app.py

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

# --- Config ---
MODEL_PATH = "./model/best_model.h5"
LABELS = ["class1", "class2", "class3", "class4"]  # ← update to your labels
IMG_SIZE = 512

# --- Load model ---
model = tf.keras.models.load_model(MODEL_PATH)

# --- Preprocessing ---
def preprocess(image: Image.Image):
    img = image.resize((IMG_SIZE, IMG_SIZE)).convert("RGB")
    arr = np.array(img)
    arr = np.expand_dims(arr, 0)
    return tf.keras.applications.mobilenet_v3.preprocess_input(arr)

# --- Prediction function ---
def predict(image):
    """
    Gradio accepts PIL image. Returns (label, confidence).
    """
    arr = preprocess(image)
    preds = model.predict(arr)[0]
    idx = int(np.argmax(preds))
    label = LABELS[idx] if idx < len(LABELS) else "Unknown"
    confidence = float(preds[idx])
    return {label: confidence}

# --- Launch Gradio Interface ---
iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(num_top_classes=1),
    title="My TF Classifier",
    description="Upload an image and get back its class and confidence."
)

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