Initial Commit

app..py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+
+# 1. Load your model
+model = tf.keras.models.load_model("face_forgery_detector.keras")
+
+# 2. Define your inference function
+def detect_forgery(image):
+    # Preprocess the image to match your model’s input requirements
+    img = tf.image.resize(image, (160, 160))  # Example size; adjust for your model
+    img = tf.expand_dims(img, axis=0)
+    img = img / 255.0  # Example normalization; adapt as needed
+
+    # Run inference
+    predictions = model.predict(img)[0]  # e.g., [Real_prob, Fake_prob]
+    # Suppose predictions = [prob_real, prob_fake]
+    prob_real = predictions[0]
+    prob_fake = predictions[1]
+
+    # Format output
+    # You can return a dictionary or a string. For example:
+    if prob_fake > prob_real:
+        return f"Forged (Fake) with confidence {prob_fake:.2f}"
+    else:
+        return f"Real with confidence {prob_real:.2f}"
+
+# 3. Build your Gradio interface
+demo = gr.Interface(
+    fn=detect_forgery,
+    inputs=gr.Image(type="numpy"),  # 'type="numpy"' gives a NumPy array
+    outputs="text",
+    title="Face Forgery Detector",
+    description="Upload a face image to check if it's likely forged or real."
+)
+
+# 4. Launch the app (Gradio handles the rest)
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+tensorflow==2.11.0
+gradio==3.25.0