Update app.py

app.py

@@ -10,30 +10,45 @@ genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
 # Initialize chat model
 model = genai.GenerativeModel("gemini-1.5-flash")
 
-# Streamlit UI
-st.title("🤖 AI Chatbot (Gemini 1.5 Flash)")
-
-# Add description
-st.markdown("""
-### About this Chatbot
-This is an AI-powered chatbot built using:
-* **Gemini 1.5 Flash** - Google's latest language model
-* **Streamlit** - For the interactive web interface
-* **Python** - For backend implementation
+# Streamlit UI Design
+st.set_page_config(page_title="AI Chatbot", page_icon="🤖", layout="centered")
 
-The chatbot can help you with:
-- General questions and conversations
-- Writing and analysis tasks
-- Problem-solving and explanations
-""")
+st.markdown(
+    """
+    <style>
+        body {
+            background-color: #f5f7fa;
+        }
+        .stChatMessage {
+            border-radius: 12px;
+            padding: 12px;
+        }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
 
-st.write("Ask me anything!")
+# Title and description
+st.title("🤖 AI Chatbot (Gemini 1.5 Flash)")
+st.markdown(
+    """
+    ### Welcome to Your AI Chatbot!
+    This chatbot is powered by **Google Gemini 1.5 Flash** and built with **Streamlit**.
+    
+    🧠 **What can it do?**
+    - Answer questions & provide explanations
+    - Help with writing & analysis tasks
+    - Assist with problem-solving
+    
+    🔥 Start chatting below!
+    """
+)
 
-# Store chat history
+# Initialize chat history
 if "messages" not in st.session_state:
     st.session_state.messages = []
 
-# Display previous messages
+# Display chat history
 for msg in st.session_state.messages:
     with st.chat_message(msg["role"]):
         st.markdown(msg["content"])
@@ -44,29 +59,25 @@ user_input = st.chat_input("Type your message...")
 if user_input:
     # Display user message
     st.chat_message("user").markdown(user_input)
-
-    # Prepare chat history for context
-    chat_history = [
-        {"role": "user" if m["role"] == "user" else "model", "parts": [m["content"]]}
-        for m in st.session_state.messages
-    ]
     
-    # Call Gemini API
+    # Format chat history for context
+    chat_history = [{"role": "user" if m["role"] == "user" else "model", "parts": [m["content"]]} for m in st.session_state.messages]
+    
+    # Generate AI response
     response = model.generate_content(
         contents=[{"role": "user", "parts": [user_input]}],
         generation_config={"temperature": 0.7},
         safety_settings=[]
     )
-
     bot_reply = response.text
-
-    # Display bot response
+    
+    # Display AI response
     st.chat_message("assistant").markdown(bot_reply)
-
+    
     # Save conversation
     st.session_state.messages.append({"role": "user", "content": user_input})
     st.session_state.messages.append({"role": "assistant", "content": bot_reply})
     
-    # Keep only last 3 message exchanges (6 messages total)
+    # Keep chat history concise
     if len(st.session_state.messages) > 6:
         st.session_state.messages = st.session_state.messages[-6:]