Create app.py

app.py

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+import gradio as gr
+import requests
+from transformers import AutoModelForImageClassification, AutoTokenizer
+from PIL import Image
+import torch
+
+# Menginisialisasi model dan tokenizer dari Hugging Face
+model_name = "ahmadalfian/fruits_vegetables_classifier"  # Model yang kamu sebutkan
+model = AutoModelForImageClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Fungsi untuk memprediksi kelas
+def predict(image):
+    image = image.convert("RGB")
+    inputs = tokenizer(image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    predictions = outputs.logits.argmax(dim=1)
+    return predictions.item()
+
+# Fungsi untuk mengambil informasi nutrisi
+def get_nutritional_info(food):
+    api_key = "3pm2NGZzYongVN1gRjnroVLUpsHC8rKWJFyx5moq"
+    url = "https://api.nal.usda.gov/fdc/v1/foods/search"
+    
+    params = {
+        "query": food,  # Nama makanan yang diprediksi
+        "pageSize": 5,  # Ambil lima hasil
+        "api_key": api_key
+    }
+    
+    response = requests.get(url, params=params)
+    data = response.json()
+
+    if "foods" in data and len(data["foods"]) > 0:
+        # Inisialisasi total untuk nutrisi yang diinginkan
+        nutrients_totals = {
+            "Energy": 0,
+            "Carbohydrate, by difference": 0,
+            "Fiber, total dietary": 0,
+            "Vitamin C, total ascorbic acid": 0
+        }
+        item_count = len(data["foods"])
+
+        # Iterasi melalui setiap makanan
+        for food in data["foods"]:
+            for nutrient in food['foodNutrients']:
+                nutrient_name = nutrient['nutrientName']
+                nutrient_value = nutrient['value']
+
+                # Cek apakah nutrisi termasuk yang diinginkan
+                if nutrient_name in nutrients_totals:
+                    nutrients_totals[nutrient_name] += nutrient_value
+
+        # Menghitung rata-rata nilai nutrisi
+        average_nutrients = {name: total / item_count for name, total in nutrients_totals.items()}
+
+        return average_nutrients
+    else:
+        return None
+
+# Fungsi utama Gradio
+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]
+    
+    nutrisi = get_nutritional_info(predicted_label)
+    
+    if nutrisi:
+        return {
+            "Predicted Class": predicted_label,
+            "Energy (kcal)": nutrisi["Energy"],
+            "Carbohydrates (g)": nutrisi["Carbohydrate, by difference"],
+            "Fiber (g)": nutrisi["Fiber, total dietary"],
+            "Vitamin C (mg)": nutrisi["Vitamin C, total ascorbic acid"]
+        }
+    else:
+        return {
+            "Predicted Class": predicted_label,
+            "Nutritional Information": "Not Found"
+        }
+
+# Antarmuka Gradio
+iface = gr.Interface(
+    fn=classify_and_get_nutrition,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs=gr.outputs.JSON(),
+    title="Fruits and Vegetables Classifier",
+    description="Upload an image of a fruit or vegetable to classify and get its nutritional information."
+)
+
+iface.launch()