Update app.py

app.py

@@ -16,6 +16,27 @@ client = Client("on1onmangoes/CNIHUB101324v10", hf_token=HF_TOKEN)
 # Update the conversation history within the function.
 # Return the updated history along with any other required outputs.
 
+def format_answer_with_documents(answer):
+    """
+    This function formats the assistant's answer and separates the personal details from the relevant documents.
+    """
+    # Extract the personal details and documents from the answer
+    personal_details = answer[0]
+    relevant_documents = answer[1:]  # Assuming documents are included after the answer
+
+    # Format the personal details
+    formatted_answer = f"Based on the documents provided, the personal details are as follows:\n\n{personal_details}\n\n"
+
+    # If there are relevant documents, format them as a separate section
+    if relevant_documents:
+        formatted_answer += "Relevant Documents:\n"
+        for idx, document in enumerate(relevant_documents, start=1):
+            formatted_answer += f"{idx}. {document.metadata['heading']} (Page {int(document.metadata['page_number'])})\n"
+            formatted_answer += f"   Source: {document.metadata['source']}\n"
+            formatted_answer += f"   Snippet: {document.page_content[:200]}...\n\n"  # Showing a snippet of content
+
+    return formatted_answer
+
 def stream_chat_with_rag(
     message: str,
     history: list,
@@ -32,24 +53,66 @@ def stream_chat_with_rag(
     print(f"Message: {message}")
     print(f"History: {history}")
 
-    
     # Build the conversation prompt including system prompt and history
-    conversation = system_prompt + "\n\n" + "For Client:" + client_name
+    conversation = f"{system_prompt}\n\nFor Client: {client_name}\n"
+    
+    # Add previous conversation history
     for user_input, assistant_response in history:
         conversation += f"User: {user_input}\nAssistant: {assistant_response}\n"
+    
+    # Add the current user message
     conversation += f"User: {message}\nAssistant:"
+
+    # Call the API with the user's message
     question = message
     answer = client.predict(question=question, api_name="/answer_with_rag")
-    # debug 092624
+    
+    # Debugging: Print the response
     print("The Answer in stream_chat_with_rag:")
     print(answer)
 
-    # print("The relevant_doc:")
-    # print(relevant_docs)
+    # Format the assistant's answer to separate the relevant documents
+    formatted_answer = format_answer_with_documents(answer)
+
+    # Update the conversation history with the new message and answer
+    history.append((message, formatted_answer))
+
+    # Return the formatted answer
+    return formatted_answer
+
+
+# this version works just issue with formatting 
+# def stream_chat_with_rag(
+#     message: str,
+#     history: list,
+#     client_name: str,
+#     system_prompt: str,
+#     num_retrieved_docs: int = 10,
+#     num_docs_final: int = 9,
+#     temperature: float = 0, 
+#     max_new_tokens: int = 1024, 
+#     top_p: float = 1.0, 
+#     top_k: int = 20, 
+#     penalty: float = 1.2,
+# ):
+#     print(f"Message: {message}")
+#     print(f"History: {history}")
+
     
-    # Update the conversation history
-    history.append((message, answer))
-    return answer
+#     # Build the conversation prompt including system prompt and history
+#     conversation = system_prompt + "\n\n" + "For Client:" + client_name
+#     for user_input, assistant_response in history:
+#         conversation += f"User: {user_input}\nAssistant: {assistant_response}\n"
+#     conversation += f"User: {message}\nAssistant:"
+#     question = message
+#     answer = client.predict(question=question, api_name="/answer_with_rag")
+#     # debug 092624
+#     print("The Answer in stream_chat_with_rag:")
+#     print(answer)
+
+#     # Update the conversation history
+#     history.append((message, answer))
+#     return answer
 
 
 # Function to handle PDF processing API call