Update app.py

app.py

@@ -167,9 +167,7 @@ def format_chat_history(message, chat_history):
 
 def conversation(qa_chain, message, history):
     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:
@@ -182,12 +180,11 @@ def conversation(qa_chain, message, history):
     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)
     
-    # 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
     
 
@@ -196,8 +193,7 @@ def upload_file(file_obj):
     for idx, file in enumerate(file_obj):
         file_path = file_obj.name
         list_file_path.append(file_path)
-    # print(file_path)
-    # initialize_database(file_path, progress)
+    
     return list_file_path
 
 
@@ -207,20 +203,10 @@ def demo():
         qa_chain = gr.State()
         collection_name = gr.State()
         
-        gr.Markdown(
-        """<center><h2>PDF-based chatbot</center></h2>
-        <h3>Ask any questions about your PDF documents</h3>""")
-        gr.Markdown(
-        """<b>Note:</b> This AI assistant, using Langchain and open-source LLMs, performs retrieval-augmented generation (RAG) from your PDF documents. \
-        The user interface explicitely shows multiple steps to help understand the RAG workflow. 
-        This chatbot takes past questions into account when generating answers (via conversational memory), and includes document references for clarity purposes.<br>
-        <br><b>Warning:</b> This space uses the free CPU Basic hardware from Hugging Face. Some steps and LLM models used below (free inference endpoints) can take some time to generate a reply.
-        """)
-        
         with gr.Tab("Step 1 - Upload PDF"):
             with gr.Row():
                 document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)")
-                # upload_btn = gr.UploadButton("Loading document...", height=100, file_count="multiple", file_types=["pdf"], scale=1)
+                
         
         with gr.Tab("Step 2 - Process document"):
             with gr.Row():
@@ -268,9 +254,7 @@ def demo():
             with gr.Row():
                 submit_btn = gr.Button("Submit message")
                 clear_btn = gr.ClearButton([msg, chatbot], value="Clear conversation")
-            
-        # Preprocessing events
-        #upload_btn.upload(upload_file, inputs=[upload_btn], outputs=[document])
+        
         db_btn.click(initialize_database, \
             inputs=[document, slider_chunk_size, slider_chunk_overlap], \
             outputs=[vector_db, collection_name, db_progress])
@@ -281,7 +265,7 @@ def demo():
             outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
             queue=False)
 
-        # Chatbot events
+        
         msg.submit(conversation, \
             inputs=[qa_chain, msg, chatbot], \
             outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \