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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 key for the nutrition information
api_key = 'slMriKRETsYXPJTu4gSTSA==vhSFa3Z8exMmOIDH'
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.",
allow_flagging="never" # Disable flagging
)
# Launch the Gradio app
if __name__ == "__main__":
iface.launch()
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