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from PIL import Image
from transformers import ViTFeatureExtractor, ViTForImageClassification
import warnings
import requests
import gradio as gr
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)
# API keys for the Nutritionix API
nutritionix_app_id = '368711d5'
nutritionix_api_key = '2d35fd0c4b1503f917ce9a8230d772a8'
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://trackapi.nutritionix.com/v2/natural/nutrients'
headers = {
'x-app-id': nutritionix_app_id,
'x-app-key': nutritionix_api_key,
'Content-Type': 'application/json'
}
data = {"query": food_name}
response = requests.post(api_url, headers=headers, json=data)
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['foods']) == 0:
return "No nutritional information found."
nutrition_data = nutrition_info['foods'][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['food_name']}</b></td></tr>
<tr>
<td style="text-align: left;"><b>Calories</b></td><td style="text-align: right;">{nutrition_data['nf_calories']}</td>
<td style="text-align: left;"><b>Serving Size (g)</b></td><td style="text-align: right;">{nutrition_data['serving_weight_grams']}</td>
</tr>
<tr>
<td style="text-align: left;"><b>Total Fat (g)</b></td><td style="text-align: right;">{nutrition_data['nf_total_fat']}</td>
<td style="text-align: left;"><b>Saturated Fat (g)</b></td><td style="text-align: right;">{nutrition_data['nf_saturated_fat']}</td>
</tr>
<tr>
<td style="text-align: left;"><b>Protein (g)</b></td><td style="text-align: right;">{nutrition_data['nf_protein']}</td>
<td style="text-align: left;"><b>Sodium (mg)</b></td><td style="text-align: right;">{nutrition_data['nf_sodium']}</td>
</tr>
<tr>
<td style="text-align: left;"><b>Potassium (mg)</b></td><td style="text-align: right;">{nutrition_data['nf_potassium']}</td>
<td style="text-align: left;"><b>Cholesterol (mg)</b></td><td style="text-align: right;">{nutrition_data['nf_cholesterol']}</td>
</tr>
<tr>
<td style="text-align: left;"><b>Total Carbohydrates (g)</b></td><td style="text-align: right;">{nutrition_data['nf_total_carbohydrate']}</td>
<td style="text-align: left;"><b>Fiber (g)</b></td><td style="text-align: right;">{nutrition_data['nf_dietary_fiber']}</td>
</tr>
<tr>
<td style="text-align: left;"><b>Sugar (g)</b></td><td style="text-align: right;">{nutrition_data['nf_sugars']}</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.",
allow_flagging="never" # Disable flagging
)
# Launch the Gradio app
if __name__ == "__main__":
iface.launch()
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