Update app.py

app.py

@@ -5,21 +5,23 @@ from huggingface_hub import hf_hub_download
 import numpy as np
 import os
 
-MODEL_REPO = "zotthytt12/vegetable-classifier"  # <- zmień
+MODEL_REPO = "zotthytt12/vegetable-classifier"
 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):
+def predict(img_path):
+    # img_path to ścieżka do pliku, który Gradio stworzył
+    from PIL import Image
+    img = Image.open(img_path)
     img = img.resize(IMG_SIZE)
     x = image.img_to_array(img)
     x = np.expand_dims(x, axis=0) / 255.0
@@ -29,11 +31,11 @@ def predict(img):
 
 iface = gr.Interface(
     fn=predict,
-    inputs=gr.Image(type="pil"),
+    inputs=gr.Image(type="filepath"),   # zmienione z "pil" na "filepath"
     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(show_error=True)
+    iface.launch(show_error=True)