Update app.py

app.py

@@ -5,7 +5,6 @@ import pandas as pd
 import os
 import gradio as gr
 
-# Load the models and tokenizers
 bert_model_name = 'bert-base-uncased'
 bert_tokenizer = BertTokenizer.from_pretrained(bert_model_name)
 bert_model = BertModel.from_pretrained(bert_model_name)
@@ -14,7 +13,6 @@ gpt2_model_name = 'gpt2'
 gpt2_tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model_name)
 gpt2_model = GPT2LMHeadModel.from_pretrained(gpt2_model_name)
 
-# Load the data
 data = {
     "questions": [
         "What is Rookus?",
@@ -39,6 +37,18 @@ data = {
     "default_answers": "I'm sorry, I cannot answer this right now. Your question has been saved, and we will get back to you with a response soon."
 }
 
+def save_to_excel(user_query, default_answer, excel_file='new_prompts.xlsx'):
+    if not os.path.isfile(excel_file):
+        df = pd.DataFrame(columns=['question', 'default_answer'])
+    else:
+        df = pd.read_excel(excel_file)
+    
+    new_data = pd.DataFrame({'question': [user_query], 'default_answer': [default_answer]})
+    df = pd.concat([df, new_data], ignore_index=True)
+    
+    with pd.ExcelWriter(excel_file, engine='openpyxl', mode='w') as writer:
+        df.to_excel(writer, index=False)
+
 def get_bert_embeddings(texts):
     inputs = bert_tokenizer(texts, return_tensors='pt', padding=True, truncation=True)
     with torch.no_grad():
@@ -70,18 +80,10 @@ def answer_query(user_query):
         answer_index = data['questions'].index(closest_question)
         answer = data['answers'][answer_index]
     else:
-        excel_file = 'new_questions1.xlsx'
-        if not os.path.isfile(excel_file):
-            df = pd.DataFrame(columns=['question'])
-            df.to_excel(excel_file, index=False)
-        
-        new_data = pd.DataFrame({'questions': [user_query]})
-        df = pd.read_excel(excel_file)
-        df = pd.concat([df, new_data], ignore_index=True)
-        with pd.ExcelWriter(excel_file, engine='openpyxl', mode='w') as writer:
-            df.to_excel(writer, index=False)
         answer = data['default_answers']
-
+    
+    save_to_excel(user_query, answer)  
+    
     return answer
 
 iface = gr.Interface(