app.py

import os
import gradio as gr
from langchain_google_genai.chat_models import ChatGoogleGenerativeAI

# Set the path to the service account key
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "./firm-catalyst-437006-s4-407500537db5.json"

# Initialize the language model with required parameters
# Replace 'your-model-name' with the actual model you intend to use
llm = ChatGoogleGenerativeAI(model='gemini-1.5-pro')  

# Chat function
def chat_with_gemini(user_input, chat_history):
    try:
        # Append the user input to the chat history
        chat_history.append(("User", user_input))
        
        # Get response from the model
        response = llm.predict(user_input)
        
        # Append the bot's response to the chat history
        chat_history.append(("Bot", response))

        # Return the updated chat history
        return chat_history
    except Exception as e:
        # In case of an error, return the error message in the chat
        chat_history.append(("Bot", f"Error: {str(e)}"))
        return chat_history

# Create a Gradio interface
with gr.Blocks() as iface:
    gr.Markdown("# Ken Chatbot")
    gr.Markdown("Ask me anything!")
    
    chatbot = gr.Chatbot()  # Initialize the chatbot
    msg = gr.Textbox(label="Type your message here...", placeholder="Enter your message and press enter to send your message...")  # Text input for user messages
    state = gr.State([])  # Store chat history

    # Set up the interaction for when the user submits a message
    msg.submit(chat_with_gemini, [msg, state], [chatbot])  # Update chatbot with new messages
    msg.submit(lambda: "", None, msg)  # Clear the input box after submission

# Launch the interface with debugging enabled
iface.launch(debug=True)