final commit

app.py

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+import gradio as gr
+import tensorflow as tf
+from huggingface_hub import hf_hub_download
+from tensorflow.keras.preprocessing import image
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Step 1: Download the model from the Hugging Face Hub
+model_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/my_tensorflow_model", filename="my_model.h5")
+
+# Step 2: Load the TensorFlow model
+model = tf.keras.models.load_model(model_path)
+
+# Step 3: Function to preprocess the input image
+def load_and_preprocess_image(img, target_size=(256, 256)):
+    # Resize the image to the model's expected input size
+    img = img.resize(target_size)
+    
+    # Convert to array and normalize
+    img_array = np.array(img) / 255.0
+    
+    # Expand dimensions to match the input shape of the model
+    img_array = np.expand_dims(img_array, axis=0)
+    
+    return img_array
+
+# Step 4: Function to make predictions
+def predict_image(img):
+    # Preprocess the image
+    img_array = load_and_preprocess_image(img)
+    
+    # Make a prediction
+    prediction = model.predict(img_array)[0][0]
+    
+    # Confidence scores
+    real_confidence = prediction * 100
+    fake_confidence = (1 - prediction) * 100
+    
+    # Determine label
+    result_label = "Real" if real_confidence > fake_confidence else "Fake"
+    
+    # Return results as text and an explanation
+    result_text = f"The model predicts this image is '{result_label}' with {max(real_confidence, fake_confidence):.2f}% confidence."
+    explanation = f"Real Confidence: {real_confidence:.2f}% | Fake Confidence: {fake_confidence:.2f}%"
+    
+    return result_text, explanation
+
+# Step 5: Define the Gradio interface
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.inputs.Image(type="pil", label="Upload an Image"),
+    outputs=[
+        gr.outputs.Textbox(label="Prediction Result"),
+        gr.outputs.Textbox(label="Confidence Scores")
+    ],
+    title="Deepfake Image Detector",
+    description="Upload an image, and the model will classify whether it is a 'real' or 'fake' image using deep learning."
+)
+
+# Step 6: Launch the app
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

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+tensorflow
+gradio
+huggingface_hub