app.py

from PIL import Image
from transformers import ViTFeatureExtractor, ViTForImageClassification
import warnings
import requests
import gradio as gr
from dotenv import load_dotenv
import os

load_dotenv()  # Load .env file
# API key for the nutrition information
api_key =os.getenv("API_KEY")


warnings.filterwarnings('ignore')

# Load the pre-trained Vision Transformer model and feature extractor
model_name = "google/vit-base-patch16-224"
feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
model = ViTForImageClassification.from_pretrained(model_name)


examples = [
    ["data/apple.jpg"],  # Path to example images
    ["data/banana.jpg"],
    ["data/pizza.png"],
    ["data/burger.png"],
    ["data/grapes.jpeg"],
    ["data/fries.jpeg"],
]


def identify_image(image_path):
    """Identify the food item in the image."""
    image = Image.open(image_path)
    inputs = feature_extractor(images=image, return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class_idx = logits.argmax(-1).item()
    predicted_label = model.config.id2label[predicted_class_idx]
    food_name = predicted_label.split(',')[0]
    return food_name

def get_calories(food_name):
    """Get the calorie information of the identified food item."""
    api_url = 'https://api.api-ninjas.com/v1/nutrition?query={}'.format(food_name)
    response = requests.get(api_url, headers={'X-Api-Key': api_key})
    if response.status_code == requests.codes.ok:
        nutrition_info = response.json()
    else:
        nutrition_info = {"Error": response.status_code, "Message": response.text}
    return nutrition_info

def format_nutrition_info(nutrition_info):
    """Format the nutritional information into an HTML table."""
    if "Error" in nutrition_info:
        return f"Error: {nutrition_info['Error']} - {nutrition_info['Message']}"
    
    if len(nutrition_info) == 0:
        return "No nutritional information found."

    nutrition_data = nutrition_info[0]
    table = f"""
    <table border="1" style="width: 100%; border-collapse: collapse;">
        <tr><th colspan="4" style="text-align: center;"><b>Nutrition Facts</b></th></tr>
        <tr><td colspan="4" style="text-align: center;"><b>Food Name: {nutrition_data['name']}</b></td></tr>
        <tr>
            <td style="text-align: left;"><b>Calories</b></td><td style="text-align: right;">{nutrition_data['calories']}</td>
            <td style="text-align: left;"><b>Serving Size (g)</b></td><td style="text-align: right;">{nutrition_data['serving_size_g']}</td>
        </tr>
        <tr>
            <td style="text-align: left;"><b>Total Fat (g)</b></td><td style="text-align: right;">{nutrition_data['fat_total_g']}</td>
            <td style="text-align: left;"><b>Saturated Fat (g)</b></td><td style="text-align: right;">{nutrition_data['fat_saturated_g']}</td>
        </tr>
        <tr>
            <td style="text-align: left;"><b>Protein (g)</b></td><td style="text-align: right;">{nutrition_data['protein_g']}</td>
            <td style="text-align: left;"><b>Sodium (mg)</b></td><td style="text-align: right;">{nutrition_data['sodium_mg']}</td>
        </tr>
        <tr>
            <td style="text-align: left;"><b>Potassium (mg)</b></td><td style="text-align: right;">{nutrition_data['potassium_mg']}</td>
            <td style="text-align: left;"><b>Cholesterol (mg)</b></td><td style="text-align: right;">{nutrition_data['cholesterol_mg']}</td>
        </tr>
        <tr>
            <td style="text-align: left;"><b>Total Carbohydrates (g)</b></td><td style="text-align: right;">{nutrition_data['carbohydrates_total_g']}</td>
            <td style="text-align: left;"><b>Fiber (g)</b></td><td style="text-align: right;">{nutrition_data['fiber_g']}</td>
        </tr>
        <tr>
            <td style="text-align: left;"><b>Sugar (g)</b></td><td style="text-align: right;">{nutrition_data['sugar_g']}</td>
            <td></td><td></td>
        </tr>
    </table>
    """
    return table

def main_process(image_path):
    """Identify the food item and fetch its calorie information."""
    food_name = identify_image(image_path)
    nutrition_info = get_calories(food_name)
    formatted_nutrition_info = format_nutrition_info(nutrition_info)
    return formatted_nutrition_info

# Define the Gradio interface
def gradio_interface(image):
    formatted_nutrition_info = main_process(image)
    return formatted_nutrition_info

# Create the Gradio UI
iface = gr.Interface(
    fn=gradio_interface,
    inputs=gr.Image(type="filepath"),
    outputs="html",
    title="Food Identification and Nutrition Info",
    description="Upload an image of food to get nutritional information.",
    examples=examples,  # Add examples
    allow_flagging="never"  # Disable flagging
)

# Launch the Gradio app
if __name__ == "__main__":
    iface.launch(share=True)