+        print(f"Error configuring Gemini API: {e}")
+        GEMINI_API_KEY = None # Ensure it's None if configuration fails
+'''
+STRESS_PROMPT_TEMPLATE = ""
+try:
+    with open("stress_prompt.txt", "r") as f_prompt:
+        STRESS_PROMPT_TEMPLATE = f_prompt.read()
+except FileNotFoundError:
+    print("FATAL ERROR: stress_prompt.txt not found. Stress classification will fail.")
+except Exception as e:
+    print(f"FATAL ERROR: Could not read stress_prompt.txt: {e}")
+
+# few shot Function to classify stress using Gemini API
+def classify_stress_with_gemini(sentence_text):
+    if not GEMINI_API_KEY:
+        print("Gemini API key not configured. Skipping classification.")
+        return "error_no_api_key"
+
+    # --- THIS IS THE MODIFIED PART ---
+    # Check if the prompt template was loaded successfully
+    if not STRESS_PROMPT_TEMPLATE:
+        print("Stress prompt template is not available. Skipping classification.")
+        return "error_no_prompt_template"
+
+    try:
+        model_gemini = genai.GenerativeModel('gemini-3-flash-preview')
+        
+        # Append the new sentence and the final instruction to the prompt template
+        # This is safer than .format() when the template contains its own curly braces.
+        prompt = STRESS_PROMPT_TEMPLATE + f'\nSentence: {sentence_text}\nClassification:'
+        print(prompt)
+        response = model_gemini.generate_content(prompt)
+        # We need to parse the classification from the response
+        classification = response.text.strip().lower()
+        
+        # The model might return "Cellular Level Stress" or "Organismal Stress"
+        if "cellular" in classification:
+            return "neg"  # 'neg' for Cellular Level Stress
+        elif "organismal" in classification:
+            return "pos"  # 'pos' for Organismal Stress
+        else:
+            print(f"Warning: Gemini returned unexpected classification: '{classification}' for sentence: '{sentence_text}'")
+            return "unknown"
+            
+    except Exception as e:
+        print(f"Error calling Gemini API for stress classification: {e}")
+        return "error_api_call"
+
+
+# zero-shot Function to classify stress using Gemini API
+def classify_stress_with_gemini(sentence_text):
+    if not GEMINI_API_KEY:
+        print("Gemini API key not configured. Skipping classification.")
+        return "error_no_api_key"
+
+    try:
+        model_gemini = genai.GenerativeModel('gemini-3-flash-preview')
+        prompt = f"""Classify the following sentence based on whether it describes 'systemic stress' or 'cellular stress'.
+Please return ONLY the word 'systemic' if it describes systemic stress, or ONLY the word 'cellular' if it describes cellular stress. Do not add any other explanation or punctuation.
+
+Sentence: "{sentence_text}"
+
+Classification:"""
+
+        response = model_gemini.generate_content(prompt)
+        classification = response.text.strip().lower()
+        
+        if classification == "systemic":
+            return "pos"  # 'pos' for systemic stress
+        elif classification == "cellular":
+            return "neg"  # 'neg' for cellular stress
+        else:
+            print(f"Warning: Gemini returned unexpected classification: '{classification}' for sentence: '{sentence_text}'")
+            return "unknown"
+            
+    except Exception as e:
+        print(f"Error calling Gemini API for stress classification: {e}")
+        return "error_api_call"
+'''
+
 # Sqlite database
 class users(db.Model):
     __tablename__='user'
@@ -59,46 +188,47 @@ class users(db.Model):
     password = db.Column(db.String(128), nullable=False)
     date_created = db.Column(db.DateTime, default=datetime.utcnow)
 
-# Preprocessing of words for CNN
-def clean_doc(doc, vocab):
-    doc = doc.lower()
-    tokens = doc.split()
-    re_punc = re.compile('[%s]' % re.escape(string.punctuation))    
-    tokens = [re_punc.sub('' , w) for w in tokens]    
-    tokens = [word for word in tokens if len(word) > 1]
-    stop_words = set(stopwords.words('english'))
-    tokens = [w for w in tokens if not w in stop_words]
-    porter = PorterStemmer()
-    stemmed = [porter.stem(word) for word in tokens]
-    return tokens
-
-# Load tokenizer
-with open('./nlp/tokenizer.pickle', 'rb') as handle:
-    tokenizer = pickle.load(handle)
-
-# Load vocabulary
-with open('./nlp/vocabulary.txt', 'r') as vocab:
-    vocab = vocab.read()
-
-def tf_auc_score(y_true, y_pred):
-    return tensorflow.metrics.auc(y_true, y_pred)[1]
-
-K.clear_session()
-
-# Create the CNN model
-def create_model(vocab_size, max_length):
-    model = Sequential()
-    model.add(Embedding(vocab_size, 32, input_length=max_length))
-    model.add(Conv1D(filters=16, kernel_size=4, activation='relu'))
-    model.add(MaxPooling1D(pool_size=2))
-    model.add(Flatten())
-    model.add(Dense(10, activation='relu'))
-    model.add(Dense(1, activation='sigmoid'))
-    opt = tensorflow.keras.optimizers.Adamax(lr=0.002, beta_1=0.9, beta_2=0.999)
-    model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[tf_auc_score])
-    return model
+# Preprocessing of words for CNN (REMOVED)
+# def clean_doc(doc, vocab):
+#     doc = doc.lower()
+#     tokens = doc.split()
+#     re_punc = re.compile('[%s]' % re.escape(string.punctuation))    
+#     tokens = [re_punc.sub('' , w) for w in tokens]    
+#     tokens = [word for word in tokens if len(word) > 1]
+#     stop_words = set(stopwords.words('english'))
+#     tokens = [w for w in tokens if not w in stop_words]
+#     porter = PorterStemmer()
+#     stemmed = [porter.stem(word) for word in tokens]
+#     return tokens
+
+# Load tokenizer (REMOVED)
+# with open('./nlp/tokenizer.pickle', 'rb') as handle:
+#     tokenizer = pickle.load(handle)
+
+# Load vocabulary (REMOVED)
+# with open('./nlp/vocabulary.txt', 'r') as vocab_file_handle: # Renamed variable to avoid conflict
+#     vocab_text = vocab_file_handle.read() # Renamed variable
+
+# def tf_auc_score(y_true, y_pred): (REMOVED)
+#     return tensorflow.metrics.AUC()(y_true, y_pred)
+
+# K.clear_session() (REMOVED)
+
+# Create the CNN model (REMOVED)
+# def create_model(vocab_size, max_length):
+#     model = Sequential()
+#     model.add(Embedding(vocab_size, 32, input_length=max_length))
+#     model.add(Conv1D(filters=16, kernel_size=4, activation='relu'))
+#     model.add(MaxPooling1D(pool_size=2))
+#     model.add(Flatten())
+#     model.add(Dense(10, activation='relu'))
+#     model.add(Dense(1, activation='sigmoid'))
+#     opt = tensorflow.keras.optimizers.Adamax(learning_rate=0.002, beta_1=0.9, beta_2=0.999)
+#     model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[tf_auc_score])
+#     return model
 
 # Use addiction ontology by default
+import ast # Moved import ast here as it's first used here.
 onto_cont=open("addiction.onto","r").read()
 dictionary=ast.literal_eval(onto_cont)
 
@@ -278,7 +408,7 @@ def logout():
             user1 = session['name']
         else: 
             user1 = session['email']
-    flash("You have been logged out, {user1}", "inval")
+    flash(f"You have been logged out, {user1}", "inval") # Used f-string for clarity
     session.pop('email', None)
     session.clear()
     return render_template('index.html',onto_len_dir=onto_len_dir, onto_list=onto_list, ontol = 'addiction', dict_onto = dict_onto)
@@ -699,10 +829,10 @@ def progress():
         except:
             namecat = 'addiction'
             session['namecat'] = namecat
-        if namecat=='choose your ontology' or namecat=='addiction' or namecat == 'addiction':
+        if namecat=='choose your ontology' or namecat=='addiction' or namecat == 'addiction': # Redundant 'addiction' check
             session['namecat']='addiction'
             onto_cont=open("addiction.onto","r").read()
-            dictionary=ast.literal_eval(onto_cont)
+            # dictionary=ast.literal_eval(onto_cont) # dictionary is global, no need to re-assign from local onto_cont
             search_type = request.args.getlist('type')
             if (search_type == []):
                 search_type = ['GWAS', 'function', 'addiction', 'drug', 'brain', 'stress', 'psychiatric', 'cell']
@@ -805,7 +935,7 @@ def search():
         d["nj{0}".format(n_num)]=''
     else:
         namecat_flag=0
-        for ky in dictionary.keys():
+        for ky in dictionary.keys(): # Using global 'dictionary'
             nodecolor[ky] = "hsl("+str((n_num+1)*int(360/len(dictionary.keys())))+", 70%, 80%)"
             d["nj{0}".format(n_num)]=generate_nodes_json(dictionary[ky],str(ky),nodecolor[ky])
             n_num+=1
@@ -818,10 +948,15 @@ def search():
                 json_nodes += generate_nodes_json(dictionary[ky],str(ky),nodecolor[ky])
         d["nj{0}".format(n_num)]=''
     
-    json_nodes = json_nodes[:-2]
-    json_nodes =json_nodes+"]}"
-    def generate(genes, tf_name):
+    json_nodes = json_nodes[:-2] # Handles case if json_nodes was only "{\"data\":["
+    if json_nodes == "{\"data\"": # if it was empty before -2
+        json_nodes = "{\"data\":[]}"
+    else:
+        json_nodes =json_nodes+"]}"
+
+    def generate(genes, tf_name): # tf_name is snt_file
         with app.test_request_context():
+            from nltk.tokenize import sent_tokenize # Moved import here, as it's only used in this function scope.
             sentences=str()
             edges=str()
             nodes = temp_nodes
@@ -832,34 +967,36 @@ def search():
             #genes_or = ' [tiab] or '.join(genes)
             all_d=''
 
+            current_dict_onto = {} # To hold the relevant ontology for this search pass
             if namecat_flag==1:
-                onto_cont = open(ses_namecat+".onto","r").read()
-                dict_onto=ast.literal_eval(onto_cont)
-
-                for ky in dict_onto.keys():
-                    if (ky in search_type):
-                        all_d_ls=undic(list(dict_onto[ky].values()))
-                        all_d = all_d+'|'+all_d_ls
+                onto_cont_local = open(ses_namecat+".onto","r").read() # ses_namecat from outer scope
+                current_dict_onto=ast.literal_eval(onto_cont_local)
             else:
-                for ky in dictionary.keys():
-                    if (ky in search_type):
-                        all_d_ls=undic(list(dictionary[ky].values()))
-                        all_d = all_d+'|'+all_d_ls
-            all_d=all_d[1:]
+                current_dict_onto = dictionary # Use global dictionary
+
+            for ky in current_dict_onto.keys():
+                if (ky in search_type):
+                    all_d_ls=undic(list(current_dict_onto[ky].values()))
+                    all_d = all_d+'|'+all_d_ls
+            if all_d: # Check if all_d is not empty
+                all_d=all_d[1:]
+
             if ("GWAS" in search_type):
                 datf = pd.read_csv('./utility/gwas_used.csv',sep='\t')
             progress+=percent
             yield "data:"+str(progress)+"\n\n"
+            
             for gene in genes:
-                abstracts_raw = getabstracts(gene,all_d)
+                abstracts_raw = getabstracts(gene,all_d) # all_d might be empty if no search_type matches
                 #print(abstracts_raw)
                 sentences_ls=[]
 
                 for row in abstracts_raw.split("\n"):
+                    if not row.strip(): continue # Skip empty lines
                     tiab=row.split("\t")
                     pmid = tiab.pop(0)
-                    tiab= " ".join(tiab)
-                    sentences_tok = sent_tokenize(tiab)
+                    tiab_text = " ".join(tiab) # Renamed to avoid conflict
+                    sentences_tok = sent_tokenize(tiab_text)
                     for sent_tok in sentences_tok:
                         sent_tok = pmid + ' ' + sent_tok
                         sentences_ls.append(sent_tok)
@@ -867,60 +1004,76 @@ def search():
                 
                 geneEdges = ""
 
-                if namecat_flag==1:
-                    onto_cont = open(ses_namecat+".onto","r").read()
-                    dict_onto=ast.literal_eval(onto_cont)
-                else:
-                    dict_onto = dictionary
+                # Use the already determined current_dict_onto
+                # if namecat_flag==1:
+                #     onto_cont = open(ses_namecat+".onto","r").read()
+                #     dict_onto_loop=ast.literal_eval(onto_cont)
+                # else:
+                #     dict_onto_loop = dictionary
+                dict_onto_loop = current_dict_onto
 
-                for ky in dict_onto.keys():
+                for ky in dict_onto_loop.keys():
                     if (ky in search_type):
-                        if (ky=='addiction') and ('addiction' in dict_onto.keys())\
-                            and ('drug' in dict_onto.keys()) and ('addiction' in dict_onto['addiction'].keys())\
-                            and ('aversion' in dict_onto['addiction'].keys()) and ('intoxication' in dict_onto['addiction'].keys()):
-                            #addiction terms must present with at least one drug
+                        # The special handling for 'addiction' with 'drug' needs careful check of dict_onto_loop structure
+                        if (ky=='addiction') and ('addiction' in dict_onto_loop.keys())\
+                            and ('drug' in dict_onto_loop.keys()) and ('addiction' in dict_onto_loop['addiction'].keys())\
+                            and ('aversion' in dict_onto_loop['addiction'].keys()) and ('intoxication' in dict_onto_loop['addiction'].keys()):
                             addiction_flag=1
-                            #addiction=undic0(addiction_d) +") AND ("+undic0(drug_d)
-                            sent=gene_category(gene, addiction_d, "addiction", sentences_ls,addiction_flag,dict_onto)
-                            if ('addiction' in search_type):
+                            # addiction_d is not defined here, assume it's a global or from more_functions
+                            # This part might need `addiction_d` from `more_functions.py` to be correctly defined.
+                            # For now, assuming addiction_d is available in the scope.
+                            sent=gene_category(gene, addiction_d, "addiction", sentences_ls,addiction_flag,dict_onto_loop)
+                            if ('addiction' in search_type): # This check is redundant with outer if
                                 geneEdges += generate_edges(sent, tf_name)
                                 json_edges += generate_edges_json(sent, tf_name)
                         else:
                             addiction_flag=0
-                            if namecat_flag==1:
-                                onto_cont = open(ses_namecat+".onto","r").read()
-                                dict_onto=ast.literal_eval(onto_cont)
-                                #ky_d=undic(list(dict_onto[ky].values()))    
-                                sent=gene_category(gene,ky,str(ky), sentences_ls, addiction_flag,dict_onto)
-                            else:
-                                #ky_d=undic(list(dict_onto[ky].values()))
-                                #print(sentences_ls)
-                                sent=gene_category(gene,ky,str(ky), sentences_ls, addiction_flag,dict_onto)
-                                #print(sent)
+                            sent=gene_category(gene,ky,str(ky), sentences_ls, addiction_flag,dict_onto_loop)
                             yield "data:"+str(progress)+"\n\n"
                             
                             geneEdges += generate_edges(sent, tf_name)
                             json_edges += generate_edges_json(sent, tf_name)                
                         sentences+=sent
-                if ("GWAS" in search_type):
+                if ("GWAS" in search_type and 'GWAS' in dict_onto_loop): # Added check for GWAS in dict_onto_loop
                     gwas_sent=[]
-                    print (datf)
-                    datf_sub1 = datf[datf["MAPPED_GENE"].str.contains('(?:\s|^)'+gene+'(?:\s|$)', flags=re.IGNORECASE)
-                                    | (datf["REPORTED GENE(S)"].str.contains('(?:\s|^)'+gene+'(?:\s|$)', flags=re.IGNORECASE))]
-                    print (datf_sub1)
-                    for nd2 in dict_onto['GWAS'].keys():
-                        for nd1 in dict_onto['GWAS'][nd2]:    
-                            for nd in nd1.split('|'):
-                                gwas_text=''
-                                datf_sub = datf_sub1[datf_sub1['DISEASE/TRAIT'].str.contains('(?:\s|^)'+nd+'(?:\s|$)', flags=re.IGNORECASE)]
-                                    #& (datf['REPORTED GENE(S)'].str.contains('(?:\s|^)'+gene+'(?:\s|$)', flags=re.IGNORECASE)
-                                    #| (datf['MAPPED_GENE'].str.contains('(?:\s|^)'+gene+'(?:\s|$)', flags=re.IGNORECASE)))]
-                                if not datf_sub.empty:
-                                    for index, row in datf_sub.iterrows():
-                                        gwas_text = "SNP:<b>"+str(row['SNPS'])+"</b>, P value: <b>"+str(row['P-VALUE'])\
-                                            +"</b>, Disease/trait:<b> "+str(row['DISEASE/TRAIT'])+"</b>, Mapped trait:<b> "\
-                                            +str(row['MAPPED_TRAIT'])+"</b><br>"
-                                        gwas_sent.append(gene+"\t"+"GWAS"+"\t"+nd+"_GWAS\t"+str(row['PUBMEDID'])+"\t"+gwas_text)
+                    # print (datf) # datf is loaded earlier
+                    datf_sub1 = datf[datf["MAPPED_GENE"].str.contains('(?:\s|^)'+gene+'(?:\s|$)', flags=re.IGNORECASE, na=False)
+                                    | (datf["REPORTED GENE(S)"].str.contains('(?:\s|^)'+gene+'(?:\s|$)', flags=re.IGNORECASE, na=False))]
+                    # print (datf_sub1)
+                    for nd2 in dict_onto_loop['GWAS'].keys():
+                        # Ensure dict_onto_loop['GWAS'][nd2] is iterable and contains strings
+                        # Example: if dict_onto_loop['GWAS'][nd2] is {'keyword1|keyword2'}
+                        # next(iter(dict_onto_loop['GWAS'][nd2])) might be what was intended
+                        # Assuming dict_onto_loop['GWAS'][nd2] is a set/list of keyword strings like {'kw1|kw2', 'kw3'}
+                        # The original code was: for nd1 in dict_onto_loop['GWAS'][nd2]: for nd in nd1.split('|'):
+                        # This implies dict_onto_loop['GWAS'][nd2] contains combined keywords.
+                        # Let's assume the structure is { 'subcategory' : {'keyword_group1', 'keyword_group2'} }
+                        # where keyword_group is "termA|termB"
+                        
+                        # Iterating over the values of the sub-dictionary if it's a dict, or elements if it's a list/set
+                        sub_keywords_container = dict_onto_loop['GWAS'][nd2]
+                        # This needs to be robust to the actual structure of dict_onto_loop['GWAS'][nd2]
+                        # Assuming it's a set of strings, where each string can be pipe-separated.
+                        # e.g., sub_keywords_container = {'phenotype1|phenotype_alias', 'phenotype2'}
+                        actual_keywords_to_iterate = []
+                        if isinstance(sub_keywords_container, dict): # e.g. {'phenotype_group': 'pheno1|pheno2'}
+                             for key_group_str in sub_keywords_container.values(): # Or .keys() if that's the intent
+                                actual_keywords_to_iterate.extend(key_group_str.split('|'))
+                        elif isinstance(sub_keywords_container, (list, set)):
+                            for key_group_str in sub_keywords_container:
+                                actual_keywords_to_iterate.extend(key_group_str.split('|'))
+                        elif isinstance(sub_keywords_container, str): # e.g. 'pheno1|pheno2'
+                            actual_keywords_to_iterate.extend(sub_keywords_container.split('|'))
+
+
+                        for nd in actual_keywords_to_iterate:  
+                            gwas_text=''
+                            # Added na=False to contains calls
+                            datf_sub = datf_sub1[datf_sub1['DISEASE/TRAIT'].str.contains('(?:\s|^)'+nd+'(?:\s|$)', flags=re.IGNORECASE, na=False)]
+                            if not datf_sub.empty:
+                                for index, row in datf_sub.iterrows():
+                                    gwas_text = f"SNP:{row['SNPS']}, P value: {row['P-VALUE']}, Disease/trait: {row['DISEASE/TRAIT']}, Mapped trait: {row['MAPPED_TRAIT']}"
+                                    gwas_sent.append(gene+"\t"+"GWAS"+"\t"+nd2+"_GWAS\t"+str(row['PUBMEDID'])+"\t"+gwas_text) # Changed nd to nd2 for target node
                     cys, gwas_json, sn_file = searchArchived('GWAS', gene , 'json',gwas_sent, path_user)
                     with open(path_user+"gwas_results.tab", "a") as gwas_edges:
                         gwas_edges.write(sn_file)
@@ -931,8 +1084,17 @@ def search():
                 yield "data:"+str(progress)+"\n\n"
                                     
                 if len(geneEdges) >0:
+                    rnd = ''
+                    if 'email' in session:
+                        if 'rnd' in session:
+                            rnd = session['rnd']
+                        elif 'path_user' in session:
+                            rnd = session['path_user'].split('/')[-2]
+                    elif 'path' in session:
+                        rnd = session['path'].split('/')[-1]
+
                     edges+=geneEdges
-                    nodes+="{ data: { id: '" + gene +  "', nodecolor:'#E74C3C', fontweight:700, url:'/synonyms?node="+gene+"'} },\n"
+                    nodes+="{ data: { id: '" + gene +  "', nodecolor:'#E74C3C', fontweight:700, url:'/synonyms?node="+gene+"&rnd="+rnd+"'} },\n"
                 else:
                     nodesToHide+=gene +  " "
 
@@ -947,14 +1109,20 @@ def search():
                     zeroLinkNode.close()
                 yield "data:"+str(progress)+"\n\n"
 
-           # Edges in json format
-            json_edges="{\"data\":["+json_edges
-            json_edges = json_edges[:-2]
-            json_edges =json_edges+"]}"
+                        # Edges in json format
+            json_edges_content = json_edges.strip()
+            if json_edges_content.endswith(','):
+                json_edges_content = json_edges_content[:-1]
+
+            if not json_edges_content:
+                json_edges = "{\"data\":[]}"
+            else:
+                json_edges = "{\"data\":[" + json_edges_content + "]}"
 
             # Write edges to txt file in json format also in user folder
             with open(path_user+"edges.json", "w") as temp_file_edges:
-                temp_file_edges.write(json_edges) 
+                temp_file_edges.write(json_edges)
+
     with open(path_user+"nodes.json", "w") as temp_file_nodes:
         temp_file_nodes.write(json_nodes)
     return Response(generate(genes, snt_file), mimetype='text/event-stream')
@@ -983,15 +1151,26 @@ def tableview():
             return render_template('index.html', onto_len_dir=onto_len_dir, onto_list=onto_list, ontol = 'addiction', dict_onto = dict_onto)
 
         jedges =''
-        file_edges = open(datadir+gene_url_tmp +'/edges.json', 'r')
-        for line in file_edges.readlines():
-            if ':' not in line:
-                nodata_temp = 1
-            else: 
-                nodata_temp = 0
-                with open(datadir+gene_url_tmp +"/edges.json") as edgesjsonfile:
+        nodata_temp = 1 # Default to no data
+        try:
+            with open(datadir+gene_url_tmp +"/edges.json") as edgesjsonfile:
+                # Check if file is empty or just contains empty structure
+                content = edgesjsonfile.read().strip()
+                if content and content != "{\"data\":[]}":
+                    # Reset file pointer and load json
+                    edgesjsonfile.seek(0) 
                     jedges = json.load(edgesjsonfile)
-                break
+                    nodata_temp = 0 
+                else:
+                    jedges = {"data": []} # Ensure jedges is a dict
+        except FileNotFoundError:
+            jedges = {"data": []} # Ensure jedges is a dict if file not found
+        except json.JSONDecodeError:
+            print(f"Warning: Could not decode JSON from {datadir+gene_url_tmp}/edges.json")
+            jedges = {"data": []} # Ensure jedges is a dict
+            nodata_temp = 1
+
+
     else:
         genes_session_tmp=tf_path+"/"+rnd_url
         gene_url_tmp = genes_session_tmp
@@ -1005,16 +1184,25 @@ def tableview():
             onto_cont=open("addiction.onto","r").read()
             dict_onto=ast.literal_eval(onto_cont)
             return render_template('index.html', onto_len_dir=onto_len_dir, onto_list=onto_list, ontol = 'addiction', dict_onto = dict_onto)
+        
         jedges =''
-        file_edges = open(gene_url_tmp +'/edges.json', 'r')
-        for line in file_edges.readlines():
-            if ':' not in line:
-                nodata_temp = 1
-            else: 
-                nodata_temp = 0
-                with open(gene_url_tmp +"/edges.json") as edgesjsonfile:
+        nodata_temp = 1 # Default to no data
+        try:
+            with open(gene_url_tmp +'/edges.json') as edgesjsonfile:
+                content = edgesjsonfile.read().strip()
+                if content and content != "{\"data\":[]}":
+                    edgesjsonfile.seek(0)
                     jedges = json.load(edgesjsonfile)
-                break
+                    nodata_temp = 0
+                else:
+                    jedges = {"data": []}
+        except FileNotFoundError:
+             jedges = {"data": []}
+        except json.JSONDecodeError:
+            print(f"Warning: Could not decode JSON from {gene_url_tmp}/edges.json")
+            jedges = {"data": []}
+            nodata_temp = 1
+
     genename=genes_url.split("_")
     if len(genename)>3:
         genename = genename[0:3]
@@ -1040,7 +1228,7 @@ def tableview0():
 
     if ('email' in session):
         filename = rnd_url.split("_0_")[0]
-        genes_session_tmp = datadir+"/user/"+str(session['hashed_email'])+"/"+rnd_url+"/"+filename
+        # genes_session_tmp = datadir+"/user/"+str(session['hashed_email'])+"/"+rnd_url+"/"+filename # Not used further
         gene_url_tmp = "/user/"+str(session['hashed_email'])+"/"+rnd_url
         try:
             with open(datadir+gene_url_tmp+"/nodes.json") as jsonfile:
@@ -1054,18 +1242,26 @@ def tableview0():
             return render_template('index.html', onto_len_dir=onto_len_dir, onto_list=onto_list, ontol = 'addiction', dict_onto = dict_onto)
 
         jedges =''
-        file_edges = open(datadir+gene_url_tmp+'/edges.json', 'r')
-        for line in file_edges.readlines():
-            if ':' not in line:
-                nodata_temp = 1
-            else: 
-                nodata_temp = 0
-                with open(datadir+gene_url_tmp+"/edges.json") as edgesjsonfile:
+        nodata_temp = 1 # Default to no data
+        try:
+            with open(datadir+gene_url_tmp +'/edges.json') as edgesjsonfile:
+                content = edgesjsonfile.read().strip()
+                if content and content != "{\"data\":[]}":
+                    edgesjsonfile.seek(0)
                     jedges = json.load(edgesjsonfile)
-                break
+                    nodata_temp = 0
+                else:
+                    jedges = {"data": []}
+        except FileNotFoundError:
+             jedges = {"data": []}
+        except json.JSONDecodeError:
+            print(f"Warning: Could not decode JSON from {datadir+gene_url_tmp}/edges.json")
+            jedges = {"data": []}
+            nodata_temp = 1
+            
     else:
-        genes_session_tmp=tf_path+"/"+rnd_url
-        gene_url_tmp = genes_session_tmp
+        # genes_session_tmp=tf_path+"/"+rnd_url # Not used further
+        gene_url_tmp = tf_path+"/"+rnd_url
         try:
             with open(gene_url_tmp+"/nodes.json") as jsonfile:
                 jnodes = json.load(jsonfile)
@@ -1078,15 +1274,23 @@ def tableview0():
             return render_template('index.html', onto_len_dir=onto_len_dir, onto_list=onto_list, ontol = 'addiction', dict_onto = dict_onto)
 
         jedges =''
-        file_edges = open(gene_url_tmp+'/edges.json', 'r')
-        for line in file_edges.readlines():
-            if ':' not in line:
-                nodata_temp = 1
-            else: 
-                nodata_temp = 0
-                with open(gene_url_tmp+"/edges.json") as edgesjsonfile:
+        nodata_temp = 1 # Default to no data
+        try:
+            with open(gene_url_tmp +'/edges.json') as edgesjsonfile:
+                content = edgesjsonfile.read().strip()
+                if content and content != "{\"data\":[]}":
+                    edgesjsonfile.seek(0)
                     jedges = json.load(edgesjsonfile)
-                break
+                    nodata_temp = 0
+                else:
+                    jedges = {"data": []}
+        except FileNotFoundError:
+            jedges = {"data": []}
+        except json.JSONDecodeError:
+            print(f"Warning: Could not decode JSON from {gene_url_tmp}/edges.json")
+            jedges = {"data": []}
+            nodata_temp = 1
+
     genes_url=request.args.get('genequery')
     genename=genes_url.split("_")
     if len(genename)>3:
@@ -1118,7 +1322,7 @@ def userarchive():
         else:
             session['user_folder'] = datadir+"/user/"+str(session['hashed_email'])
     else:
-        onto_name_archive=''
+        # onto_name_archive='' # This variable is not used here
         flash("You logged out!")
         onto_len_dir = 0
         onto_list = ''
@@ -1135,26 +1339,34 @@ def userarchive():
     folder_list = []
     directory_list = []
     gene_list=[]
-    onto_list=[]
+    onto_list_archive =[] # Renamed to avoid conflict with outer scope 'onto_list'
 
     for filename in dirlist:
-        if ('_0_'  in filename):
-            folder_list.append(filename)
-            gene_name = filename.split('_0_')[1]
-            onto_name = filename.split('_0_')[2]
-            if gene_name[-2:] == '_m':
-                gene_name = gene_name[:-2]
-                gene_name = gene_name + ", ..."
-            gene_name = gene_name.replace('_', ', ')
-            gene_list.append(gene_name)
-            onto_list.append(onto_name)
-            onto_name=""
-            gene_name=""
-            filename=filename[0:4]+"-"+filename[5:7]+"-"+filename[8:13]+":"+filename[14:16]+":"+filename[17:19]
-            directory_list.append(filename)
+        if ('_0_'  in filename): # Ensure it's a search result folder, not e.g. "ontology"
+            if os.path.isdir(os.path.join(session['user_folder'], filename)): # Check if it's a directory
+                folder_list.append(filename)
+                try:
+                    gene_name = filename.split('_0_')[1]
+                    onto_name = filename.split('_0_')[2]
+                    if gene_name.endswith('_m'): # Check using endswith for robustness
+                        gene_name = gene_name[:-2]
+                        gene_name = gene_name + ", ..."
+                    gene_name = gene_name.replace('_', ', ')
+                    gene_list.append(gene_name)
+                    onto_list_archive.append(onto_name) # Use renamed list
+                    # onto_name="" # Not necessary, re-assigned in loop
+                    # gene_name="" # Not necessary, re-assigned in loop
+                    # Format filename for display
+                    display_filename=filename.split('_0_')[0] # Get only the timestamp part for display formatting
+                    display_filename=display_filename[0:4]+"-"+display_filename[5:7]+"-"+display_filename[8:10]+" "+display_filename[11:13]+":"+display_filename[14:16]+":"+display_filename[17:19]
+                    directory_list.append(display_filename)
+                except IndexError:
+                    print(f"Skipping folder with unexpected name format: {filename}")
+                    continue
+
     len_dir = len(directory_list)
     message3="<ul><li> Click on the Date/Time to view archived results. <li>The Date/Time are based on US Central time zone.</ul> "
-    return render_template('userarchive.html', len_dir=len_dir, gene_list = gene_list, onto_list = onto_list, folder_list=folder_list, directory_list=directory_list, session_id=session_id, message3=message3)
+    return render_template('userarchive.html', len_dir=len_dir, gene_list = gene_list, onto_list = onto_list_archive, folder_list=folder_list, directory_list=directory_list, session_id=session_id, message3=message3)
 
 
 # Remove the search directory
@@ -1177,189 +1389,301 @@ def remove():
 def date():
     select_date = request.args.get('selected_date')
     # Open the cache folder for the user
-    tf_path=datadir+"/user"
+    tf_path=datadir+"/user" # tf_path is effectively datadir+"/user"
+    nodata_temp = 1 # Default to no data
+    jedges = {"data": []} # Default empty jedges
+    jnodes = {"data": []} # Default empty jnodes
+    gene_list_all = []
+    gene_name = "N/A"
+    num_gene = 0
+
     if ('email' in session):
-        time_extension = str(select_date)
-        time_extension = time_extension.split('_0_')[0]
-        gene_name1 = str(select_date).split('_0_')[1]
-        time_extension = time_extension.replace(':', '_')
-        time_extension = time_extension.replace('-', '_')
-        session['user_folder'] = tf_path+"/"+str(session['hashed_email'])
-        genes_session_tmp = tf_path+"/"+str(session['hashed_email'])+"/"+select_date+"/"+time_extension
-        with open(tf_path+"/"+str(session['hashed_email'])+"/"+select_date+"/nodes.json", "r") as jsonfile:
-            jnodes = json.load(jsonfile)
-        jedges =''
-        file_edges = open(tf_path+"/"+str(session['hashed_email'])+"/"+select_date+"/edges.json", "r")
-        for line in file_edges.readlines():
-            if ':' not in line:
-                nodata_temp = 1
-            else:
-                nodata_temp = 0
-                with open(tf_path+"/"+str(session['hashed_email'])+"/"+select_date+"/edges.json", "r") as edgesjsonfile:
+        time_extension = str(select_date).split('_0_')[0]
+        # gene_name1 = str(select_date).split('_0_')[1] # Not used directly for fetching, gene list derived from edges
+        # time_extension = time_extension.replace(':', '_') # This was for folder creation, not reading
+        # time_extension = time_extension.replace('-', '_')
+        session['user_folder'] = tf_path+"/"+str(session['hashed_email']) # This seems redundant here
+        genes_session_tmp = tf_path+"/"+str(session['hashed_email'])+"/"+select_date+"/"+time_extension # This path is for the _snt, _cy files etc.
+
+        try:
+            with open(tf_path+"/"+str(session['hashed_email'])+"/"+select_date+"/nodes.json", "r") as jsonfile:
+                jnodes = json.load(jsonfile)
+        except (FileNotFoundError, json.JSONDecodeError) as e:
+            print(f"Error loading nodes.json: {e}")
+            # Keep default jnodes
+
+        try:
+            with open(tf_path+"/"+str(session['hashed_email'])+"/"+select_date+"/edges.json", "r") as edgesjsonfile:
+                content = edgesjsonfile.read().strip()
+                if content and content != "{\"data\":[]}":
+                    edgesjsonfile.seek(0)
                     jedges = json.load(edgesjsonfile)
-                break
-        gene_list_all=[]
-        gene_list=[]
-        if nodata_temp == 0:
+                    nodata_temp = 0
+        except (FileNotFoundError, json.JSONDecodeError) as e:
+            print(f"Error loading edges.json: {e}")
+            # Keep default jedges and nodata_temp = 1
+
+        if nodata_temp == 0 and jedges.get("data"):
+            current_gene_list = []
             for p in jedges['data']:
-                if p['source'] not in gene_list:
+                if p['source'] not in current_gene_list:
                     gene_list_all.append(p['source'])
-                    gene_list.append(p['source'])
-            if len(gene_list)>3:
-                gene_list = gene_list[0:3]
+                    current_gene_list.append(p['source'])
+            
+            display_gene_list = current_gene_list
+            added = ""
+            if len(current_gene_list)>3:
+                display_gene_list = current_gene_list[0:3]
                 added = ",..."
-            else:
-                added = ""
-            gene_name = str(gene_list)[1:]
-            gene_name=gene_name[:-1]
-            gene_name=gene_name.replace("'","")
-            gene_name = gene_name+added
-            num_gene = gene_name.count(',')+1
-        else:
-            gene_name1 = gene_name1.replace("_", ", ")
-            gene_name = gene_name1
-            num_gene = gene_name1.count(',')+1
-            for i in range(0,num_gene):
-                gene_list.append(gene_name1.split(',')[i])
-        genes_session = ''
-        for gen in gene_list_all:
-            genes_session += str(gen) + "_"
-        genes_session = genes_session[:-1]
+            
+            gene_name_str = str(display_gene_list)[1:-1] # Remove brackets
+            gene_name_str=gene_name_str.replace("'","")
+            gene_name = gene_name_str + added
+            num_gene = len(current_gene_list) # Count of unique source genes
+        else: # No data or error, try to get gene name from folder
+            try:
+                gene_name_from_folder = str(select_date).split('_0_')[1]
+                if gene_name_from_folder.endswith("_m"):
+                    gene_name_from_folder = gene_name_from_folder[:-2] + ", ..."
+                gene_name = gene_name_from_folder.replace("_", ", ")
+                num_gene = gene_name.count(',') + 1
+                gene_list_all = gene_name.split(', ') # Approximate
+            except IndexError:
+                gene_name = "N/A"
+                num_gene = 0
+        
+        genes_session_str = '' # Renamed to avoid conflict
+        for gen_item in gene_list_all: # Use gene_list_all derived from edges if possible
+            genes_session_str += str(gen_item).strip() + "_" # Ensure clean gene names
+        if genes_session_str:
+            genes_session_str = genes_session_str[:-1]
+
     else:
         flash("You logged out!")
         onto_len_dir = 0
-        onto_list = ''
+        onto_list_session = '' # Renamed to avoid conflict
         onto_cont=open("addiction.onto","r").read()
         dict_onto=ast.literal_eval(onto_cont)
-        return render_template('index.html', onto_len_dir=onto_len_dir, onto_list=onto_list, ontol = 'addiction', dict_onto = dict_onto)
-    message3="<ul><li> <font color=\"#E74C3C\">Click on the abstract count to read sentences linking the keyword and the gene</font> <li> Click on a keyword to see the terms included in the search. <li>View the results in <a href='\\cytoscape/?rnd={}&genequery={}'\ ><b> a graph.</b></a> </ul> Links will be preserved when the table is copy-n-pasted into a spreadsheet.".format(select_date,genes_session)
-    return render_template('tableview.html',nodata_temp=nodata_temp, num_gene=num_gene,genes_session_tmp = genes_session_tmp, rnd_url=select_date ,jedges=jedges, jnodes=jnodes,gene_name=gene_name, genes_url=genes_session, message3=message3)
+        return render_template('index.html', onto_len_dir=onto_len_dir, onto_list=onto_list_session, ontol = 'addiction', dict_onto = dict_onto)
+    
+    message3="<ul><li> <font color=\"#E74C3C\">Click on the abstract count to read sentences linking the keyword and the gene</font> <li> Click on a keyword to see the terms included in the search. <li>View the results in <a href='\\cytoscape/?rnd={}&genequery={}'\ ><b> a graph.</b></a> </ul> Links will be preserved when the table is copy-n-pasted into a spreadsheet.".format(select_date,genes_session_str)
+    return render_template('tableview.html',nodata_temp=nodata_temp, num_gene=num_gene,genes_session_tmp = genes_session_tmp, rnd_url=select_date ,jedges=jedges, jnodes=jnodes,gene_name=gene_name, genes_url=genes_session_str, message3=message3)
 
 @app.route('/cytoscape/')
 def cytoscape():
     genes_url=request.args.get('genequery')
     rnd_url=request.args.get('rnd')
     tf_path=tempfile.gettempdir()
-    genes_session_tmp=tf_path + "/" + genes_url
-    rnd_url_tmp=tf_path +"/" + rnd_url
+    # genes_session_tmp=tf_path + "/" + genes_url # This variable is not used
+    # rnd_url_tmp=tf_path +"/" + rnd_url # This is for non-logged in users path later
     message2="<ul><li><font color=\"#E74C3C\">Click on a line to read the sentences </font> <li>Click on a keyword to see the terms included in the search<li>Hover a pointer over a node to hide other links <li>Move the nodes around to adjust visibility <li> Reload the page to restore the default layout<li>View the results in <a href='\\tableview/?rnd={}&genequery={}'\ ><b>a table. </b></a></ul>".format(rnd_url,genes_url)
     
+    elements = "" # Default empty elements
+    zeroLink = "" # Default empty zeroLink
+
     if ('email' in session):
-        filename = rnd_url.split("_0_")[0]
-        rnd_url_tmp = datadir+"/user/"+str(session['hashed_email'])+"/"+rnd_url+"/"+filename
+        filename_part = rnd_url.split("_0_")[0] # Corrected variable name
+        rnd_url_path = datadir+"/user/"+str(session['hashed_email'])+"/"+rnd_url+"/"+filename_part # Corrected variable name
         try:
-            with open(rnd_url_tmp+"_cy","r") as f:
+            with open(rnd_url_path+"_cy","r") as f:
                 elements=f.read()
         except FileNotFoundError:
-            flash("You logged out!")
+            flash("You logged out or the search data is missing!") # More specific message
             onto_len_dir = 0
-            onto_list = ''
+            onto_list_session = '' # Renamed
             onto_cont=open("addiction.onto","r").read()
             dict_onto=ast.literal_eval(onto_cont)
-            return render_template('index.html', onto_len_dir=onto_len_dir, onto_list=onto_list, ontol = 'addiction', dict_onto = dict_onto)
+            return render_template('index.html', onto_len_dir=onto_len_dir, onto_list=onto_list_session, ontol = 'addiction', dict_onto = dict_onto)
 
-        with open(rnd_url_tmp+"_0link","r") as z:
-            zeroLink=z.read()
-            if (len(zeroLink)>0):
-                message2+="<span style=\"color:darkred;\">No result was found for these genes: " + zeroLink + "</span>"
-    else:
-        rnd_url_tmp=tf_path +"/" + rnd_url
         try:
-            rnd_url_tmp.replace("\"", "")
-            with open(rnd_url_tmp+"_cy","r") as f:
+            with open(rnd_url_path+"_0link","r") as z:
+                zeroLink=z.read()
+        except FileNotFoundError:
+            zeroLink = "" # File might not exist if no zero link genes
+
+    else: # Not logged in, use temp path
+        rnd_url_path=tf_path +"/" + rnd_url
+        try:
+            # rnd_url_path.replace("\"", "") # This doesn't modify in place and is likely not needed
+            with open(rnd_url_path+"_cy","r") as f:
                 elements=f.read()
         except FileNotFoundError:
-            flash("You logged out!")
+            flash("You logged out or the search data is missing!")
             onto_len_dir = 0
-            onto_list = ''
+            onto_list_session = '' # Renamed
             onto_cont=open("addiction.onto","r").read()
             dict_onto=ast.literal_eval(onto_cont)
-            return render_template('index.html', onto_len_dir=onto_len_dir, onto_list=onto_list, ontol = 'addiction', dict_onto = dict_onto)
+            return render_template('index.html', onto_len_dir=onto_len_dir, onto_list=onto_list_session, ontol = 'addiction', dict_onto = dict_onto)
+        
+        try:
+            with open(rnd_url_path+"_0link","r") as z:
+                zeroLink=z.read()
+        except FileNotFoundError:
+            zeroLink = ""
 
-        with open(rnd_url_tmp+"_0link","r") as z:
-            zeroLink=z.read()
-            if (len(zeroLink)>0):
-                message2+="<span style=\"color:darkred;\">No result was found for these genes: " + zeroLink + "</span>"
+    if (len(zeroLink.strip())>0): # Check if zeroLink has content after stripping whitespace
+        message2+="<span style=\"color:darkred;\">No result was found for these genes: " + zeroLink + "</span>"
+            
     return render_template('cytoscape.html', elements=elements, message2=message2)
 
 
 @app.route("/sentences")
 def sentences():
-    def predict_sent(sent_for_pred):
-        max_length = 64
-        tokens = clean_doc(sent_for_pred, vocab)
-        tokens = [w for w in tokens if w in vocab]
-        # convert to line
-        line = ' '.join(tokens)
-        line = [line]
-        tokenized_sent = tokenizer.texts_to_sequences(line)
-        tokenized_sent = pad_sequences(tokenized_sent, maxlen=max_length, padding='post') 
-        predict_sent = model.predict(tokenized_sent, verbose=0)
-        percent_sent = predict_sent[0,0]
-        if round(percent_sent) == 0:
-            return 'neg'
-        else:
-            return 'pos'
+    # Removed predict_sent and CNN model loading
+    # def predict_sent(sent_for_pred): ...
+    
     pmid_list=[]
     pmid_string=''
     edge=request.args.get('edgeID')
     (tf_name, gene0, cat0)=edge.split("|")
 
-    if(cat0=='stress'):
-        model = create_model(23154, 64)
-        model.load_weights("./nlp/weights.ckpt")
     out3=""
     out_pos = ""
     out_neg = ""
     num_abstract = 0
-    stress_cellular = "<br><br><br>"+"</ol><b>Sentence(s) describing celluar stress (classified using a deep learning model):</b><hr><ol>"
-    stress_systemic = "<b></ol>Sentence(s) describing systemic stress (classified using a deep learning model):</b><hr><ol>"
-    with open(tf_name, "r") as df:
-        all_sents=df.read()
-
-    for sent in all_sents.split("\n"):
-        if len(sent.strip())!=0:
-            (gene,nouse,cat, pmid, text)=sent.split("\t")
-            if (gene.upper() == gene0.upper() and cat.upper() == cat0.upper()) :
-                out3+= "<li> "+ text + " <a href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=" + pmid +"\" target=_new>PMID:"+pmid+"<br></a>"
-                num_abstract += 1
-                if(pmid+cat0 not in pmid_list):
-                    pmid_string = pmid_string + ' ' + pmid
-                    pmid_list.append(pmid+cat0)
-                if(cat0=='stress'):
-                    out4 = predict_sent(text)
-                    if(out4 == 'pos'):
-                        out_pred_pos = "<li> "+ text + " <a href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=" + pmid +"\" target=_new>PMID:"+pmid+"<br></a>"                    
-                        out_pos += out_pred_pos
-                    else:
-                        out_pred_neg = "<li>"+ text + " <a href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=" + pmid +"\" target=_new>PMID:"+pmid+"<br></a>"                    
-                        out_neg += out_pred_neg
+    stress_cellular = "<br><br><br>"+"</ol><b>Sentence(s) describing cellular stress (classified using Gemini API):</b><hr><ol>"
+    stress_systemic = "<b></ol>Sentence(s) describing systemic stress (classified using Gemini API):</b><hr><ol>"
+    
+    matching_sents = get_sentences_from_file(tf_name, gene0, cat0)
+    if not matching_sents:
+        # It's possible the file was found but no sentences matched the criteria.
+        return render_template('sentences.html', sentences=f"<p>No sentences found for {gene0} and {cat0}.</p>")
+
+    all_stress_sentences = []
+    num_abstract = len(matching_sents)
+
+    for sent_obj in matching_sents:
+        text = sent_obj['text']
+        pmid = sent_obj['pmid']
+        
+        formatted_line = f"<li> {text} <a href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term={pmid}\" target=_new>PMID:{pmid}<br></a>"
+        all_stress_sentences.append({'raw_text': text, 'html_line': formatted_line})
+        
+        out3 += formatted_line
+        if(pmid+cat0 not in pmid_list):
+            pmid_string = pmid_string + ' ' + pmid
+            pmid_list.append(pmid+cat0)
+
+    # Step 2: If the category is 'stress' and we have sentences, perform batch classification
+    if cat0 == 'stress' and all_stress_sentences:
+        if not GEMINI_API_KEY:
+            print("Gemini API key not configured. Skipping batch classification.")
+        else:
+            try:
+                # Create the batched prompt
+                sentences_to_classify_str = ""
+                for i, s_obj in enumerate(all_stress_sentences):
+                    # Use a unique, parsable identifier for each sentence
+                    sentences_to_classify_str += f'Sentence {i}: "{s_obj["raw_text"]}"\n'
+
+                batched_prompt = f"""For each sentence below, classify it as describing "Cellular Stress" or "Organismal Stress".
+Return your response as a valid JSON object where keys are the sentence numbers (e.g., "0", "1", "2") and values are the classification ("Cellular Stress" or "Organismal Stress").
+
+Example format: {{"0": "Cellular Stress", "1": "Organismal Stress"}}
+
+Here are the sentences to classify:
+{sentences_to_classify_str}
+"""
+                # Call the API
+                model_gemini = genai.GenerativeModel('gemini-3-flash-preview')
+                response = model_gemini.generate_content(batched_prompt)
+
+                # Step 3: Parse the JSON response
+                # The model might wrap the JSON in ```json ... ```, so we need to clean it.
+                cleaned_response_text = response.text.strip().replace("```json", "").replace("```", "").strip()
+                classifications = json.loads(cleaned_response_text)
+                
+                # Step 4: Distribute the sentences into buckets based on the parsed classifications
+                for i, s_obj in enumerate(all_stress_sentences):
+                    # Get the classification for sentence 'i'. Use .get() for safety.
+                    classification = classifications.get(str(i), "unknown").lower()
+                    if "cellular" in classification:
+                        out_neg += s_obj['html_line']
+                    elif "organismal" in classification:
+                        out_pos += s_obj['html_line']
+
+            except Exception as e:
+                print(f"Error during batch Gemini classification: {e}")
     out1="<h3>"+gene0 + " and " + cat0  + "</h3>\n"
     if len(pmid_list)>1:
-        out2 = str(num_abstract) + ' sentences in ' + " <a href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=" + pmid_string +"\" target=_new>"+ str(len(pmid_list)) + ' studies' +"<br></a>" + "<br><br>"
-    else:
-        out2 = str(num_abstract) + ' sentence(s) in '+ " <a href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=" + pmid_string +"\" target=_new>"+ str(len(pmid_list)) + ' study' +"<br></a>" "<br><br>"
-    if(out_neg == "" and out_pos == ""):
-        out= out1+ out2 +out3
-    elif(out_pos != "" and out_neg!=""):
-        out = out1 + out2 + stress_systemic+out_pos + stress_cellular + out_neg
-    elif(out_pos != "" and out_neg ==""):
-        out= out1+ out2 + stress_systemic + out_pos
-    elif(out_neg != "" and out_pos == ""):
-        out = out1 +out2+stress_cellular+out_neg
-    K.clear_session()
-    return render_template('sentences.html', sentences="<ol>"+out+"</ol><p>")
+        out2 = str(num_abstract) + ' sentences in ' + " <a href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=" + pmid_string.strip() +"\" target=_new>"+ str(len(pmid_list)) + ' studies' +"<br></a>" + "<br><br>"
+    elif len(pmid_list) == 1: # Handle single study case
+        out2 = str(num_abstract) + ' sentence(s) in '+ " <a href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=" + pmid_string.strip() +"\" target=_new>"+ str(len(pmid_list)) + ' study' +"<br></a>" "<br><br>"
+    else: # No PMIDs found, num_abstract might still be > 0 if PMIDs were not parsable in file but text matched
+        out2 = str(num_abstract) + ' sentence(s) found.<br><br>'
+
+
+    if(cat0 == 'stress'): # Only show stress classification if category is stress
+        if(out_neg == "" and out_pos == ""):
+            # If no classification results, show all sentences if any, or a message
+            if out3:
+                 out= out1+ out2 + "<b>All related sentences (Gemini classification not available or no specific stress types found):</b><hr><ol>" + out3
+            else:
+                 out = out1 + out2 + "No sentences found for this combination, or Gemini classification yielded no results."
+        elif(out_pos != "" and out_neg!=""):
+            out = out1 + out2 + stress_systemic+out_pos + stress_cellular + out_neg
+        elif(out_pos != "" and out_neg ==""):
+            out= out1+ out2 + stress_systemic + out_pos
+        elif(out_neg != "" and out_pos == ""):
+            out = out1 +out2+stress_cellular+out_neg
+    else: # Not stress category, just show all found sentences
+        out= out1+ out2 + "<ol>" + out3
+
+    # K.clear_session() # Removed
+    return render_template('sentences.html', sentences=out+"</ol><p>")
 
 
 # Show the cytoscape graph for one gene from the top gene list
 @app.route("/showTopGene")
 def showTopGene():
     query=request.args.get('topGene')
-    nodesEdges=searchArchived('topGene',query, 'cys','','')[0]
+    # Assuming searchArchived returns a tuple, and the first element is nodesEdges
+    archived_data = searchArchived('topGene',query, 'cys','','')
+    if isinstance(archived_data, tuple) and len(archived_data) > 0:
+        nodesEdges = archived_data[0]
+    else: # Fallback if searchArchived doesn't return expected tuple
+        nodesEdges = "" 
+        print(f"Warning: searchArchived did not return expected data for {query}")
+
     message2="<li><strong>"+query + "</strong> is one of the top addiction genes. <li> An archived search is shown. Click on the blue circle to update the results and include keywords for brain region and gene function. <strong> The update may take a long time to finish.</strong> "
     return render_template("cytoscape.html", elements=nodesEdges, message="Top addiction genes", message2=message2)
 
+'''
+@app.route("/shownode")
+def shownode():
+    node=request.args.get('node')
+    out = "" # Default value
+    current_dict_onto = {}
+
+    if 'namecat' in session:
+        try:
+            with open(session['namecat']+".onto","r") as file2:
+                onto_cont_local=file2.read()
+                current_dict_onto=ast.literal_eval(onto_cont_local)
+        except FileNotFoundError:
+            print(f"Ontology file not found: {session['namecat']}.onto. Falling back to default.")
+            current_dict_onto = dictionary # Fallback to default if custom not found
+        except Exception as e:
+            print(f"Error loading custom ontology {session['namecat']}.onto: {e}. Falling back to default.")
+            current_dict_onto = dictionary
+    else:
+        current_dict_onto = dictionary # Default global dictionary
+
+    for ky in current_dict_onto.keys():
+        if node in current_dict_onto[ky].keys():
+            # Ensure current_dict_onto[ky][node] is a dict and has at least one item
+            node_details = current_dict_onto[ky][node]
+            if isinstance(node_details, dict) and node_details:
+                 out="<p>"+node.upper()+"<hr><li>"+ next(iter(node_details)).replace("|", "<li>")
+                 break # Found the node, no need to check other keys
+            elif isinstance(node_details, str): # If it's just a string of keywords
+                 out="<p>"+node.upper()+"<hr><li>"+ node_details.replace("|", "<li>")
+                 break
+    if not out: # If node not found or details are empty
+        out = f"<p>Details for node '{node.upper()}' not found in the current ontology.</p>"
 
+    return render_template('sentences.html', sentences=out+"<p>")
+'''
 @app.route("/shownode")
 def shownode():
     node=request.args.get('node')
@@ -1377,30 +1701,173 @@ def shownode():
     return render_template('sentences.html', sentences=out+"<p>")
 
 
+
 @app.route("/synonyms")
 def synonyms():
-    node=request.args.get('node')
-    node=node.upper()
-    allnodes={**genes}
+    node = request.args.get('node')
+    rnd = request.args.get('rnd')
+
+    if not node:
+        return "Error: Gene node is required.", 400
+    node = node.upper()
+
     try:
-        synonym_list = list(allnodes[node].split("|")) 
+        # --- Part 1: Handle Synonyms Links ---
+        allnodes = {}
+        if 'genes' in globals() and isinstance(globals()['genes'], dict):
+            allnodes = globals()['genes']
+        else:
+            print("Warning: 'genes' dictionary for synonyms not found.")
+        
+        synonym_list = list(allnodes[node].split("|"))
         session['synonym_list'] = synonym_list
         session['main_gene'] = node.upper()
-        out="<hr><li>"+ allnodes[node].replace("|", "<li>")
-        synonym_list_str = ';'.join([str(syn) for syn in synonym_list]) 
-        synonym_list_str +=';' + node
+        synonym_list_str = ';'.join([str(syn) for syn in synonym_list])
+        synonym_list_str += ';' + node
         case = 1
-        return render_template('genenames.html', case = case, gene = node.upper(), synonym_list = synonym_list, synonym_list_str=synonym_list_str)
-    except:
-        try:
-            synonym_list = session['synonym_list']
-            synonym_list_str = ';'.join([str(syn) for syn in synonym_list]) 
-            synonym_list_str +=';' + node
-            case = 1
-            return render_template('genenames.html', case=case, gene = session['main_gene'] , synonym_list = synonym_list, synonym_list_str=synonym_list_str)
-        except:
-            case = 2
-            return render_template('genenames.html', gene = node, case = case)
+
+        formatted_sentences = ""
+
+        if rnd and rnd.strip():
+            # --- Logic to use existing search results ---
+            print(f"Synonyms: rnd '{rnd}' provided. Reading from search results.")
+            path = ''
+            if 'email' in session and 'hashed_email' in session:
+                path = datadir+"/user/"+str(session['hashed_email'])+"/"+rnd+"/"
+            else:
+                tf_path = tempfile.gettempdir()
+                path = tf_path + "/" + rnd + "/"
+
+            timestamp = rnd.split("_0_")[0]
+            snt_file_path = path + timestamp + "_snt"
+            gwas_file_path = path + "gwas_results.tab"
+
+            sents_by_main_cat = {}
+            
+            try:
+                with open(snt_file_path, "r") as f:
+                    for line in f:
+                        if not line.strip(): continue
+                        try:
+                            (l_gene, l_main_cat, l_sub_cat, l_pmid, l_text) = line.strip().split("\t")
+                            if l_gene.upper() == node:
+                                if l_main_cat not in sents_by_main_cat: sents_by_main_cat[l_main_cat] = {}
+                                if l_sub_cat not in sents_by_main_cat[l_main_cat]: sents_by_main_cat[l_main_cat][l_sub_cat] = []
+                                sents_by_main_cat[l_main_cat][l_sub_cat].append({'pmid': l_pmid, 'text': l_text})
+                        except ValueError: continue
+            except FileNotFoundError: print(f"Sentence file not found: {snt_file_path}")
+
+            try:
+                with open(gwas_file_path, "r") as f:
+                    for line in f:
+                        if not line.strip(): continue
+                        try:
+                            (l_gene, l_main_cat, l_sub_cat, l_pmid, l_text) = line.strip().split("\t")
+                            if l_gene.upper() == node:
+                                if 'GWAS' not in sents_by_main_cat: sents_by_main_cat['GWAS'] = {}
+                                sub_cat_clean = l_sub_cat.replace('_GWAS', '')
+                                if sub_cat_clean not in sents_by_main_cat['GWAS']: sents_by_main_cat['GWAS'][sub_cat_clean] = []
+                                sents_by_main_cat['GWAS'][sub_cat_clean].append({'pmid': l_pmid, 'text': l_text})
+                        except ValueError: continue
+            except FileNotFoundError: print(f"GWAS sentence file not found: {gwas_file_path}")
+
+            for main_cat, sub_cats in sorted(sents_by_main_cat.items()):
+                for sub_cat, sentences in sorted(sub_cats.items()):
+                    formatted_sentences += f"\n## Keyword: {sub_cat} (Category: {main_cat})\n"
+                    for sent_obj in sentences:
+                        clean_text = re.sub('<[^<]+?>', '', sent_obj['text'])
+                        formatted_sentences += f"- {clean_text} (PMID: {sent_obj['pmid']})\n"
+        else:
+            # --- Fallback Logic: Perform a fresh search ---
+            print(f"Synonyms: rnd not provided. Performing fresh search for {node}.")
+            current_ontology = {}
+            if 'namecat' in session and session['namecat'] != 'addiction' and not session['namecat'].startswith(tempfile.gettempdir()):
+                try:
+                    with open(session['namecat'] + ".onto", "r") as f_onto: current_ontology = ast.literal_eval(f_onto.read())
+                except (FileNotFoundError, SyntaxError, TypeError): current_ontology = dictionary
+            else: current_ontology = dictionary
+
+            abstracts_raw = getabstracts(node, "")
+            sentences_ls = []
+            if abstracts_raw:
+                for row in abstracts_raw.split("\n"):
+                    if not row.strip(): continue
+                    parts = row.split("\t", 1)
+                    if len(parts) < 2: continue
+                    pmid, tiab_text = parts
+                    for sent_tok in sent_tokenize(tiab_text): sentences_ls.append({'pmid': pmid, 'text': sent_tok})
+
+            pubmed_formatted_sentences = ""
+            if sentences_ls:
+                gene_regex = re.compile(r'\b(' + re.escape(node) + r')\b', re.IGNORECASE)
+                for category_key, keyword_nodes in sorted(current_ontology.items()):
+                    if not isinstance(keyword_nodes, dict): continue
+                    for keyword_node, search_terms_obj in sorted(keyword_nodes.items()):
+                        if isinstance(search_terms_obj, set) and search_terms_obj: search_terms_str = next(iter(search_terms_obj))
+                        elif isinstance(search_terms_obj, str): search_terms_str = search_terms_obj
+                        else: continue
+                        
+                        keyword_regex_str = r'\b(' + '|'.join(re.escape(term) for term in search_terms_str.split('|')) + r')\b'
+                        keyword_regex = re.compile(keyword_regex_str, re.IGNORECASE)
+                        
+                        sents_for_this_keyword = [s for s in sentences_ls if gene_regex.search(s['text']) and keyword_regex.search(s['text'])]
+                        
+                        if sents_for_this_keyword:
+                            pubmed_formatted_sentences += f"\n## Keyword: {keyword_node} (Category: {category_key})\n"
+                            for sent_obj in sents_for_this_keyword: pubmed_formatted_sentences += f"- {sent_obj['text']} (PMID: {sent_obj['pmid']})\n"
+            
+            gwas_formatted_sentences = ""
+            if 'GWAS' in current_ontology:
+                try:
+                    datf = pd.read_csv('./utility/gwas_used.csv', sep='\t')
+                    gene_pattern = r'(?:\s|^)' + re.escape(node) + r'(?:\s|$)'
+                    datf_sub1 = datf[datf["MAPPED_GENE"].str.contains(gene_pattern, flags=re.IGNORECASE, na=False) | datf["REPORTED GENE(S)"].str.contains(gene_pattern, flags=re.IGNORECASE, na=False)]
+                    if not datf_sub1.empty:
+                        gwas_sents_for_node = []
+                        gwas_ontology_part = current_ontology.get('GWAS', {})
+                        if isinstance(gwas_ontology_part, dict):
+                            for keyword_node, search_terms_obj in sorted(gwas_ontology_part.items()):
+                                if isinstance(search_terms_obj, set) and search_terms_obj: search_terms_str = next(iter(search_terms_obj))
+                                elif isinstance(search_terms_obj, str): search_terms_str = search_terms_obj
+                                else: continue
+                                for term in search_terms_str.split('|'):
+                                    if not term: continue
+                                    term_pattern = r'(?:\s|^)' + re.escape(term) + r'(?:\s|$)'
+                                    datf_sub = datf_sub1[datf_sub1['DISEASE/TRAIT'].str.contains(term_pattern, flags=re.IGNORECASE, na=False)]
+                                    if not datf_sub.empty:
+                                        for _, row in datf_sub.iterrows():
+                                            gwas_text = f"SNP:{row['SNPS']}, P value: {row['P-VALUE']}, Disease/trait: {row['DISEASE/TRAIT']}, Mapped trait: {row['MAPPED_TRAIT']}"
+                                            gwas_sents_for_node.append({'pmid': row['PUBMEDID'], 'text': gwas_text, 'category': keyword_node})
+                        if gwas_sents_for_node:
+                            gwas_by_keyword = {}
+                            for s in gwas_sents_for_node:
+                                kw = s['category']
+                                if kw not in gwas_by_keyword: gwas_by_keyword[kw] = []
+                                gwas_by_keyword[kw].append(s)
+                            for keyword, sentences in sorted(gwas_by_keyword.items()):
+                                gwas_formatted_sentences += f"\n\n## Keyword: {keyword} (Category: GWAS)\n"
+                                unique_sentences = {f"{s['pmid']}_{s['text']}": s for s in sentences}
+                                for sent_obj in unique_sentences.values(): gwas_formatted_sentences += f"- {sent_obj['text']} (PMID: {sent_obj['pmid']})\n"
+                except FileNotFoundError: print("Warning: ./utility/gwas_used.csv not found.")
+                except Exception as e: print(f"Error processing GWAS data in /synonyms fallback: {e}")
+
+            formatted_sentences = pubmed_formatted_sentences + gwas_formatted_sentences
+
+        # --- Part 4: Assemble final prompt ---
+        if not formatted_sentences.strip():
+            formatted_sentences = "No relevant sentences were found in the literature for this gene."
+
+        prompt_string = GENECUP_PROMPT_TEMPLATE.replace("{{gene}}", node)
+        prompt_string += formatted_sentences
+
+        return render_template('genenames.html', case=case, gene=node.upper(), synonym_list=synonym_list, synonym_list_str=synonym_list_str, prompt=prompt_string)
+
+    except KeyError:
+        case = 2
+        return render_template('genenames.html', gene=node, case=case)
+    except Exception as e:
+        print(f"An unexpected error occurred in /synonyms for node {node}: {e}")
+        return f"An error occurred while processing your request for {node}.", 500
 
 
 @app.route("/startGeneGene")
@@ -1411,86 +1878,157 @@ def startGeneGene():
 
 @app.route("/searchGeneGene")
 def gene_gene():
+    # Ensure session['path'] is set (e.g. from /progress by non-logged-in user)
+    if 'path' not in session:
+        # Handle error: session path not set, perhaps redirect or show error
+        # For now, let's assume it's set by a previous step like /progress
+        # If it can be called directly, this needs robust handling.
+        # Quick fix: if not set, create a temporary one, but this might indicate flow issue
+        if 'email' not in session : # Only create temp path if not logged in and path is missing
+             tf_path_gg=tempfile.gettempdir()
+             rnd_gg = "tmp_gg" + ''.join(random.choice(string.ascii_letters) for x in range(6))
+             session['path'] = tf_path_gg + "/" + rnd_gg
+             os.makedirs(session['path'], exist_ok=True)
+        else: # Logged in user should have path_user from /progress
+            if 'path_user' in session:
+                session['path'] = session['path_user'] # Unify to use session['path']
+            else: # Critical error if logged in and no path_user
+                 return "Error: User session path not found.", 500
+
+
     tmp_ggPMID=session['path']+"_ggPMID"
     gg_file=session['path']+"_ggSent" # Gene_gene
     result_file=session['path']+"_ggResult"
+    
+    # pubmed_path needs to be defined, assuming it's a global or config
+    # For this example, let's assume it's a pre-configured path.
+    # If not, this os.system call will fail or use current dir.
+    # pubmed_path = "/path/to/local/pubmed/mirror" # Example, should be configured
+    pubmed_path = "./pubmed_data/" # Placeholder, ensure this path exists or is correctly set
+    os.makedirs(pubmed_path, exist_ok=True) # Ensure it exists if it's a local relative path
+
+    def findWholeWord(w): # Helper function, should be defined if not in more_functions
+        return re.compile(r'(?<!\w)({})(?!\w)'.format(w), flags=re.IGNORECASE).search
 
     def generate(query):
+        from nltk.tokenize import sent_tokenize # Local import
         progress=1
         yield "data:"+str(progress)+"\n\n"
-        os.system("esearch -db pubmed -query \"" +  query + "\" | efetch -format uid |sort >" + tmp_ggPMID)
-        abstracts=os.popen("comm -1 -2 topGene_uniq.pmid " + tmp_ggPMID + " |fetch-pubmed -path "+pubmed_path+ " | xtract -pattern PubmedArticle -element MedlineCitation/PMID,ArticleTitle,AbstractText|sed \"s/-/ /g\"").read()
-        os.system("rm "+tmp_ggPMID)
+        # Ensure query is safe for shell command
+        safe_query = query.replace("\"", "\\\"") # Basic escaping
+        os.system(f"esearch -db pubmed -query \"{safe_query}\" | efetch -format uid |sort > \"{tmp_ggPMID}\"")
+        
+        # 'topGene_uniq.pmid' file needs to exist
+        # For robustness, check if it exists
+        top_gene_pmid_file = "topGene_uniq.pmid"
+        if not os.path.exists(top_gene_pmid_file):
+            print(f"Warning: {top_gene_pmid_file} not found. Gene-gene search might be affected.")
+            # Create an empty file to prevent comm command error, or handle differently
+            open(top_gene_pmid_file, 'a').close() 
+
+        abstracts_cmd = f"comm -1 -2 \"{top_gene_pmid_file}\" \"{tmp_ggPMID}\" | fetch-pubmed -path \"{pubmed_path}\" | xtract -pattern PubmedArticle -element MedlineCitation/PMID,ArticleTitle,AbstractText | sed \"s/-/ /g\""
+        try:
+            abstracts_process = os.popen(abstracts_cmd)
+            abstracts = abstracts_process.read()
+            abstracts_process.close()
+        except Exception as e_abs:
+            print(f"Error getting abstracts for gene-gene search: {e_abs}")
+            abstracts = ""
+
+        if os.path.exists(tmp_ggPMID): # Clean up temp file
+            os.system(f"rm \"{tmp_ggPMID}\"")
+
         progress=10
         yield "data:"+str(progress)+"\n\n"
         topGenes=dict()
-        out=str()
+        out_str=str() # Renamed from out
         hitGenes=dict()
-        with open("topGene_symb_alias.txt", "r") as top_f:
-            for line in top_f:
-                (symb, alias)=line.strip().split("\t")
-                topGenes[symb]=alias.replace("; ","|")
+        
+        # 'topGene_symb_alias.txt' file needs to exist
+        top_gene_alias_file = "topGene_symb_alias.txt"
+        if os.path.exists(top_gene_alias_file):
+            with open(top_gene_alias_file, "r") as top_f:
+                for line in top_f:
+                    parts = line.strip().split("\t")
+                    if len(parts) == 2:
+                        symb, alias = parts
+                        topGenes[symb]=alias.replace("; ","|")
+        else:
+            print(f"Warning: {top_gene_alias_file} not found. Top gene list will be empty.")
+
         allAbstracts= abstracts.split("\n")
-        abstractCnt=len(allAbstracts)
+        abstractCnt=len(allAbstracts) if abstracts else 0 # Handle empty abstracts
         rowCnt=0
 
         for row in allAbstracts:
+            if not row.strip(): continue
             rowCnt+=1
-            if rowCnt/10==int(rowCnt/10):
+            if abstractCnt > 0 and rowCnt % 10 == 0 : # Check abstractCnt > 0
                 progress=10+round(rowCnt/abstractCnt,2)*80
                 yield "data:"+str(progress)+"\n\n"
-            tiab=row.split("\t")
-            pmid = tiab.pop(0)
-            tiab= " ".join(tiab)
-            sentences = sent_tokenize(tiab)
+            
+            tiab_parts=row.split("\t", 1) # Split only on first tab
+            if len(tiab_parts) < 2: continue # Skip malformed lines
+            pmid = tiab_parts[0]
+            tiab_text_gg = tiab_parts[1] # Renamed
+            
+            sentences_gg = sent_tokenize(tiab_text_gg) # Renamed
             ## keep the sentence only if it contains the gene 
-            for sent in sentences:
-                if findWholeWord(query)(sent):
-                    sent=re.sub(r'\b(%s)\b' % query, r'<strong>\1</strong>', sent, flags=re.I)
-                    for symb in topGenes:
-                        allNames=symb+"|"+topGenes[symb]
-                        if findWholeWord(allNames)(sent) :
-                            sent=sent.replace("<b>","").replace("</b>","")
-                            sent=re.sub(r'\b(%s)\b' % allNames, r'<b>\1</b>', sent, flags=re.I)
-                            out+=query+"\t"+"gene\t" + symb+"\t"+pmid+"\t"+sent+"\n"
-                            if symb in hitGenes.keys():
-                                hitGenes[symb]+=1
+            for sent_item in sentences_gg: # Renamed
+                if findWholeWord(query)(sent_item):
+                    sent_item=re.sub(r'\b(%s)\b' % query, r'<strong>\1</strong>', sent_item, flags=re.I)
+                    for symb_item in topGenes: # Renamed
+                        allNames=symb_item+"|"+topGenes[symb_item]
+                        if findWholeWord(allNames)(sent_item) :
+                            sent_item=sent_item.replace("<b>","").replace("</b>","") # Clean previous bolds
+                            sent_item=re.sub(r'\b(%s)\b' % allNames, r'<b>\1</b>', sent_item, flags=re.I) # Bold current match
+                            out_str+=query+"\t"+"gene\t" + symb_item+"\t"+pmid+"\t"+sent_item+"\n"
+                            if symb_item in hitGenes: # Check if key exists
+                                hitGenes[symb_item]+=1
                             else:
-                                hitGenes[symb]=1
+                                hitGenes[symb_item]=1
         progress=95
         yield "data:"+str(progress)+"\n\n"
         with open(gg_file, "w+") as gg:
-            gg.write(out)
-            gg.close()
-        results="<h4>"+query+" vs top addiction genes</h4> Click on the number of sentences will show those sentences. Click on the <span style=\"background-color:#FcF3cf\">top addiction genes</span> will show an archived search for that gene.<hr>"
+            gg.write(out_str)
+            # gg.close() # Not needed with 'with open'
+        
+        results_html="<h4>"+query+" vs top addiction genes</h4> Click on the number of sentences will show those sentences. Click on the <span style=\"background-color:#FcF3cf\">top addiction genes</span> will show an archived search for that gene.<hr>" # Renamed
         topGeneHits={}
-        for key in hitGenes.keys():
-            url=gg_file+"|"+query+"|"+key
-            if hitGenes[key]==1:
-                sentword="sentence"
-            else:
-                sentword="sentences"
-            topGeneHits[ "<li> <a href=/sentences?edgeID=" + url+ " target=_new>" + "Show " + str(hitGenes[key]) + " " + sentword +" </a> about "+query+" and <a href=/showTopGene?topGene="+key+" target=_gene><span style=\"background-color:#FcF3cf\">"+key+"</span></a>" ]=hitGenes[key]
-        topSorted = [(k, topGeneHits[k]) for k in sorted(topGeneHits, key=topGeneHits.get, reverse=True)]
+        for key_gene in hitGenes.keys(): # Renamed
+            url_gg=gg_file+"|"+query+"|"+key_gene # Renamed
+            sentword="sentence" if hitGenes[key_gene]==1 else "sentences"
+            topGeneHits[ "<li> <a href=/sentences?edgeID=" + url_gg+ " target=_new>" + "Show " + str(hitGenes[key_gene]) + " " + sentword +" </a> about "+query+" and <a href=/showTopGene?topGene="+key_gene+" target=_gene><span style=\"background-color:#FcF3cf\">"+key_gene+"</span></a>" ]=hitGenes[key_gene]
         
-        for k,v in topSorted:
-            results+=k
-        saveResult=open(result_file, "w+")
-        saveResult.write(results)
-        saveResult.close()
+        topSorted = sorted(topGeneHits.items(), key=lambda item: item[1], reverse=True) # Correct way to sort dict by value
+        
+        for k_html,v_count in topSorted: # Renamed
+            results_html+=k_html
+        
+        with open(result_file, "w+") as saveResult: # Ensure it's opened in write mode
+            saveResult.write(results_html)
+            # saveResult.close() # Not needed
+
         progress=100
         yield "data:"+str(progress)+"\n\n"
     
     # Start the run
-    query=session['forTopGene']
-    return Response(generate(query), mimetype='text/event-stream')
+    query_gene_gene=session.get('forTopGene', '') # Get from session, default to empty
+    if not query_gene_gene:
+        return Response("Error: No gene query found for gene-gene search.", mimetype='text/event-stream')
+    return Response(generate(query_gene_gene), mimetype='text/event-stream')
 
 
 @app.route('/showGeneTopGene')
 def showGeneTopGene ():
-    with open(session['path']+"_ggResult", "r") as result_f:
-        results=result_f.read()
-    return render_template('sentences.html', sentences=results+"<p><br>")
+    results_content = "<p>No results found.</p>" # Default content
+    result_file_path = session.get('path', '') + "_ggResult" # Get path from session
+    if result_file_path and os.path.exists(result_file_path):
+        with open(result_file_path, "r") as result_f:
+            results_content=result_f.read()
+    else:
+        print(f"Warning: Result file {result_file_path} not found for showGeneTopGene.")
+    return render_template('sentences.html', sentences=results_content+"<p><br>")
 
 
 # Generate a page that lists all the top 150 addiction genes with links to cytoscape graph.
@@ -1500,5 +2038,5 @@ def top150genes():
 
 
 if __name__ == '__main__':
-    db.create_all()
-    app.run(debug=True, port=4200)
+    # For production, consider using a more robust web server like Gunicorn or Waitress
+    app.run(debug=True, host='0.0.0.0', port=4200) # Changed to 0.0.0.0 for accessibility if needed