Update app.py

app.py

@@ -106,8 +106,8 @@ async def predict_image_and_nutrition(file: UploadFile = File(...)):
             return json.loads(response.decode())[0]["generated_text"]
         
         # Use the prediction to generate nutrition information
-        # prompt = f"Nutrition information (Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6, Folate, Niacin, Pantothenic acid) for {prediction} in formatted list"
-        prompt = f"Provide all the nutrition information for {prediction}, including Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6, Folate, Niacin, and Pantothenic acid. Please present the information in a clear, formatted list only, without additional explanations."
+        prompt = f"Nutrition information (Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6, Folate, Niacin, Pantothenic acid) for {prediction} in formatted list"
+        # prompt = f"Provide all the nutrition information for {prediction}, including Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6, Folate, Niacin, and Pantothenic acid. Please present the information in a clear, formatted list only, without additional explanations."
         response = call_llm(llm_client, prompt)
 
         return {"predicted_label": prediction, "nutrition_info": response}