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 Hugging Face Hub
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", "face_classification_model1.h5")
model2 = load_model_from_hub("arsath-sm/face_classification_model2", "face_classification_model2.h5")

def preprocess_image(image):
    img = tf.image.resize(image, (224, 224))  # Resize to match the input size of your models
    img = tf.cast(img, tf.float32) / 255.0  # Normalize pixel values
    return tf.expand_dims(img, 0)  # Add batch dimension

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 (
        f"Model 1 (ResNet) Prediction: {result1} (Confidence: {confidence1:.2f})",
        f"Model 2 (Inception) Prediction: {result2} (Confidence: {confidence2:.2f})"
    )

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

# Launch the app
iface.launch()