import gradio as gr
import json
from predict_risks_ft import process_data
import ast  # fallback if json.loads fails (e.g., for single quotes)

def predict_from_json(json_input):
    try:
        if not json_input or not json_input.strip():
            return {"error": "No input provided."}

        try:
            # Try to parse as JSON
            input_data = json.loads(json_input)
        except json.JSONDecodeError:
            # If JSON fails, try Python literal (e.g., {'key': 'value'})
            input_data = ast.literal_eval(json_input)

        print("Parsed input:", input_data)

        results = process_data(input_data)

        if isinstance(results, (dict, list)):
            return results
        else:
            return {"error": "Invalid output format, expected dict or list."}

    except Exception as e:
        return {"error": f"Processing error: {str(e)}"}


# Create the Gradio interface
demo = gr.Interface(
    fn=predict_from_json,
    inputs=gr.Textbox(
        placeholder="Insert JSON here...", 
        label="Patient Data (JSON)"
    ),  # This allows user to input JSON data as text
    outputs=gr.JSON(label="Prediction Result"),  # Return result as JSON
    title="Health Risk Classifier",
    description="Insert patient medical data in JSON format to predict health risks.",
)

if __name__ == "__main__":
    demo.launch(share=True)  # Share=True to generate a public link