Update app.py

app.py

@@ -143,8 +143,10 @@ def update_vectors(files, parser):
     # Save the updated list of documents
     save_documents(uploaded_documents)
 
+    # Return a tuple with the status message and the updated document list
     return f"Vector store updated successfully. Processed {total_chunks} chunks from {len(files)} files using {parser}.", display_documents()
 
+
 def delete_documents(selected_docs):
     global uploaded_documents
     
@@ -381,8 +383,7 @@ def summarize_web_results(query: str, search_results: List[Dict[str, str]], conv
         You have to create a comprehensive news summary FOCUSING on the context provided to you. 
         Include key facts, relevant statistics, and expert opinions if available. 
         Ensure the article is well-structured with an introduction, main body, and conclusion, IF NECESSARY. 
-        Determine if the new query is a continuation of the previous conversation or an entirely new topic, if its in contiuation, address the query in the context of the ongoing conversation.
-        If it's a new topic, than address the query as a new conversation.
+        Address the query in the context of the ongoing conversation IF APPLICABLE.
         Cite sources directly within the generated text and not at the end of the generated text, integrating URLs where appropriate to support the information provided:
 
         {search_context}
@@ -395,7 +396,7 @@ def summarize_web_results(query: str, search_results: List[Dict[str, str]], conv
         return f"An error occurred during summarization: {str(e)}"
 
 # Modify the existing respond function to handle both PDF and web search
-def respond(message, history, model, temperature, num_calls, selected_docs, use_web_search):
+def respond(message, history, model, temperature, num_calls, use_web_search, selected_docs):
     logging.info(f"User Query: {message}")
     logging.info(f"Model Used: {model}")
     logging.info(f"Selected Documents: {selected_docs}")
@@ -437,7 +438,7 @@ def respond(message, history, model, temperature, num_calls, selected_docs, use_
                 if not relevant_docs:
                     yield "No relevant information found in the selected documents. Please try selecting different documents or rephrasing your query."
                     return
-
+    
                 context_str = "\n".join([doc.page_content for doc in relevant_docs])
                 logging.info(f"Context length: {len(context_str)}")
             else:
@@ -445,23 +446,27 @@ def respond(message, history, model, temperature, num_calls, selected_docs, use_
                 yield "No documents available. Please upload PDF documents to answer questions."
                 return
             
-            if model == "@cf/meta/llama-3.1-8b-instruct":
+            if model.startswith("duckduckgo/"):
+                # Use DuckDuckGo chat with context
+                for partial_response in get_response_from_duckduckgo(message, model, context_str, num_calls, temperature):
+                    yield partial_response
+            elif model == "@cf/meta/llama-3.1-8b-instruct":
                 # Use Cloudflare API
-                for response in get_response_from_cloudflare(prompt="", context=context_str, query=message, num_calls=num_calls, temperature=temperature, search_type="pdf"):
-                    yield response
+                for partial_response in get_response_from_cloudflare(prompt="", context=context_str, query=message, num_calls=num_calls, temperature=temperature, search_type="pdf"):
+                    yield partial_response
             else:
                 # Use Hugging Face API
-                for response in get_response_from_pdf(message, model, selected_docs, num_calls=num_calls, temperature=temperature):
-                    yield response
+                for partial_response in get_response_from_pdf(message, model, selected_docs, num_calls=num_calls, temperature=temperature):
+                    yield partial_response
         except Exception as e:
             logging.error(f"Error with {model}: {str(e)}")
             if "microsoft/Phi-3-mini-4k-instruct" in model:
                 logging.info("Falling back to Mistral model due to Phi-3 error")
                 fallback_model = "mistralai/Mistral-7B-Instruct-v0.3"
-                yield from respond(message, history, fallback_model, temperature, num_calls, selected_docs, use_web_search)
+                yield from respond(message, history, fallback_model, temperature, num_calls, selected_docs)
             else:
                 yield f"An error occurred with the {model} model: {str(e)}. Please try again or select a different model."
-
+        
 logging.basicConfig(level=logging.DEBUG)
 
 def get_response_from_cloudflare(prompt, context, query, num_calls=3, temperature=0.2, search_type="pdf"):
@@ -645,7 +650,7 @@ def refresh_documents():
 # Define the checkbox outside the demo block
 document_selector = gr.CheckboxGroup(label="Select documents to query")
 
-use_web_search = gr.Checkbox(label="Use Web Search", value=True)
+use_web_search = gr.Checkbox(label="Use Web Search", value=False)
 
 custom_placeholder = "Ask a question (Note: You can toggle between Web Search and PDF Chat in Additional Inputs below)"
 
@@ -658,8 +663,8 @@ demo = gr.ChatInterface(
         gr.Dropdown(choices=MODELS, label="Select Model", value=MODELS[3]),
         gr.Slider(minimum=0.1, maximum=1.0, value=0.2, step=0.1, label="Temperature"),
         gr.Slider(minimum=1, maximum=5, value=1, step=1, label="Number of API Calls"),
-        gr.CheckboxGroup(label="Select documents to query", choices=[]),
-        gr.Checkbox(label="Use Web Search", value=True)
+        gr.Checkbox(label="Use Web Search", value=True),
+        gr.CheckboxGroup(label="Select documents to query")        
     ],
     title="AI-powered PDF Chat and Web Search Assistant",
     description="Chat with your PDFs or use web search to answer questions.",
@@ -700,10 +705,10 @@ demo = gr.ChatInterface(
     )
 )
 
+# Add file upload functionality
 # Add file upload functionality
 with demo:
     gr.Markdown("## Upload and Manage PDF Documents")
-
     with gr.Row():
         file_input = gr.Files(label="Upload your PDF documents", file_types=[".pdf"])
         parser_dropdown = gr.Dropdown(choices=["pypdf", "llamaparse"], label="Select PDF Parser", value="llamaparse")
@@ -714,19 +719,25 @@ with demo:
     delete_button = gr.Button("Delete Selected Documents")
     
     # Update both the output text and the document selector
-    update_button.click(update_vectors, 
-                        inputs=[file_input, parser_dropdown], 
-                        outputs=[update_output, document_selector])
+    update_button.click(
+        update_vectors, 
+        inputs=[file_input, parser_dropdown], 
+        outputs=[update_output, demo.additional_inputs[-1]]  # Use the CheckboxGroup from additional_inputs
+    )
     
     # Add the refresh button functionality
-    refresh_button.click(refresh_documents, 
-                         inputs=[], 
-                         outputs=[document_selector])
+    refresh_button.click(
+        refresh_documents, 
+        inputs=[], 
+        outputs=[demo.additional_inputs[-1]]  # Use the CheckboxGroup from additional_inputs
+    )
     
     # Add the delete button functionality
-    delete_button.click(delete_documents,
-                        inputs=[document_selector],
-                        outputs=[update_output, document_selector])
+    delete_button.click(
+        delete_documents,
+        inputs=[demo.additional_inputs[-1]],  # Use the CheckboxGroup from additional_inputs
+        outputs=[update_output, demo.additional_inputs[-1]]
+    )
 
     gr.Markdown(
     """