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VGG.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:121b148fe3b53d5be7c04bff32abfcbb58ecab958aadedcc7952c7444014000a
+size 62908016

app.py

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+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import tensorflow as tf
+
+# Load the trained model
+model = load_model("VGG.h5")
+
+# Define class names (order from your dataset's subfolders)
+class_names = ['cat', 'dog', 'wild']  # Change if your folder names differ
+
+IMG_SIZE = 224
+
+def predict(img):
+    # Preprocess the image
+    img = img.resize((IMG_SIZE, IMG_SIZE))
+    img_array = image.img_to_array(img)
+    img_array = img_array / 255.0  # Rescale
+    img_array = np.expand_dims(img_array, axis=0)
+
+    # Predict
+    preds = model.predict(img_array)[0]
+    result = {class_names[i]: float(preds[i]) for i in range(len(class_names))}
+    return result
+
+# Build Gradio Interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Animal Face Classifier (VGG16)",
+    description="Upload an image of an animal face (cat, dog, or wild) and get the predicted class probabilities."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+tensorflow
+numpy
+Pillow