app.py

# from ultralytics import YOLO
# import pandas as pd
# from PIL import Image, ImageDraw
# import gradio as gr

# # تحميل الموديل
# model = YOLO("best.pt")

# # تحميل بيانات التغذية
# food_df = pd.read_csv("food_cleaned.csv")

# # جلب القيم الغذائية
# def get_nutrition(label):
#     row = food_df[food_df["Food_Name"].str.lower() == label.lower()]
#     if row.empty:
#         return "No data"
#     cals = row["Calories_per_100g"].values[0]
#     fat = row["Fat_g"].values[0]
#     protein = row["Protein_g"].values[0]
#     carbs = row["Carbs_g"].values[0]
#     return f"{label}: {cals} kcal, {fat}g fat, {protein}g protein, {carbs}g carbs"

# # دالة الكشف والرسم
# def detect(image):
#     results = model.predict(image)
#     result = results[0]
#     boxes = result.boxes
#     names = model.names

#     img = Image.fromarray(result.plot())  # الصورة عليها البوكسات
#     draw = ImageDraw.Draw(img)

#     for box in boxes:
#         cls_id = int(box.cls[0])
#         label = names[cls_id]
#         nutrition = get_nutrition(label)
#         xy = box.xyxy[0].tolist()
#         draw.text((xy[0], xy[1] - 10), nutrition, fill=(255, 0, 0))

#     return img

# # Gradio app
# demo = gr.Interface(
#     fn=detect,
#     inputs=gr.Image(type="pil"),
#     outputs=gr.Image(type="pil"),
#     title="YOLOv8 Food Detector + Nutrition Info",
#     description="Upload an image of food and see calories and nutrients!"
# )

# demo.launch()


from ultralytics import YOLO
import pandas as pd
from PIL import Image
import gradio as gr
from collections import Counter

# Load YOLO model
model = YOLO("best.pt")

# Load food nutrition data
food_df = pd.read_csv("food_cleaned.csv")

# Retrieve nutrition info for a given label
def get_nutrition(label):
    row = food_df[food_df["Food_Name"].str.lower() == label.lower()]
    if row.empty:
        return {"label": label, "info": "No data found"}
    return {
        "label": label,
        "calories": float(row["Calories_per_100g"].values[0]),
        "fat": float(row["Fat_g"].values[0]),
        "protein": float(row["Protein_g"].values[0]),
        "carbs": float(row["Carbs_g"].values[0])
    }

# Detection and nutrition calculation
def detect(image):
    results = model.predict(image)
    result = results[0]
    boxes = result.boxes
    names = model.names

    detected_labels = []
    for box in boxes:
        cls_id = int(box.cls[0])
        label = names[cls_id]
        detected_labels.append(label)

    label_counts = Counter(detected_labels)

    total_calories = 0
    total_fat = 0
    total_protein = 0
    total_carbs = 0
    detailed_items = []

    for label, count in label_counts.items():
        nutrition = get_nutrition(label)
        if "info" in nutrition:
            continue
        nutrition["count"] = count
        nutrition["total_calories"] = round(nutrition["calories"] * count, 2)
        nutrition["total_fat"] = round(nutrition["fat"] * count, 2)
        nutrition["total_protein"] = round(nutrition["protein"] * count, 2)
        nutrition["total_carbs"] = round(nutrition["carbs"] * count, 2)

        total_calories += nutrition["total_calories"]
        total_fat += nutrition["total_fat"]
        total_protein += nutrition["total_protein"]
        total_carbs += nutrition["total_carbs"]

        detailed_items.append(nutrition)

    overall_summary = {
        "Total Calories": round(total_calories, 2),
        "Total Fat": round(total_fat, 2),
        "Total Protein": round(total_protein, 2),
        "Total Carbs": round(total_carbs, 2)
    }

    return Image.fromarray(result.plot()), {"summary": overall_summary, "details": detailed_items}

# Gradio web app
demo = gr.Interface(
    fn=detect,
    inputs=gr.Image(type="pil"),
    outputs=[
        gr.Image(type="pil", label="Detected Image"),
        gr.JSON(label="Nutrition Info")
    ],
    title="Smart Food Detector - Nutrition Calculator",
    description="Upload a food image to get total calories, fat, protein, and carbs."
)

demo.launch()