Create app.py

app.py

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+import tensorflow as tf
+import numpy as np
+import gradio as gr
+
+# Load the model
+model = tf.keras.models.load_model("vgg19_binary_nonbinary.h5")
+
+# Define the preprocessing function
+def preprocess_image(image):
+    image = image.resize((224, 224))  # Resize to match model input size
+    image = np.array(image) / 255.0   # Normalize pixel values
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    return image
+
+# Define the prediction function
+def classify_image(image):
+    processed_image = preprocess_image(image)
+    predictions = model.predict(processed_image)
+    class_names = ["binary", "non-binary"]
+    confidence = {class_names[i]: float(predictions[0][i]) for i in range(2)}
+    return confidence
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),  # Input is an image
+    outputs=gr.Label(num_top_classes=2),  # Output is a label with confidence scores
+    title="Binary vs Non-Binary Image Classification",
+    description="Upload an image to classify it as 'binary' or 'non-binary'.",
+)
+
+# Launch the app
+interface.launch()