app.py

import gradio as gr
import numpy as np
from PIL import Image
import tensorflow as tf

# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="model.tflite")
interpreter.allocate_tensors()

# Get input and output details
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

class_names = ['Biceps Brachii', 'Gastrocnemius Medialis', 'Healthy', 'Tibialis Anterior']
CONFIDENCE_THRESHOLD = 0.7

def predict_image(image):
    try:
        image = image.resize((128, 128)).convert("RGB")
        img_array = np.array(image, dtype=np.float32) / 255.0
        img_array = np.expand_dims(img_array, axis=0)

        interpreter.set_tensor(input_details[0]['index'], img_array)
        interpreter.invoke()
        output = interpreter.get_tensor(output_details[0]['index'])

        class_idx = int(np.argmax(output))
        confidence = float(np.max(output))

        if confidence < CONFIDENCE_THRESHOLD:
            return f"⚠️ Low confidence ({confidence:.2f}). The model is unsure. Please try a clearer image."
        else:
            return f"✅ Prediction: {class_names[class_idx]} (Confidence: {confidence:.2f})"
    except Exception as e:
        return f"Error: {str(e)}"

gr.Interface(
    fn=predict_image,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="Muscle Disease Detection",
    description="Upload an MRI image to detect muscle conditions."
).launch()