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import os
import streamlit as st
#from dotenv import load_dotenv
#import google.generativeai as gen_ai
# Load environment variables
load_dotenv()
# Configure Streamlit page settings
st.set_page_config(
page_title="ML Galaxy!",
page_icon=":brain:", # Favicon emoji
layout="centered", # Page layout option
)
# Retrieve the Google API key from the environment
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
# Check if the API key is loaded
if not GOOGLE_API_KEY:
st.error("API key not found! Please set the GOOGLE_API_KEY in your .env file.")
st.stop()
# Configure the Generative AI model
try:
gen_ai.configure(api_key=GOOGLE_API_KEY)
model = gen_ai.GenerativeModel("gemini-pro")
except Exception as e:
st.error(f"Error initializing the Gemini-Pro model: {e}")
st.stop()
# Function to translate roles between Gemini-Pro and Streamlit terminology
def translate_role_for_streamlit(user_role):
return "assistant" if user_role == "model" else user_role
# Initialize the chat session if not already present in session state
if "chat_session" not in st.session_state:
try:
st.session_state.chat_session = model.start_chat(history=[])
except Exception as e:
st.error(f"Error initializing chat session: {e}")
st.stop()
# Display the chatbot's title
st.title("🤖 ML Galaxy")
# Display the chat history
try:
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)
except Exception as e:
st.error(f"Error displaying chat history: {e}")
# Input field for user's message
user_prompt = st.chat_input("Ask Gemini-Pro...")
if user_prompt:
# Add the user's message to the chat and display it
st.chat_message("user").markdown(user_prompt)
# Send the user's message to Gemini-Pro and get the response
try:
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)
except Exception as e:
st.error(f"Error processing your message: {e}")
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