Update app.py

app.py

@@ -283,6 +283,37 @@ def initialize_assistants_and_vector_stores():
 
 assistant1, assistant2, assistant3 = initialize_assistants_and_vector_stores()
 
+def analyze_nutrition_icmr_rda(nutrient_analysis, nutrient_analysis_rda):
+    global debug_mode, client
+    system_prompt = """Analyze the nutritional content of the food item, focusing on nutrients that significantly exceed the Recommended Daily Allowance (RDA) or threshold limits defined by ICMR. 
+Provide contextual insights for users as explained below:
+
+Calories: If the calorie content is very high, compare it to a well-balanced nutritional meal. Indicate how many such meals' worth of calories the product contains.
+Sugar & Salt: Present the amounts in teaspoons to make it easier to understand daily intake levels.
+Fat & Calories: Offer practical context for these nutrients, explaining the implications of their levels and how they relate to balanced eating.
+
+Ensure the response is clear, accurate, and provides actionable recommendations for healthier choices."""
+
+    user_prompt = f"""
+Product Name: {brand_name} {product_name}
+
+Nutrition Analysis :
+{nutrient_analysis}
+{nutrient_analysis_rda}
+"""
+    if debug_mode:
+        print(f"\nuser_prompt : \n {user_prompt}")
+        
+    completion = client.chat.completions.create(
+        model="gpt-4o",  # Make sure to use an appropriate model
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt}
+        ]
+    )
+
+    return completion.choices[0].message.content
+    
 def analyze_processing_level(ingredients, brand_name, product_name, assistant_id):
     global debug_mode, client
     thread = client.beta.threads.create(
@@ -410,7 +441,7 @@ def analyze_claims(claims, ingredients, product_name, assistant_id):
     
     return claims_analysis_str
 
-def generate_final_analysis(brand_name, product_name, nutrient_analysis, nutrient_analysis_rda, processing_level, harmful_ingredient_analysis, claims_analysis, system_prompt):
+def generate_final_analysis(brand_name, product_name, nutritional_level, processing_level, harmful_ingredient_analysis, claims_analysis, system_prompt):
     global debug_mode, client
     system_prompt_orig = """You are provided with a detailed analysis of a food product. Your task is to generate actionable insights to help the user decide whether to consume the product, at what frequency, and identify any potential harms or benefits. Consider the context of consumption to ensure the advice is personalized and practical.
 
@@ -445,8 +476,7 @@ Additionally, consider the following while generating insights:
 Product Name: {brand_name} {product_name}
 
 Nutrition Analysis :
-{nutrient_analysis}
-{nutrient_analysis_rda}
+{nutritional_level}
 
 Processing Level:
 {processing_level}
@@ -495,7 +525,9 @@ def analyze_product(product_info_raw, system_prompt):
 
             nutrient_analysis_rda = find_nutrition(nutrient_analysis_rda_data)
             print(f"DEBUG ! RDA nutrient analysis is {nutrient_analysis_rda}")
-            
+            #Call GPT for nutrient analysis
+            nutritional_level = analyze_nutrition_icmr_rda(nutrient_analysis, nutrient_analysis_rda)
+        
         if len(ingredients_list) > 0:    
             processing_level = analyze_processing_level(ingredients_list, brand_name, product_name, assistant1.id) if ingredients_list else ""
             harmful_ingredient_analysis = analyze_harmful_ingredients(ingredients_list, brand_name, product_name, assistant2.id) if ingredients_list else ""
@@ -503,7 +535,7 @@ def analyze_product(product_info_raw, system_prompt):
         if len(claims_list) > 0:                    
             claims_analysis = analyze_claims(claims_list, ingredients_list, product_name, assistant3.id) if claims_list else ""
                 
-        final_analysis = generate_final_analysis(brand_name, product_name, nutrient_analysis, nutrient_analysis_rda, processing_level, harmful_ingredient_analysis, claims_analysis, system_prompt)
+        final_analysis = generate_final_analysis(brand_name, product_name, nutritional_level, processing_level, harmful_ingredient_analysis, claims_analysis, system_prompt)
         return final_analysis
     else:
         return "I'm sorry, product information could not be extracted from the url."