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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:]
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