Create app.py

app.py

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+# Import necessary libraries
+import gradio as gr
+import wget
+from transformers import pipeline
+import requests
+
+# Nutritionix API setup
+api_url = "https://trackapi.nutritionix.com/v2/natural/nutrients"
+
+# App ID, App Key provided by Nutritionix
+headers = {
+    "x-app-id": "dd773727",
+    "x-app-key": "86f278fc4c7f276c386f280848acf3e6",
+}
+
+# Load the Models
+
+# Load the BLIP VQA Model (Recognize the food)
+visual_quest_ans = pipeline("visual-question-answering", model="Salesforce/blip-vqa-base")
+
+# Load the Translation Model (English to Arabic)
+translation_eng_to_ar = pipeline("translation_en_to_ar", model="marefa-nlp/marefa-mt-en-ar")
+
+# 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 on 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,
+        "timezone": "US/Eastern"
+    }
+
+    # Send a POST request to the Nutritionix API with the food item
+    response = requests.post(api_url, headers=headers, json=data)
+
+    # Get the nutritional information in JSON format
+    nutritions = response.json()
+    return nutritions
+
+# Function to translate text from English to Arabic with preprocessing
+def translator(text):
+    text = text.strip()  # Remove leading/trailing spaces
+    result = translation_eng_to_ar(text) # Use the translation model to translate the text
+    result = result[0]['translation_text']
+    return result
+
+# Function to process food recognition and get nutrition info
+def process_food_result(image, language):
+    # Recognize the food item in the uploaded image
+    food_item = food_recognizer(image)
+    
+    # Fetch nutritional information for the recognized food item
+    nutritions_info = nutrition_info(food_item)
+
+    # Extract nutritional information
+    food_info = nutritions_info['foods'][0]
+    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
+    if language == "Arabic":
+        # Translate the food item name to Arabic
+        food_item_ar = translator(food_item)
+        output_ar = f"""
+        <div style='direction: rtl; text-align: right;'>
+            <b>الطعام</b>: {food_item_ar}<br>
+            <b>حجم الحصة</b>: {serving_size_text_ar}<br>
+            <b>السعرات الحرارية</b>: {calories} كيلو كالوري<br>
+            <b>البروتين</b>: {protein} جرام<br>
+            <b>الكربوهيدرات</b>: {carbs} جرام<br>
+            <b>السكر</b>: {sugars} جرام<br>
+            <b>الألياف</b>: {fiber} جرام<br>
+            <b>الصوديوم</b>: {sodium} مجم<br>
+            <b>الدهون</b>: {fat} جرام
+        </div>
+        """
+        return output_ar
+    else:
+       # For English output
+        output_en = f"""
+        <div style='text-align: left;'>
+            <b>Food</b>: {food_item}<br>
+            <b>Serving Size</b>: {serving_size_text_en}<br>
+            <b>Calories</b>: {calories} kcal<br>
+            <b>Protein</b>: {protein}g<br>
+            <b>Carbohydrates</b>: {carbs}g<br>
+            <b>Sugars</b>: {sugars}g<br>
+            <b>Fiber</b>: {fiber}g<br>
+            <b>Sodium</b>: {sodium}mg<br>
+            <b>Fat</b>: {fat}g
+        </div>
+        """
+        return output_en
+
+
+# Gradio interface function
+def gradio_function(image, language):
+    # Call the process_food_result function to get the output
+    result = process_food_result(image, language)
+    return result
+
+# Define URLs of example images
+image_urls = [
+    "https://raw.githubusercontent.com/Abdulrahman078/ML_Datasets-Imgs-Vids/main/close-up-delicious-pizza.jpg",
+    "https://raw.githubusercontent.com/Abdulrahman078/ML_Datasets-Imgs-Vids/main/assorted-desserts-with-chocolate-frosted-pink-glazed-sprinkles.jpg",
+    "https://raw.githubusercontent.com/Abdulrahman078/ML_Datasets-Imgs-Vids/main/fried-fish-with-cranberries-wooden-board.jpg",
+    "https://raw.githubusercontent.com/Abdulrahman078/ML_Datasets-Imgs-Vids/main/glass-water.jpg"
+]
+
+# Download the images and use their paths
+example_images = [wget.download(url) for url in image_urls]
+
+
+# Setup the Gradio interface
+iface = gr.Interface(
+    fn=gradio_function, # Function to call
+    inputs=[gr.Image(type="pil", label="Upload an image"), # Input: Image (in PIL format)
+            gr.Dropdown(choices=["Arabic", "English"], label="Select Language", value="Arabic")], # Input: Dropdown for language selection
+    outputs=gr.HTML(label="Food and Nutrition Information"), # Output: HTML for displaying nutrition info
+    title="Bilingual 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 in both English or Arabic languages.", # Description of the tool
+    examples=[[img] for img in example_images]  # Add examples with the image and language
+)
+
+# Launch the Gradio interface with debug mode enabled
+iface.launch(debug=True)