Update app.py

app.py

@@ -49,59 +49,59 @@ if st.session_state.page == "home":
 # Example domain-specific chatbot page
 elif st.session_state.page == "python":
     st.title("Python Chatbot 🐍")
-    hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN") or os.getenv("HF_TOKEN")
-    if not hf_token:
-        st.error("Please add your Hugging Face API token to Secrets (HUGGINGFACEHUB_API_TOKEN or HF_TOKEN).")
-        st.stop()
-
-# Setup the LangChain HuggingFaceEndpoint and ChatHuggingFace LLM
-    deep_seek_model = HuggingFaceEndpoint(
-    repo_id="deepseek-ai/DeepSeek-R1",
-    # provider = 'nebius'
-    temperature=0.7,
-    max_new_tokens=100,
-    task="conversational",
-    huggingfacehub_api_token=hf_token,
-    )
-
-    deepseek = ChatHuggingFace(
-    llm=deep_seek_model,
-    repo_id="deepseek-ai/DeepSeek-R1",
-    # provider="nebius",
-    temperature=0.7,
-    max_new_tokens=100,
-    task="conversational"
-    )
-# Initialize session state for chat history
-    if "messages" not in st.session_state:
-        st.session_state.messages = [
-        SystemMessage(content="Answer like a 10 year experinced Python developer")
-    ]
-
-    def generate_response(user_input):
-    # Append user message
-        st.session_state.messages.append(HumanMessage(content=user_input))
-    # Invoke the model
-        response = deepseek.invoke(st.session_state.messages)
-    # Append AI response
-        st.session_state.messages.append(AIMessage(content=response))
-        return response
-
-# User input
-    user_input = st.text_input("Ask a question about Python:")
-
-    if user_input:
-        with st.spinner("Getting answer..."):
-            answer = generate_response(user_input)
-        st.markdown(f"**Answer:** {answer}")
-
-# Display chat history
-    if st.session_state.messages:
-        for msg in st.session_state.messages[1:]:  # skip initial SystemMessage
-            if isinstance(msg, HumanMessage):
-                st.markdown(f"**You:** {msg.content}")
-            elif isinstance(msg, AIMessage):
-                st.markdown(f"**Bot:** {msg.content}")
+#     hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN") or os.getenv("HF_TOKEN")
+#     if not hf_token:
+#         st.error("Please add your Hugging Face API token to Secrets (HUGGINGFACEHUB_API_TOKEN or HF_TOKEN).")
+#         st.stop()
+
+# # Setup the LangChain HuggingFaceEndpoint and ChatHuggingFace LLM
+#     deep_seek_model = HuggingFaceEndpoint(
+#     repo_id="deepseek-ai/DeepSeek-R1",
+#     # provider = 'nebius'
+#     temperature=0.7,
+#     max_new_tokens=100,
+#     task="conversational",
+#     huggingfacehub_api_token=hf_token,
+#     )
+
+#     deepseek = ChatHuggingFace(
+#     llm=deep_seek_model,
+#     repo_id="deepseek-ai/DeepSeek-R1",
+#     # provider="nebius",
+#     temperature=0.7,
+#     max_new_tokens=100,
+#     task="conversational"
+#     )
+# # Initialize session state for chat history
+#     if "messages" not in st.session_state:
+#         st.session_state.messages = [
+#         SystemMessage(content="Answer like a 10 year experinced Python developer")
+#     ]
+
+#     def generate_response(user_input):
+#     # Append user message
+#         st.session_state.messages.append(HumanMessage(content=user_input))
+#     # Invoke the model
+#         response = deepseek.invoke(st.session_state.messages)
+#     # Append AI response
+#         st.session_state.messages.append(AIMessage(content=response))
+#         return response
+
+# # User input
+#     user_input = st.text_input("Ask a question about Python:")
+
+#     if user_input:
+#         with st.spinner("Getting answer..."):
+#             answer = generate_response(user_input)
+#         st.markdown(f"**Answer:** {answer}")
+
+# # Display chat history
+#     if st.session_state.messages:
+#         for msg in st.session_state.messages[1:]:  # skip initial SystemMessage
+#             if isinstance(msg, HumanMessage):
+#                 st.markdown(f"**You:** {msg.content}")
+#             elif isinstance(msg, AIMessage):
+#                 st.markdown(f"**Bot:** {msg.content}")
     st.button("⬅️ Back to Home", on_click=lambda: switch_page("home"))
     # Here you can load your Python LLM and chat interface