Update app.py

app.py

@@ -106,10 +106,23 @@ 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"
-        response = call_llm(llm_client, prompt)
+        # 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"
+        # response = call_llm(llm_client, prompt)
 
-        return {"predicted_label": prediction, "nutrition_info": response}
+        # return {"predicted_label": prediction, "nutrition_info": response}
+
+        nutrition_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 only."
+        nutrition_info = call_llm(llm_client, nutrition_prompt)
+
+        # Second prompt: Information about the prediction label
+        info_prompt = f"Provide information about the label {prediction}, including its origin, general uses, and key characteristics."
+        prediction_info = call_llm(llm_client, info_prompt)
+
+        return {
+            "predicted_label": prediction, 
+            "nutrition_info": nutrition_info,
+            "prediction_info": prediction_info
+        }
     except Exception as e:
         return JSONResponse(
             status_code=500,