Update app.py

app.py

@@ -83,7 +83,8 @@ async def predict_image_and_nutrition(file: UploadFile = File(...)):
 
         # Define the repository ID and your token
         #repo_id = "google/gemma-2-9b-it"
-        repo_id = "microsoft/Phi-3-mini-4k-instruct"
+        repo_id = "Qwen/Qwen2.5-72B-Instruct"
+        # repo_id = "microsoft/Phi-3-mini-4k-instruct"
         # repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
         api_token = "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21"
 
@@ -106,13 +107,24 @@ 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."
-        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"
+        # # 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}
+        # 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_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} and give tips for making it healthier."
+        health_benefits_and_tips = call_llm(llm_client, health_benefits_prompt)
+
+        return {
+            "predicted_label": prediction, 
+            "nutrition_info": nutrition_info,
+            "health_benefits_and_tips": health_benefits_and_tips
+        }
     except Exception as e:
         return JSONResponse(
             status_code=500,