Update app.py

app.py

@@ -209,31 +209,36 @@ def format_chat_history(message, chat_history):
     return formatted_chat_history
     
 
-def conversation(qa_chain, message, history):
+def conversation(vector_db, message, history):
+    # Initialize ChatOpenAI with your desired settings
+    chat_open_ai = ChatOpenAI(temperature=0.9, model_name="gpt-3.5-turbo")
+    # Assuming vector_db is already initialized correctly
+    pdf_ga = ChatVectorDBChain.from_llm(chat_open_ai, vector_db, return_source_documents=True)
+    
+    # Format the chat history for input to the model
     formatted_chat_history = format_chat_history(message, history)
-    #print("formatted_chat_history",formatted_chat_history)
-   
+    
     # Generate response using QA chain
-    response = qa_chain({"question": message, "chat_history": formatted_chat_history})
-    response_answer = response["answer"]
-    if response_answer.find("Helpful Answer:") != -1:
+    result = pdf_ga({"question": message, "chat_history": formatted_chat_history})
+    response_answer = result["answer"]
+    if "Helpful Answer:" in response_answer:
         response_answer = response_answer.split("Helpful Answer:")[-1]
-    response_sources = response["source_documents"]
-    response_source1 = response_sources[0].page_content.strip()
-    response_source2 = response_sources[1].page_content.strip()
-    response_source3 = response_sources[2].page_content.strip()
-    # Langchain sources are zero-based
-    response_source1_page = response_sources[0].metadata["page"] + 1
-    response_source2_page = response_sources[1].metadata["page"] + 1
-    response_source3_page = response_sources[2].metadata["page"] + 1
-    # print ('chat response: ', response_answer)
-    # print('DB source', response_sources)
+    
+    response_sources = result["source_documents"]
+    response_source1 = response_sources[0].page_content.strip() if len(response_sources) > 0 else "No source available"
+    response_source2 = response_sources[1].page_content.strip() if len(response_sources) > 1 else "No source available"
+    response_source3 = response_sources[2].page_content.strip() if len(response_sources) > 2 else "No source available"
+    
+    # Langchain sources are zero-based; adjust pages if sources are available
+    response_source1_page = response_sources[0].metadata["page"] + 1 if len(response_sources) > 0 else "N/A"
+    response_source2_page = response_sources[1].metadata["page"] + 1 if len(response_sources) > 1 else "N/A"
+    response_source3_page = response_sources[2].metadata["page"] + 1 if len(response_sources) > 2 else "N/A"
     
     # Append user message and response to chat history
     new_history = history + [(message, response_answer)]
-    # return gr.update(value=""), new_history, response_sources[0], response_sources[1] 
-    return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page
     
+    return gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page
+
 
 def upload_file(file_obj):
     list_file_path = []