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

# Load model
model = load_model("trained_image_model.h5")
class_labels = ['original', 'screen_photo', 'screenshots']

# Prediction function
def predict_image(img: Image.Image):
    img = img.resize((224, 224))
    img_array = image.img_to_array(img) / 255.0
    img_array = np.expand_dims(img_array, axis=0)
    prediction = model.predict(img_array)
    predicted_class = np.argmax(prediction, axis=1)[0]
    return class_labels[predicted_class]

# Gradio UI
interface = gr.Interface(
    fn=predict_image,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="📸 Image Type Classifier",
    description="Upload an image to classify it as: Original Photo, Screen Photo, or Screenshot."
)

interface.launch()