app.py

import os
import time
import uuid
import sqlite3
from typing import List, Tuple, Optional, Dict, Union
from PIL import Image
from io import BytesIO

import google.generativeai as genai
import streamlit as st

# Database setup
conn = sqlite3.connect('chat_history.db') 
c = conn.cursor()

c.execute('''
       CREATE TABLE IF NOT EXISTS history 
       (role TEXT, message TEXT)
       ''')

# Generative AI setup
api_key = "AIzaSyC70u1sN87IkoxOoIj4XCAPw97ae2LZwNM" 
genai.configure(api_key=api_key)

generation_config = {
 "temperature": 0.9,
 "max_output_tokens": 3000 
}

safety_settings = []

# Streamlit UI
st.set_page_config(page_title="Chatbot", page_icon="🤖")

# Header with logo
st.markdown("""
<style>
.container {
 display: flex;
}
.logo-text {
 font-weight:700 !important;
 font-size:50px !important;
 color: #f9a01b !important;
 padding-top: 75px !important;
}
.logo-img {
 float:right;
}
</style>
<div class="container">
 <p class="logo-text">Chatbot</p>
 <img class="logo-img" src="https://media.roboflow.com/spaces/gemini-icon.png" width=120 height=120>
</div>
""", unsafe_allow_html=True)

# Sidebar for parameters and model selection
st.sidebar.title("Parameters")
temperature = st.sidebar.slider(
 "Temperature",
 min_value=0.0,
 max_value=1.0,
 value=0.9,
 step=0.01,
 help="Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that expect a true or correct response, while higher temperatures can lead to more diverse or unexpected results."
)
max_output_tokens = st.sidebar.slider(
 "Token limit",
 min_value=1,
 max_value=2048,
 value=3000,
 step=1,
 help="Token limit determines the maximum amount of text output from one prompt. A token is approximately four characters. The default value is 2048."
)
st.sidebar.title("Model")
model_name = st.sidebar.selectbox(
 "Select a model",
 options=["gemini-pro", "gemini-pro-vision"],
 index=0,
 help="Gemini Pro is a text-only model that can generate natural language responses based on the chat history. Gemini Pro Vision is a multimodal model that can generate natural language responses based on the chat history and the uploaded images."
)

# Initialize user_input in session state
if "user_input" not in st.session_state:
    st.session_state["user_input"] = ""

# Chat history
st.title("Chatbot")
if "chat_history" not in st.session_state:
    st.session_state["chat_history"] = [] 

for message in st.session_state["chat_history"]:
    r, t = message["role"], message["parts"][0]["text"]
    st.markdown(f"**{r.title()}:** {t}")

# User input
user_input = st.text_area("", height=5, key="user_input") 

# File uploader
uploaded_files = st.file_uploader("Upload images here or paste screenshots", type=["png", "jpg", "jpeg"], accept_multiple_files=True, key="uploaded_files")

# If files are uploaded, open and display them
if uploaded_files:
    for uploaded_file in uploaded_files:
        image = Image.open(uploaded_file)
        st.image(image)

# Run button
run_button = st.button("Run", key="run_button")

# Clear button
clear_button = st.button("Clear", key="clear_button")

# Download button
download_button = st.button("Download", key="download_button")

# Progress bar
progress_bar = st.progress(0)

# Footer
st.markdown("""
<style>
.footer {
 position: fixed;
 left: 0;
 bottom: 0;
 width: 100%;
 background-color: #f9a01b;
 color: white;
 text-align: center;
}
</style>
<div class="footer">
 <p>Made with Streamlit and Google Generative AI</p>
</div>
""", unsafe_allow_html=True)

# Clear chat history and image uploader
if clear_button:
    st.session_state["chat_history"] = []
# Update progress bar
progress_bar.progress(1)

# Generate model response
if run_button:
    if model_name == "gemini-pro":
        response = genai.generate(
            prompt=st.session_state["user_input"],
            max_tokens=max_output_tokens,
            temperature=temperature,
            safety_settings=safety_settings
        )
    elif model_name == "gemini-pro-vision":
        images = [Image.open(file).convert('RGB') for file in uploaded_files]
        response = genai.generate(
            prompt=st.session_state["user_input"],
            max_tokens=max_output_tokens,
            temperature=temperature,
            safety_settings=safety_settings,
            images=images
        )

    # Add model response to chat history
    st.session_state["chat_history"].append({"role": "model", "parts": [{"text": response}]})

    # Save chat history to database
    c.execute("INSERT INTO history VALUES (?, ?)", (role, message))
    conn.commit()

    # Clear user input
    st.session_state["user_input"] = ""

    # Rerun the app
    st.experimental_rerun()

# Save chat history to a text file
if download_button:
    chat_text = "\n".join([f"{r.title()}: {t}" for r, t in st.session_state["chat_history"]])
    st.download_button(
        label="Download chat history",
        data=chat_text,
        file_name="chat_history.txt",
        mime="text/plain"
    )