app.py

import streamlit as st
import google.generativeai as genai
import os
from dotenv import load_dotenv

# Load API key from .env file
load_dotenv()
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

The chatbot can help you with:
- General questions and conversations
- Writing and analysis tasks
- Problem-solving and explanations
""")

st.write("Ask me anything!")

# Store chat history
if "messages" not in st.session_state:
    st.session_state.messages = []

# Display previous messages
for msg in st.session_state.messages:
    with st.chat_message(msg["role"]):
        st.markdown(msg["content"])

# Get user input
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
    response = model.generate_content(
        contents=[{"role": "user", "parts": [user_input]}],
        generation_config={"temperature": 0.7},
        safety_settings=[]
    )

    bot_reply = response.text

    # Display bot 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)
    if len(st.session_state.messages) > 6:
        st.session_state.messages = st.session_state.messages[-6:]