Update app.py

app.py

@@ -114,19 +114,19 @@ async def predict_image_and_nutrition(file: UploadFile = File(...)):
         # return {"predicted_label": prediction, "nutrition_info": response}
 
         # nutrition_prompt = f"Provide the nutrition information (Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6, Folate, Niacin, Pantothenic acid) for {prediction} per 100 grams in a formatted list only."
-        # nutrition_prompt = f"Provide the nutrition information (Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6) for {prediction} per 100 grams, Output the information as a concise, formatted list without repetition."
-        # nutrition_info = call_llm(llm_client, nutrition_prompt)
+        nutrition_prompt = f"Provide the nutrition information (Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6) for {prediction} per 100 grams, Output the information as a concise, formatted list without repetition."
+        nutrition_info = call_llm(llm_client, nutrition_prompt)
         
         # # Second prompt: Health benefits and tips
-        # health_benefits_prompt = f"Provide the health benefits and considerations for {prediction}. Additionally, include practical tips for making {prediction} healthier. Keep the response focused on these two aspects only."
-        health_benefits_prompt = f"Provide detailed information about {prediction}, including its origin, common uses, cultural significance, and any interesting facts. Keep the response informative and well-structured."
+        health_benefits_prompt = f"Provide the health benefits and considerations for {prediction}. Additionally, include practical tips for making {prediction} healthier. Keep the response focused on these two aspects only."
+        # health_benefits_prompt = f"Provide detailed information about {prediction}, including its origin, common uses, cultural significance, and any interesting facts. Keep the response informative and well-structured."
 
         Information = call_llm(llm_client, health_benefits_prompt)
 
         return {
             "predicted_label": prediction, 
-            "Information": Information,
-            # "nutrition_info": nutrition_info
+            "nutrition_info": nutrition_info,
+            "Information": Information
         }
     except Exception as e:
         return JSONResponse(