import os
import kaggle
import google.generativeai as gemini
import gradio as gr

# Ensure that Kaggle API is authenticated correctly
try:
    kaggle.api.authenticate()
except Exception as e:
    print(f"Kaggle API authentication failed: {str(e)}")

# Ensure that the Google Gemini API key is set properly
gemini_api_key = os.getenv("GEMINI_API_KEY")
if not gemini_api_key:
    print("Error: GEMINI_API_KEY environment variable is not set.")
else:
    gemini.configure(api_key=gemini_api_key)

# Function to handle the chat interaction
def gradio_chat(usertxt, chat_history):
    try:
        # Initialize chat session with previous history
        chat = gemini.ChatModel.start_chat(history=chat_history)
        # Send user message to the Gemini model
        response = chat.send_message(usertxt)
        # Append user and assistant's responses to the chat history
        chat_history.append({"role": "user", "content": usertxt})
        chat_history.append({"role": "assistant", "content": response.text})
        return chat_history, chat_history
    except Exception as e:
        error_message = f"Error occurred: {str(e)}"
        chat_history.append({"role": "assistant", "content": error_message})
        return chat_history, chat_history

# HTML content for Gradio interface (you can customize this as needed)
html_content = """
<h1>Food Safety Inspection Hub Prototype</h1>
<p>Chat with our AI-powered assistant to report food safety concerns and interact with authorities.</p>
"""

# Define the Gradio interface
with gr.Blocks() as demo:
    gr.HTML(html_content)
    chatbot = gr.Chatbot()
    user_input = gr.Textbox(placeholder="Enter your message here...")
    chat_history = gr.State([])
    submit_btn = gr.Button("Submit")

    # When submit button is clicked, trigger the chat function
    submit_btn.click(gradio_chat, inputs=[user_input, chat_history], outputs=[chatbot, chat_history])

# Launch the Gradio interface
demo.launch()