Update app.py

app.py

@@ -1,44 +1,55 @@
 import streamlit as st
-from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain.chains import ConversationChain
 from langchain.memory import ConversationBufferMemory
+from langchain_google_genai import ChatGoogleGenerativeAI
 
-# Sidebar for Google API key
-st.sidebar.title("⚙️ Settings")
-api_key = st.sidebar.text_input("Enter your Google API Key", type="password")
-
+# ---------------- Streamlit Page Config ----------------
+st.set_page_config(page_title="🤖 AI Conversational Data Science Tutor", layout="wide")
 st.title("🤖 AI Conversational Data Science Tutor")
 
-if api_key:
-    # Initialize Gemini model
+# ---------------- Sidebar for Settings ----------------
+st.sidebar.header("Settings")
+google_api_key = st.sidebar.text_input("Enter your Google API Key", type="password")
+
+# ---------------- Initialize Model ----------------
+if google_api_key:
     llm = ChatGoogleGenerativeAI(
-        model="gemini-1.5-flash",
-        google_api_key=api_key,
-        temperature=0.5,
+        model="gemini-1.5-pro",
+        google_api_key=google_api_key,
+        temperature=0.3,
     )
 
-    # Memory for conversation
+    # Conversation memory
     if "memory" not in st.session_state:
         st.session_state.memory = ConversationBufferMemory(return_messages=True)
 
-    # Build the chain
     conversation = ConversationChain(
         llm=llm,
         memory=st.session_state.memory,
-        verbose=False
+        verbose=True,
     )
 
-    # Chat input
-    user_input = st.chat_input("Ask a Data Science question...")
+    # ---------------- Chat Interface ----------------
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    user_input = st.chat_input("Ask your Data Science question...")
     if user_input:
+        # Store user message
+        st.session_state.messages.append({"role": "user", "content": user_input})
         with st.chat_message("user"):
-            st.write(user_input)
+            st.markdown(user_input)
+
+        # Get AI response with memory
+        response = conversation.predict(input=user_input)
 
+        st.session_state.messages.append({"role": "assistant", "content": response})
         with st.chat_message("assistant"):
-            response = conversation.predict(
-                input=f"You are a Data Science Tutor. Only answer Data Science questions. User asked: {user_input}"
-            )
-            st.write(response)
+            st.markdown(response)
 
 else:
-    st.warning("🔑 Please enter your Google API key in the sidebar to start.")
+    st.warning("Please enter your Google API Key in the sidebar to continue.")