import gradio as gr
from transformers import pipeline
import requests
import torch
import time
# Nutritionix API setup
api_url = "https://trackapi.nutritionix.com/v2/natural/nutrients"

# App ID, App Key provided by Nutritionix
headers = {
    "x-app-id": "703922ce",
    "x-app-key": "44e3db1ebdb8fa6a763ea2aaced07959",
}

# Load the Models

# Check if a GPU is available
device = 0 if torch.cuda.is_available() else -1

# Load the BLIP VQA Model (Recognize the food)
visual_quest_ans = pipeline("visual-question-answering", model="Salesforce/blip-vqa-base", device=device)

# Function to recognize food from the image using the VQA model
def food_recognizer(image):
    # Pass the image and the question to the model to identify the food in the image
    result = visual_quest_ans(image=image, question="What is the food or the drink in the image?")
    return result[0]['answer']

# Function to fetch nutritional information from Nutritionix API
def nutrition_info(food):
    # Prepare the data for the API request
    data = {
        "query": food
    }

    # Send a POST request to the Nutritionix API with the food item
    response = requests.post(api_url, headers=headers, json=data)

    # Check if the response is valid and contains the necessary information
    if response.status_code == 200:
        nutritions = response.json()
        if 'foods' in nutritions and len(nutritions['foods']) > 0:
            return nutritions['foods'][0]  # Return the first food item
    return None  # Return None if no valid nutrition data is found



# Function to process food recognition and get nutrition info
def process_food_result(image):
    
    # Recognize the food item in the uploaded image
    food_item = food_recognizer(image)
    
    # Fetch nutritional information for the recognized food item
    food_info = nutrition_info(food_item)

    # If no nutritional information is found, return a message
    if food_info is None:
        return f"Sorry, no nutritional information found for '{food_item}'."
    # Extract nutritional information
    calories = food_info['nf_calories']
    protein = food_info['nf_protein']
    carbs = food_info['nf_total_carbohydrate']
    fat = food_info['nf_total_fat']
    # Use 'Unknown' if value is not available
    sugars = food_info.get('nf_sugars', 'Unknown')
    fiber = food_info.get('nf_dietary_fiber', 'Unknown')
    sodium = food_info.get('nf_sodium', 'Unknown')
    serving_size = food_info.get('serving_weight_grams', 'Unknown')
    
    # Identify if the food item is a liquid (simple check for common drink categories)
    liquid_keywords = ['juice', 'water', 'milk', 'soda', 'tea', 'coffee']
    is_liquid = any(keyword in food_item.lower() for keyword in liquid_keywords)

    # Convert serving size to milliliters if it's a liquid
    if is_liquid and serving_size != 'Unknown':
        serving_size_ml = serving_size  # Assume 1 gram ≈ 1 milliliter for liquids
        serving_size_text_en = f"{serving_size_ml} mL"
        serving_size_text_ar = f"{serving_size_ml} مل"
    else:
        serving_size_text_en = f"{serving_size} grams"
        serving_size_text_ar = f"{serving_size} جرام"

    # Generate output in the selected language
    

    output_en = f"""
        Food: {food_item}
        Serving Size: {serving_size_text_en}
        Calories: {calories} kcal
        Protein: {protein}g
        Carbohydrates: {carbs}g
        Sugars: {sugars}g
        Fiber: {fiber}g
        Sodium: {sodium}mg
        Fat: {fat}g 
        """
    return output_en

# Gradio interface function
def gradio_function(image):
    # Call the process_food_result function to get the output
    
    result = process_food_result(image)
    return result

# Setup the Gradio interface
iface = gr.Interface(
    fn=gradio_function, # Function to call
    inputs=[gr.Image(type="pil", label="Upload an image")],
    outputs=gr.Textbox(label="Food and Nutrition Information"), 
    title="Food Recognition and Nutrition Info Tool", # Title of the Gradio interface
    description="Upload an image of food, and the tool will recognize it and provide nutritional information." # Description of the tool
)

# Launch the Gradio interface with debug mode enabled
iface.launch(share=True)