Update app.py

app.py

@@ -1,4 +1,3 @@
-# Import necessary libraries
 import gradio as gr
 import wget
 from transformers import pipeline
@@ -16,80 +15,52 @@ headers = {
 
 # 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)
-
-# Load the Translation Model (English to Arabic)
 translation_eng_to_ar = pipeline("translation_en_to_ar", model="marefa-nlp/marefa-mt-en-ar", 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 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
-    }
-
-    # Send a POST request to the Nutritionix API with the food item
+    data = {"query": food}
     response = requests.post(api_url, headers=headers, json=data)
+    return response.json()
 
-    # 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
+    text = text.strip()
+    result = translation_eng_to_ar(text)
+    return result[0]['translation_text']
 
-# 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"
+        serving_size_text_en = f"{serving_size} mL"
+        serving_size_text_ar = f"{serving_size} 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"""
+        return f"""
         <div style='direction: rtl; text-align: right;'>
             <b>الطعام</b>: {food_item_ar}<br>
             <b>حجم الحصة</b>: {serving_size_text_ar}<br>
@@ -102,10 +73,8 @@ def process_food_result(image, language):
             <b>الدهون</b>: {fat} جرام
         </div>
         """
-        return output_ar
     else:
-       # For English output
-        output_en = f"""
+        return f"""
         <div style='text-align: left;'>
             <b>Food</b>: {food_item}<br>
             <b>Serving Size</b>: {serving_size_text_en}<br>
@@ -118,38 +87,15 @@ def process_food_result(image, language):
             <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]
-examples = [[img] for img in example_images]
-
+    return process_food_result(image, language)
 
-# 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=examples  # Add examples with the image and language
+    fn=gradio_function,
+    inputs=[gr.Image(type="pil", label="Upload an image"),
+            gr.Dropdown(choices=["Arabic", "English"], label="Select Language", value="Arabic")],
+    outputs=gr.HTML(label="Food and Nutrition Information")
 )
 
-# Launch the Gradio interface with debug mode enabled
-iface.launch(debug=True)
+iface.launch(debug=True)