app.py

# --- File: app.py ---

import gradio as gr
import tensorflow as tf
import numpy as np
from huggingface_hub import hf_hub_download

# --- Configuration ---
# The Gradio app will download the model from this same repository
REPO_ID = "VimalJohnMV/Wrinklum-Revealus"
MODEL_FILENAME = "wrinkle_evaluator_model.h5"
IMG_HEIGHT = 256
IMG_WIDTH = 256

# --- 1. Load the model from Hugging Face Hub ---
try:
    print("Downloading model from Hugging Face Hub...")
    model_path = hf_hub_download(repo_id=REPO_ID, filename=MODEL_FILENAME)
    model = tf.keras.models.load_model(model_path)
    print("Model loaded successfully.")
except Exception as e:
    print(f"Error loading model: {e}")
    model = None # Handle case where model fails to load

# --- 2. Define the prediction function ---
def evaluate_wrinkles_and_comment(input_image):
    if model is None:
        return "Error: Model could not be loaded."

    if input_image is None:
        return "Please upload an image."

    # Preprocess the image using TensorFlow's native functions for better compatibility
    img_tensor = tf.convert_to_tensor(input_image, dtype=tf.float32)
    img_resized = tf.image.resize(img_tensor, (IMG_HEIGHT, IMG_WIDTH))
    img_normalized = img_resized / 255.0
    img_array = np.expand_dims(img_normalized, axis=0)

    # Make a prediction
    prediction = model.predict(img_array)[0][0]

    # Generate comment based on the prediction
    if prediction < 0.2:
        comment = "WOW, A perfectly ironed shirt! How did you get it to look so......BORING!!!!"
    elif prediction < 0.5:
        comment = "It's nice to see an outfit that hasn't completely given up on life yet"
    elif prediction < 0.8:
        comment = "I see you've gone for the 'woke up in a duffel bag' look. A classic."
    else:
        comment = "I didn't know your shirt was a participant in a very aggressive game of origami. 😅"

    return f"Wrinkle Score (0.0-1.0): {prediction:.2f}\n\nComment: {comment}"

# --- 3. Create the Gradio Interface ---
iface = gr.Interface(
    fn=evaluate_wrinkles_and_comment,
    inputs=gr.Image(type="numpy", label="Upload a Dress Photo"),
    outputs=gr.Textbox(label="Evaluation Result", lines=4),
    title="👗Wrinklum-Revealus",
    description="Upload a photo of a dress, and this AI will give it a wrinkle score and a comment."
)

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