Update app.py

app.py

@@ -20,9 +20,6 @@ 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 in the image
@@ -46,12 +43,7 @@ def nutrition_info(food):
             return nutritions['foods'][0]  # Return the first food item
     return None  # Return None if no valid nutrition data is found
 
-# 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,progress= gr.Progress()):
@@ -93,26 +85,10 @@ def process_food_result(image, language,progress= gr.Progress()):
         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"""
+    output_en = f"""
         <div style='text-align: left;'>
             <b>Food</b>: {food_item}<br>
             <b>Serving Size</b>: {serving_size_text_en}<br>
@@ -125,7 +101,7 @@ def process_food_result(image, language,progress= gr.Progress()):
             <b>Fat</b>: {fat}g
         </div>
         """
-        return output_en
+    return output_en
 
 # Gradio interface function
 def gradio_function(image, language):