Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Cargar modelo desde Hugging Face
+model = tf.keras.models.load_model("https://huggingface.co/shaktibiplab/Animal-Classification/resolve/main/animal_model.h5")
+
+labels = ["cat", "dog", "elephant", "horse", "lion", "tiger"]
+
+def predict(image):
+    image = image.resize((256, 256))
+    img_array = np.array(image) / 255.0
+    img_array = img_array.reshape(1, 256, 256, 3)
+    prediction = model.predict(img_array)[0]
+    index = np.argmax(prediction)
+    label = labels[index]
+    confidence = float(prediction[index])
+    return f"{label} ({round(confidence * 100, 2)}%)"
+
+iface = gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs="text")
+iface.launch()