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()