Create app.py

app.py

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+
+import gradio as gr
+import numpy as np
+import cv2
+from PIL import Image
+import tensorflow as tf
+from tensorflow.keras.applications import MobileNetV2
+from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions
+import json
+
+# Load MobileNetV2 model
+model = MobileNetV2(weights='imagenet')
+
+def add_hsv_noise(image, hue_noise=0, saturation_noise=0, value_noise=0):
+    """Add HSV noise to an image"""
+    if image is None:
+        return None
+
+    # Convert PIL to numpy array
+    img_array = np.array(image)
+
+    # Convert RGB to HSV
+    hsv = cv2.cvtColor(img_array, cv2.COLOR_RGB2HSV).astype(np.float32)
+
+    # Add noise to each channel
+    hsv[:, :, 0] = np.clip(hsv[:, :, 0] + hue_noise, 0, 179)  # Hue: 0-179
+    hsv[:, :, 1] = np.clip(hsv[:, :, 1] + saturation_noise, 0, 255)  # Saturation: 0-255
+    hsv[:, :, 2] = np.clip(hsv[:, :, 2] + value_noise, 0, 255)  # Value: 0-255
+
+    # Convert back to RGB
+    rgb = cv2.cvtColor(hsv.astype(np.uint8), cv2.COLOR_HSV2RGB)
+
+    return Image.fromarray(rgb)
+
+def predict_image(image, top_n, hue_noise, saturation_noise, value_noise):
+    """Predict image classes with noise applied"""
+    if image is None:
+        return None, "Please upload an image first."
+
+    # Apply HSV noise
+    noisy_image = add_hsv_noise(image, hue_noise, saturation_noise, value_noise)
+
+    # Preprocess for MobileNet
+    img_resized = noisy_image.resize((224, 224))
+    img_array = np.array(img_resized)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = preprocess_input(img_array)
+
+    # Make prediction
+    predictions = model.predict(img_array)
+    decoded_predictions = decode_predictions(predictions, top=top_n)[0]
+
+    # Format results
+    results = []
+    for i, (class_id, class_name, probability) in enumerate(decoded_predictions):
+        results.append(f"{i+1}. {class_name}: {probability:.4f} ({probability*100:.2f}%)")
+
+    results_text = "\n".join(results)
+
+    return noisy_image, results_text
+
+# Create Gradio interface
+with gr.Blocks(title="MobileNet HSV Noise Analysis") as demo:
+    gr.Markdown("# MobileNet Classification with HSV Noise")
+    gr.Markdown("Upload an image and adjust HSV noise sliders to see how it affects MobileNet predictions.")
+
+    with gr.Row():
+        with gr.Column():
+            # Input controls
+            input_image = gr.Image(type="pil", label="Upload Image")
+            top_n = gr.Slider(minimum=1, maximum=10, value=5, step=1, label="Top N Classes")
+
+            gr.Markdown("### HSV Noise Controls")
+            hue_noise = gr.Slider(minimum=-50, maximum=50, value=0, step=1, label="Hue Noise (-50 to 50)")
+            saturation_noise = gr.Slider(minimum=-100, maximum=100, value=0, step=5, label="Saturation Noise (-100 to 100)")
+            value_noise = gr.Slider(minimum=-100, maximum=100, value=0, step=5, label="Value/Brightness Noise (-100 to 100)")
+
+        with gr.Column():
+            # Output displays
+            output_image = gr.Image(label="Image with Noise Applied")
+            predictions_text = gr.Textbox(label="Top Predictions", lines=10, max_lines=15)
+
+    # Set up real-time updates
+    inputs = [input_image, top_n, hue_noise, saturation_noise, value_noise]
+    outputs = [output_image, predictions_text]
+
+    # Update predictions when any input changes
+    for input_component in inputs:
+        input_component.change(
+            fn=predict_image,
+            inputs=inputs,
+            outputs=outputs
+        )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch(share=True, debug=True)