app.py

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

# Load the TensorFlow SavedModel
model = tf.saved_model.load("saved_model")

# Grab the callable signature (first one usually works)
infer = list(model.signatures.values())[0]

labels = ["cool", "neutral", "warm"]

def predict(image):
    image = image.resize((224, 224))
    img_array = np.array(image) / 255.0
    img_array = np.expand_dims(img_array, axis=0).astype(np.float32)

    output = infer(tf.constant(img_array))
    preds = list(output.values())[0].numpy()

    label = labels[np.argmax(preds)]
    confidence = float(np.max(preds))
    return f"Predicted Undertone: {label.capitalize()} ({confidence*100:.2f}%)"

gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", label="Upload your wrist photo"),
    outputs="text",
    title="Wrist Undertone Detector 🩵🩷💛",
    description="Upload a wrist photo to detect your undertone: Cool, Neutral, or Warm.",
    allow_flagging="never"
).launch()