Update app.py

app.py

@@ -1,22 +1,11 @@
 import gradio as gr
 import requests
-from transformers import AutoModelForImageClassification, AutoFeatureExtractor
+from transformers import pipeline
 from PIL import Image
-import torch
 
-# Menginisialisasi model dan feature extractor dari Hugging Face
-model_name = "ahmadalfian/fruits_vegetables_classifier"
-model = AutoModelForImageClassification.from_pretrained(model_name)
-feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
-
-# Fungsi untuk memprediksi kelas
-def predict(image):
-    # Preprocess gambar menggunakan feature extractor
-    inputs = feature_extractor(images=image, return_tensors="pt")
-    with torch.no_grad():
-        outputs = model(**inputs)
-    predictions = outputs.logits.argmax(dim=1)
-    return predictions.item()
+# Menggunakan pipeline dari Hugging Face
+model_name = "ahmadalfian/fruits_vegetables_classifier"  # Model klasifikasi
+classifier = pipeline("image-classification", model=model_name)
 
 # Fungsi untuk mengambil informasi nutrisi
 def get_nutritional_info(food):
@@ -59,20 +48,13 @@ def get_nutritional_info(food):
     else:
         return None
 
-# Fungsi utama Gradio
+# Fungsi utama untuk klasifikasi dan informasi nutrisi
 def classify_and_get_nutrition(image):
-    predicted_class_idx = predict(image)
-    class_labels = [
-        'apple', 'banana', 'beetroot', 'bell pepper', 'cabbage', 'capsicum',
-        'carrot', 'cauliflower', 'chilli pepper', 'corn', 'cucumber', 
-        'eggplant', 'garlic', 'ginger', 'grapes', 'jalepeno', 'kiwi', 
-        'lemon', 'lettuce', 'mango', 'onion', 'orange', 'paprika', 
-        'pear', 'peas', 'pineapple', 'pomegranate', 'potato', 'raddish', 
-        'soy beans', 'spinach', 'sweetcorn', 'sweetpotato', 'tomato', 
-        'turnip', 'watermelon'
-    ]  # Semua label kelas
-    predicted_label = class_labels[predicted_class_idx]
+    # Prediksi menggunakan pipeline
+    predictions = classifier(image)
+    predicted_label = predictions[0]['label'].lower()  # Ambil label prediksi
     
+    # Ambil informasi nutrisi
     nutrisi = get_nutritional_info(predicted_label)
     
     if nutrisi: