app.py

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

# Function to load model from H5 file
def load_model_from_hub(repo_id, filename):
    model_path = hf_hub_download(repo_id=repo_id, filename=filename)
    return tf.keras.models.load_model(model_path)

# Load models from Hugging Face Hub
model1 = load_model_from_hub("arsath-sm/face_classification_model1", "model.h5")
model2 = load_model_from_hub("arsath-sm/face_classification_model2", "model.h5")

def preprocess_image(image):
    img = tf.convert_to_tensor(image)
    img = tf.image.resize(img, (150, 150))
    img = img / 255.0
    return tf.expand_dims(img, 0)

def predict_image(image):
    preprocessed_image = preprocess_image(image)
    
    # Make predictions using both models
    pred1 = model1.predict(preprocessed_image)[0][0]
    pred2 = model2.predict(preprocessed_image)[0][0]
    
    # Prepare results for each model
    result1 = "Real" if pred1 > 0.5 else "Fake"
    confidence1 = pred1 if pred1 > 0.5 else 1 - pred1
    result2 = "Real" if pred2 > 0.5 else "Fake"
    confidence2 = pred2 if pred2 > 0.5 else 1 - pred2
    
    return {
        "Model 1 Prediction": f"{result1} (Confidence: {confidence1:.2f})",
        "Model 2 Prediction": f"{result2} (Confidence: {confidence2:.2f})"
    }

# Create the Gradio interface
iface = gr.Interface(
    fn=predict_image,
    inputs=gr.Image(),
    outputs={
        "Model 1 Prediction": gr.Textbox(),
        "Model 2 Prediction": gr.Textbox()
    },
    title="Real vs AI Face Classification",
    description="Upload an image to classify whether it's a real face or an AI-generated face using two different models."
)

# Launch the app
iface.launch()