Create app.py

app.py

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+import gradio as gr
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+
+# Load the model
+model = load_model('./model.h5')
+
+def detect_image(input_image):
+    img = Image.fromarray(input_image).resize((256, 256))
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    prediction = model.predict(img_array)[0][0]
+    probability_real = prediction * 100
+    probability_ai = (1 - prediction) * 100
+
+    if probability_real > probability_ai:
+        result = 'Input Image is Real'
+        confidence = probability_real
+    else:
+        result = 'Input Image is AI Generated'
+        confidence = probability_ai
+
+    return result, confidence
+
+demo = gr.Interface(
+    fn=detect_image,
+    inputs=gr.Image(type="numpy", shape=(256, 256)),
+    outputs=[gr.Textbox(label="Result"), gr.Textbox(label="Confidence (%)")],
+    title="Deepfake Detection",
+    description="Upload an image to detect if it's real or AI generated."
+)
+
+# Deploy the interface on Gradio Hub
+demo.launch(share=True)