Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+from huggingface_hub import hf_hub_download
+import numpy as np
+import os
+
+MODEL_REPO = "zotthytt12/vegetable-classifier"  # <- zmień
+MODEL_FILENAME = "model/veg_model.h5"
+
+# pobierz model z Hugging Face Hub
+model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILENAME)
+model = tf.keras.models.load_model(model_path)
+
+# (opcjonalnie) nazwy klas
+CLASS_NAMES = ['Bean', 'Bitter_Gourd', 'Bottle_Gourd', 'Brinjal', 'Broccoli',
+               'Cabbage', 'Capsicum', 'Carrot', 'Cauliflower', 'Cucumber',
+               'Papaya', 'Potato', 'Pumpkin', 'Radish', 'Tomato']
+
+IMG_SIZE = (128, 128)
+
+def predict(img):
+    img = img.resize(IMG_SIZE)
+    x = image.img_to_array(img)
+    x = np.expand_dims(x, axis=0) / 255.0
+    preds = model.predict(x)
+    probs = preds[0]
+    return {CLASS_NAMES[i]: float(probs[i]) for i in range(len(CLASS_NAMES))}
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Vegetable Classifier",
+    description="Wgraj zdjęcie warzywa, a model powie co to jest."
+)
+
+if __name__ == "__main__":
+    iface.launch()