main.py

import os
import streamlit as st
from dotenv import load_dotenv
import google.generativeai as gen_ai
import base64

# Load environment variables
load_dotenv()



# Function to convert image to Base64
def image_to_base64(image_path):
    with open('shutterstock_1154457493.jpg', "rb") as image_file:
        encoded_string = base64.b64encode(image_file.read()).decode("utf-8")
    return encoded_string

# Set your device photo path
photo_path = 'shutterstock_1154457493.jpg'

# Convert image to Base64
encoded_icon = image_to_base64(photo_path)

# Configure Streamlit page settings
st.set_page_config(
    page_title="GeminiChatBot",
    page_icon=f"data:image/png;base64,{encoded_icon}",  # Favicon emoji
    layout="centered",  # Page layout option
)

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")

# Set up Google Gemini-Pro AI model
gen_ai.configure(api_key=GOOGLE_API_KEY)
model = gen_ai.GenerativeModel('gemini-pro')

# Function to translate roles between Gemini-Pro and Streamlit terminology
def translate_role_for_streamlit(user_role):
    if user_role == "model":
        return "assistant"
    else:
        return user_role

# Initialize chat session in Streamlit if not already present
if "chat_session" not in st.session_state:
    st.session_state.chat_session = model.start_chat(history=[])

# Display the chatbot's title on the page
st.title("Gemini ChatBot")

# Display the chat history
for message in st.session_state.chat_session.history:
    with st.chat_message(translate_role_for_streamlit(message.role)):
        st.markdown(message.parts[0].text)

# Input field for user's message
user_prompt = st.chat_input("Ask Gemini-Pro...")
if user_prompt:
    # Add user's message to chat and display it
    st.chat_message("user").markdown(user_prompt)

    # Send user's message to Gemini-Pro and get the response
    gemini_response = st.session_state.chat_session.send_message(user_prompt)

    # Display Gemini-Pro's response
    with st.chat_message("assistant"):
        st.markdown(gemini_response.text)