Update app.py

app.py

@@ -13,11 +13,7 @@ logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 # Page configuration
-st.set_page_config(
-    page_title="DeepSeek Chatbot - ruslanmv.com",
-    page_icon="🤖",
-    layout="centered"
-)
+st.set_page_config(page_title="DeepSeek Chatbot - ruslanmv.com", page_icon="🤖", layout="centered")
 
 # Initialize session state for chat history
 if "messages" not in st.session_state:
@@ -29,31 +25,13 @@ with st.sidebar:
     st.markdown("[Get HuggingFace Token](https://huggingface.co/settings/tokens)")
 
     # Dropdown to select model
-    model_options = [
-        "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
-    ]
+    model_options = ["deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"]
     selected_model = st.selectbox("Select Model", model_options, index=0)
 
-    system_message = st.text_area(
-        "System Message",
-        value="You are a friendly chatbot created by who Provide clear, accurate, and brief answers. Keep responses polite, engaging, and to the point. If unsure, politely suggest alternatives.",
-        height=100
-    )
-
-    max_tokens = st.slider(
-        "Max Tokens",
-        10, 4000, 100
-    )
-
-    temperature = st.slider(
-        "Temperature",
-        0.1, 4.0, 0.3
-    )
-
-    top_p = st.slider(
-        "Top-p",
-        0.1, 1.0, 0.6
-    )
+    system_message = st.text_area("System Message", value="You are a friendly chatbot. Provide clear, accurate, and brief answers.", height=100)
+    max_tokens = st.slider("Max Tokens", 10, 4000, 100)
+    temperature = st.slider("Temperature", 0.1, 4.0, 0.3)
+    top_p = st.slider("Top-p", 0.1, 1.0, 0.6)
 
 # Function to query the Hugging Face API
 def query(payload, api_url):
@@ -79,11 +57,7 @@ def process_pdf(uploaded_file):
     documents = loader.load()
 
     # Split the documents into chunks
-    text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size=1000,
-        chunk_overlap=200,
-        add_start_index=True
-    )
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200, add_start_index=True)
     return text_splitter.split_documents(documents)
 
 # Function to generate response using LangChain
@@ -120,8 +94,13 @@ if uploaded_file:
     documents = process_pdf(uploaded_file)
     context = "\n\n".join([doc.page_content for doc in documents])
 
-    # Ask the user a question
-    if prompt := st.chat_input("Type your message..."):
+    # Combine system message and user input into a single prompt
+    prompt_input = "Ask a question about the PDF content"
+
+    # Show the PDF-based question input if the PDF is uploaded
+    prompt = st.chat_input(prompt_input) if documents else None
+
+    if prompt:
         st.session_state.messages.append({"role": "user", "content": prompt})
 
         with st.chat_message("user"):
@@ -129,60 +108,14 @@ if uploaded_file:
 
         try:
             with st.spinner("Generating response..."):
-                # Combine system message and user input into a single prompt
-                full_prompt = f"{system_message}\n\nUser: {prompt}\nAssistant:"
-                payload = {
-                    "inputs": full_prompt,
-                    "parameters": {
-                        "max_new_tokens": max_tokens,
-                        "temperature": temperature,
-                        "top_p": top_p,
-                        "return_full_text": False
-                    }
-                }
-
-                # Dynamically construct the API URL based on the selected model
-                api_url = f"https://api-inference.huggingface.co/models/{selected_model}"
-                logger.info(f"Selected model: {selected_model}, API URL: {api_url}")
-
-                # Query the Hugging Face API using the selected model
-                output = query(payload, api_url)
-
-                # Handle API response
-                if output is not None and isinstance(output, list) and len(output) > 0:
-                    if 'generated_text' in output[0]:
-                        assistant_response = output[0]['generated_text'].strip()
-
-                        # Check for and remove duplicate responses
-                        responses = assistant_response.split("\n</think>\n")
-                        unique_response = responses[0].strip()
-
-                        logger.info(f"Generated response: {unique_response}")
-
-                        # Append response to chat only once
-                        with st.chat_message("assistant"):
-                            st.markdown(unique_response)
-
-                        st.session_state.messages.append({"role": "assistant", "content": unique_response})
-                    else:
-                        logger.error(f"Unexpected API response structure: {output}")
-                        st.error("Error: Unexpected response from the model. Please try again.")
-                else:
-                    logger.error(f"Empty or invalid API response: {output}")
-                    st.error("Error: Unable to generate a response. Please check the model and try again.")
+                answer = generate_response_with_langchain(prompt, context)
+
+                # Show the answer from LangChain model
+                with st.chat_message("assistant"):
+                    st.markdown(answer)
+
+                st.session_state.messages.append({"role": "assistant", "content": answer})
 
         except Exception as e:
             logger.error(f"Application Error: {str(e)}", exc_info=True)
             st.error(f"Application Error: {str(e)}")
-
-# Allow user to ask a question based on extracted PDF content
-if uploaded_file and documents:  # Ensure documents exist before proceeding
-    if prompt := st.chat_input("Ask a question about the PDF content"):
-        context = "\n\n".join([doc.page_content for doc in documents])  # Get context from documents
-        answer = generate_response_with_langchain(prompt, context)
-        
-        # Show the answer from LangChain model
-        with st.chat_message("assistant"):
-            st.markdown(answer)
-
-        st.session_state.messages.append({"role": "assistant", "content": answer})