Update app.py

app.py

@@ -10,38 +10,100 @@ ds_model = ChatHuggingFace(llm = deep_seek_model, repo_id = 'deepseek-ai/DeepSee
 
 st.title("ChatGenius Hub: Master Every Data Skills")
 
+
+# Set the default page in session state
+if "page" not in st.session_state:
+    st.session_state.page = "home"
+
+# Function to switch pages
+def switch_page(page_name):
+    st.session_state.page = page_name
+
+# Home page with buttons for different domains
+if st.session_state.page == "home":
+    st.title("🤖 Data Science Expert Bots")
+    st.markdown("Choose a domain to chat with an expert model:")
+
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        if st.button("Python 🐍"):
+            switch_page("python")
+        if st.button("Power BI 📊"):
+            switch_page("powerbi")
+
+    with col2:
+        if st.button("SQL 🛢️"):
+            switch_page("sql")
+        if st.button("Deep Learning 🧠"):
+            switch_page("deeplearning")
+
+    with col3:
+        if st.button("Machine Learning 🤖"):
+            switch_page("ml")
+        if st.button("Statistics 📈"):
+            switch_page("statistics")
+
+# Example domain-specific chatbot page
+elif st.session_state.page == "python":
+    st.title("Python Chatbot 🐍")
+    st.button("⬅️ Back to Home", on_click=lambda: switch_page("home"))
+    # Here you can load your Python LLM and chat interface
+
+elif st.session_state.page == "sql":
+    st.title("SQL Chatbot 🛢️")
+    st.button("⬅️ Back to Home", on_click=lambda: switch_page("home"))
+    # Load SQL chatbot here
+
+elif st.session_state.page == "powerbi":
+    st.title("Power BI Chatbot 📊")
+    st.button("⬅️ Back to Home", on_click=lambda: switch_page("home"))
+
+elif st.session_state.page == "ml":
+    st.title("Machine Learning Chatbot 🤖")
+    st.button("⬅️ Back to Home", on_click=lambda: switch_page("home"))
+
+elif st.session_state.page == "deeplearning":
+    st.title("Deep Learning Chatbot 🧠")
+    st.button("⬅️ Back to Home", on_click=lambda: switch_page("home"))
+
+elif st.session_state.page == "statistics":
+    st.title("Statistics Chatbot 📈")
+    st.button("⬅️ Back to Home", on_click=lambda: switch_page("home"))
+
+
 # message = [SystemMessage(content = "Answer like a data scientist"),
 #           HumanMessage(content = "Tell me about Support Vector")]
 # result =  ds_model.invoke(message)
 # print(result.content)
 
 
-if "messages" not in st.session_state:
-    st.session_state.messages = [
-        SystemMessage(content="Answer like a data scientist")
-    ]
-
-def generate_response(user_input):
-    # Append user message
-    st.session_state.messages.append(HumanMessage(content=user_input))
-    # Invoke the model
-    response = ds_model.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 Data Science:")
-
-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}")
+
+# if "messages" not in st.session_state:
+#     st.session_state.messages = [
+#         SystemMessage(content="Answer like a data scientist")
+#     ]
+
+# def generate_response(user_input):
+#     # Append user message
+#     st.session_state.messages.append(HumanMessage(content=user_input))
+#     # Invoke the model
+#     response = ds_model.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 Data Science:")
+
+# 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}")