import gradio as gr
from tensorflow.keras.models import load_model
import numpy as np
from PIL import Image
from utils.preprocessing import preprocess_image

model = load_model("model/tumor_classifier.h5")

def predict_tumor(image, report):
    if image is None and not report.strip():
        return "❌ Please upload an image or enter a report."

    response = ""
    if image:
        img_array = preprocess_image(image)
        prediction = model.predict(img_array)[0][0]
        response += "🧠 Tumor detected." if prediction > 0.5 else "✅ No tumor detected."

    if report.strip():
        response += f"\n\n📋 Notes:\n{report.strip()}"

    return response

iface = gr.Interface(
    fn=predict_tumor,
    inputs=[
        gr.Image(type="pil", label="Upload MRI/CT Scan"),
        gr.Textbox(lines=3, label="Optional Medical Report")
    ],
    outputs="text",
    title="🧠 Brain Tumor Detection Assistant",
    description="Upload a scan and/or enter a report to detect brain tumor."
)

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