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Create app.py
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app.py
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import streamlit as st
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import google.generativeai as genai
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import os
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from dotenv import load_dotenv
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# Load API key from .env file
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load_dotenv()
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genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
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# Initialize chat model
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model = genai.GenerativeModel("gemini-1.5-flash")
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# Streamlit UI
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st.title("🤖 AI Chatbot (Gemini 1.5 Flash)")
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# Add description
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st.markdown("""
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### About this Chatbot
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This is an AI-powered chatbot built using:
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* **Gemini 1.5 Flash** - Google's latest language model
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* **Streamlit** - For the interactive web interface
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* **Python** - For backend implementation
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The chatbot can help you with:
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- General questions and conversations
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- Writing and analysis tasks
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- Problem-solving and explanations
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""")
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st.write("Ask me anything!")
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# Store chat history
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if "messages" not in st.session_state:
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st.session_state.messages = []
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# Display previous messages
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for msg in st.session_state.messages:
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with st.chat_message(msg["role"]):
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st.markdown(msg["content"])
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# Get user input
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user_input = st.chat_input("Type your message...")
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if user_input:
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# Display user message
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st.chat_message("user").markdown(user_input)
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# Prepare chat history for context
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chat_history = [
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{"role": "user" if m["role"] == "user" else "model", "parts": [m["content"]]}
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for m in st.session_state.messages
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]
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# Call Gemini API
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response = model.generate_content(
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contents=[{"role": "user", "parts": [user_input]}],
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generation_config={"temperature": 0.7},
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safety_settings=[]
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)
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bot_reply = response.text
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# Display bot response
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st.chat_message("assistant").markdown(bot_reply)
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# Save conversation
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st.session_state.messages.append({"role": "user", "content": user_input})
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st.session_state.messages.append({"role": "assistant", "content": bot_reply})
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# Keep only last 3 message exchanges (6 messages total)
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if len(st.session_state.messages) > 6:
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st.session_state.messages = st.session_state.messages[-6:]
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