Update app.py

app.py

@@ -72,36 +72,53 @@ elif st.session_state.page == "python":
 #     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}")
+
+
+    gemma_model = HuggingFaceEndpoint(
+    repo_id="google/gemma-3-27b-it",
+    temperature=0.7,
+    max_new_tokens=512,
+    task="conversational",
+    huggingfacehub_api_token=hf_token,
+    )
+
+    chat_gemma = ChatHuggingFace(
+    llm=gemma_model,
+    repo_id="google/gemma-3-27b-it",
+    temperature=0.7,
+    max_new_tokens=512,
+    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
 
@@ -135,7 +152,7 @@ elif st.session_state.page == "sql":
 
 # Initialize chat history
     if "messages" not in st.session_state:
-        st.session_state.messages = [SystemMessage(content="Answer clearly like a technical SQL interviewer.")]
+        st.session_state.messages = [SystemMessage(content="Answer clearly like a technical 10 year experienced person in SQL .")]
 
 # User input
     user_input = st.text_input("💡 Ask your SQL interview question:", placeholder="e.g., give me 10 SQL interview questions with answers")