src/app.py

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

# Load environment variables
load_dotenv()

# Get API key
api_key = os.getenv("GEMINI_API_KEY")

# If API key is not in environment variables, try to get it from Streamlit secrets
if not api_key and "GEMINI_API_KEY" in st.secrets:
    api_key = st.secrets["GEMINI_API_KEY"]

if not api_key:
    st.error("⚠️ Gemini API key not found. Please ensure GEMINI_API_KEY is set in environment variables or Streamlit secrets.")
    st.stop()

try:
    genai.configure(api_key=api_key)
except Exception as e:
    st.error(f"⚠️ Error configuring Gemini API: {str(e)}")
    st.stop()

st.set_page_config(page_title="Gemini Stream Chat")
st.markdown("## 🚀 AI replica for [Takeoff](https://readyfortakeoff.app/)")
st.caption("Powered directly by `google.generativeai`")

SYSTEM_PROMPT = """
You are an AI chatbot built for Takeoff (https://readyfortakeoff.app), a portfolio-building platform designed for individuals and jobseekers.

Your role is to act as a helpful AI replica embedded in a user's portfolio. You can answer questions from recruiters and visitors about the user's work experience, projects, and skills. You should highlight relevant examples and provide helpful, professional, and concise responses.

You can reference data such as the user's resume, portfolio content, project notes, and achievements. Where appropriate, link to projects or suggest relevant content the user has created.

Your goal is to make it easy for others to understand the user's background and professional strengths.
"""
#SYSTEM_PROMPT = """
#"""
if "chat_history" not in st.session_state:
    st.session_state.chat_history = []

if st.session_state.chat_history:
    for msg in st.session_state.chat_history:
        with st.chat_message(msg["role"]):
            st.markdown(msg["parts"][0])

prompt = st.chat_input("Feel free to ask me anything...")

if prompt:
    with st.chat_message("user"):
        st.markdown(prompt)

    model = genai.GenerativeModel("gemini-1.5-flash")
    chat = model.start_chat(history=[
        {"role": "user", "parts": [SYSTEM_PROMPT]},
        {"role": "model", "parts": ["I understand. I will act as a helpful AI replica for Takeoff, focusing on providing professional and concise responses about the user's background and achievements."]},
        *[
            {"role": m["role"], "parts": [m["parts"][0]]}
            for m in st.session_state.chat_history
        ]
    ])

    with st.chat_message("ai"):
        full_response = ""
        response_container = st.empty()

        response_stream = chat.send_message(prompt, stream=True)
        for chunk in response_stream:
            full_response += chunk.text
            response_container.markdown(full_response + "▌")
        response_container.markdown(full_response)

    st.session_state.chat_history.append({"role": "user", "parts": [prompt]})
    st.session_state.chat_history.append({"role": "model", "parts": [full_response]})