import gradio as gr
import requests
import json

import os

# WARNING: It is not recommended to hardcode sensitive data like API tokens in code.
# Consider using environment variables or other secure methods for production applications.
API_URL = "https://deployment.datasaur.ai/api/deployment/8/2717/chat/completions"
API_TOKEN = os.environ["DATASAUR_API_KEY"]

def magic_function(input_text):
    """
    Sends text to the Datasaur deployment API and returns the processed text.
    """
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {API_TOKEN}",
    }
    data = {
        "messages": [{"role": "user", "content": input_text}]
    }
    
    try:
        response = requests.post(API_URL, headers=headers, json=data)
        response.raise_for_status()  # Raise an exception for bad status codes (4xx or 5xx)
        
        response_json = response.json()
        
        # Extract content from a standard chat completion response structure.
        # This may need adjustment if the API has a different format.
        content = response_json.get("choices", [{}])[0].get("message", {}).get("content", "Error: Could not parse response.")
        return content
        
    except requests.exceptions.RequestException as e:
        return f"API Request Error: {e}"
    except (ValueError, KeyError, IndexError):
        # Handle cases where response is not valid JSON or structure is unexpected
        return f"Error processing API response: {response.text}"


with gr.Blocks() as demo:
    gr.Markdown("# Memo Improvement Workflow")
    with gr.Row():
        text_area = gr.Textbox(label="Your Text", lines=20, scale=4)
        with gr.Column(scale=1):
            magic_button = gr.Button("Magic Button")

    magic_button.click(
        fn=magic_function,
        inputs=text_area,
        outputs=text_area
    )

if __name__ == "__main__":
    demo.launch()