add code

src/streamlit_app.py

@@ -1,40 +1,56 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+import os
 import streamlit as st
+import google.generativeai as genai
+from dotenv import load_dotenv
 
-"""
-# Welcome to Streamlit!
+load_dotenv()
+genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
+
+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.
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+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.
 
-In the meantime, below is an example of what you can do with just a few lines of code:
+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.
 """
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+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": m["role"], "parts": [m["parts"][0]]}
+        for m in st.session_state.chat_history
+    ])
+
+    full_prompt = f"{SYSTEM_PROMPT}\n\nUser: {prompt}"
+
+    with st.chat_message("ai"):
+        full_response = ""
+        response_container = st.empty()
+
+        response_stream = chat.send_message(full_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]})