Update app.py

app.py

@@ -37,7 +37,11 @@ MODELS = [
     "mistralai/Mistral-7B-Instruct-v0.3",
     "mistralai/Mixtral-8x7B-Instruct-v0.1",
     "@cf/meta/llama-3.1-8b-instruct",
-    "mistralai/Mistral-Nemo-Instruct-2407"
+    "mistralai/Mistral-Nemo-Instruct-2407",
+    "duckduckgo/gpt-4o-mini",
+    "duckduckgo/claude-3-haiku",
+    "duckduckgo/llama-3.1-70b",
+    "duckduckgo/mixtral-8x7b"
 ]
 
 # Initialize LlamaParse
@@ -297,41 +301,60 @@ def retry_last_response(history, model, temperature, num_calls):
     
     return chatbot_interface(last_user_msg, history, model, temperature, num_calls)
 
+def get_response_from_duckduckgo(query, model, num_calls=1, temperature=0.2):
+    logging.info(f"Using DuckDuckGo chat with model: {model}")
+    ddg_model = model.split('/')[-1]  # Extract the model name from the full string
+    
+    full_response = ""
+    for _ in range(num_calls):
+        try:
+            results = DDGS().chat(query, model=ddg_model)
+            full_response += results + "\n"
+        except Exception as e:
+            logging.error(f"Error in generating response from DuckDuckGo: {str(e)}")
+            yield f"An error occurred with the {model} model: {str(e)}. Please try again."
+            return
+
+    yield full_response.strip()
+
 def respond(message, history, model, temperature, num_calls, selected_docs):
     logging.info(f"User Query: {message}")
     logging.info(f"Model Used: {model}")
     logging.info(f"Selected Documents: {selected_docs}")
 
     try:
-        embed = get_embeddings()
-        if os.path.exists("faiss_database"):
-            database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
-            retriever = database.as_retriever(search_kwargs={"k": 20})
-            
-            # Filter relevant documents based on user selection
-            all_relevant_docs = retriever.get_relevant_documents(message)
-            relevant_docs = [doc for doc in all_relevant_docs if doc.metadata["source"] in selected_docs]
-            
-            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])
-        else:
-            context_str = "No documents available."
-            yield "No documents available. Please upload PDF documents to answer questions."
-            return
-        
-        if model == "@cf/meta/llama-3.1-8b-instruct":
-            # Use Cloudflare API
-            for partial_response in get_response_from_cloudflare(prompt="", context=context_str, query=message, num_calls=num_calls, temperature=temperature, search_type="pdf"):
-                first_line = partial_response.split('\n')[0] if partial_response else ''
+        if model.startswith("duckduckgo/"):
+            # Use DuckDuckGo chat for the new models
+            for partial_response in get_response_from_duckduckgo(message, model, num_calls, temperature):
                 yield partial_response
         else:
-            # Use Hugging Face API
-            for partial_response in get_response_from_pdf(message, model, selected_docs, num_calls=num_calls, temperature=temperature):
-                first_line = partial_response.split('\n')[0] if partial_response else ''
-                yield partial_response
+            # Existing logic for PDF-based queries and other models
+            embed = get_embeddings()
+            if os.path.exists("faiss_database"):
+                database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
+                retriever = database.as_retriever(search_kwargs={"k": 20})
+                
+                all_relevant_docs = retriever.get_relevant_documents(message)
+                relevant_docs = [doc for doc in all_relevant_docs if doc.metadata["source"] in selected_docs]
+                
+                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])
+            else:
+                context_str = "No documents available."
+                yield "No documents available. Please upload PDF documents to answer questions."
+                return
+            
+            if model == "@cf/meta/llama-3.1-8b-instruct":
+                # Use Cloudflare API
+                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 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:
@@ -528,6 +551,7 @@ use_web_search = gr.Checkbox(label="Use Web Search", value=True)
 
 custom_placeholder = "Ask a question (Note: You can toggle between Web Search and PDF Chat in Additional Inputs below)"
 
+# Update the demo interface
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
@@ -536,8 +560,8 @@ demo = gr.ChatInterface(
         gr.Slider(minimum=1, maximum=5, value=1, step=1, label="Number of API Calls"),
         document_selector
     ],
-    title="AI-powered PDF Chat Assistant",
-    description="Chat with your PDFs to answer questions.",
+    title="AI-powered PDF Chat and DuckDuckGo Chat Assistant",
+    description="Chat with your PDFs or use DuckDuckGo chat models to answer questions.",
     theme=gr.themes.Soft(
         primary_hue="orange",
         secondary_hue="amber",
@@ -560,18 +584,19 @@ demo = gr.ChatInterface(
     examples=[
         ["Tell me about the contents of the uploaded PDFs."],
         ["What are the main topics discussed in the documents?"],
-        ["Can you summarize the key points from the PDFs?"]
+        ["Can you summarize the key points from the PDFs?"],
+        ["What's the latest news about artificial intelligence?"]
     ],
     cache_examples=False,
     analytics_enabled=False,
-    textbox=gr.Textbox(placeholder="Ask a question about the uploaded PDFs", container=False, scale=7),
+    textbox=gr.Textbox(placeholder="Ask a question about the uploaded PDFs or any topic", container=False, scale=7),
     chatbot = gr.Chatbot(  
-    show_copy_button=True,
-    likeable=True,
-    layout="bubble",
-    height=400,
-    value=initial_conversation()
-)
+        show_copy_button=True,
+        likeable=True,
+        layout="bubble",
+        height=400,
+        value=initial_conversation()
+    )
 )
 
 # Add file upload functionality