Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+from huggingface_hub import from_pretrained_keras
+
+# Load the models
+model1 = from_pretrained_keras("arsath-sm/face_classification_model1")
+model2 = from_pretrained_keras("arsath-sm/face_classification_model2")
+
+# Preprocess the image
+def preprocess_image(img):
+    img = image.img_to_array(img)
+    img = np.expand_dims(img, axis=0)
+    img = img / 255.0
+    return img
+
+# Make predictions
+def predict(img):
+    preprocessed_img = preprocess_image(img)
+    
+    prediction1 = model1.predict(preprocessed_img)[0][0]
+    prediction2 = model2.predict(preprocessed_img)[0][0]
+    
+    result1 = "Real" if prediction1 > 0.5 else "Fake"
+    result2 = "Real" if prediction2 > 0.5 else "Fake"
+    
+    confidence1 = prediction1 if result1 == "Real" else 1 - prediction1
+    confidence2 = prediction2 if result2 == "Real" else 1 - prediction2
+    
+    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,
+    inputs=gr.Image(type="pil"),
+    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()