Update app.py (#2)

- Update app.py (ae87d076b81cff6841a1c1007ab882afad3d1505)

app.py

@@ -4,6 +4,7 @@ import json, os
 import requests, time
 from data_extractor import extract_data, find_product, get_product
 from nutrient_analyzer import analyze_nutrients
+from rda import find_nutrition
 
 #Used the @st.cache_resource decorator on this function. 
 #This Streamlit decorator ensures that the function is only executed once and its result (the OpenAI client) is cached. 
@@ -35,7 +36,41 @@ def get_product_list(product_name_by_user, data_extractor_url):
     response = find_product(product_name_by_user)
     return response
 
-def find_product_type(product_info_from_db):
+def rda_analysis(product_info_from_db_nutritionalInformation, product_info_from_db_servingSize):
+    data_for_rda_analysis = {'nutritionPerServing' : {}, 'userServingSize' : None}
+    nutrient_name_list = ['energy', 'protein', 'carbohydrates', 'addedSugars', 'dietaryFiber', 'totalFat', 'saturatedFat', 'monounsaturatedFat', 'polyunsaturatedFat', 'transFat', 'sodium']
+
+    completion = client.chat.completions.create(
+        model="gpt-4o",
+        messages=[
+            {"role": "system", "content": f"You will be given nutritional information of a food product. Find values of {', '.join(nutrient_name_list)} in the user-defined format"},
+            {"role": "user", "content": 
+             "Nutritional content of food product is " + json.dumps(product_info_from_db_nutritionalInformation) + """. Generate output in this JSON format 
+            {'energy': <value>,
+            'protein': <value>,
+            'carbohydrates': <value>,
+            'addedSugars': <value>,
+            'dietaryFiber': <value>,
+            'totalFat': <value>,
+            'saturatedFat': <value>,
+            'monounsaturatedFat': <value>,
+            'polyunsaturatedFat': <value>,
+            'transFat': <value>,
+            'sodium': <value>,
+            'servingSize': <Serving size of the provided nutrition value>} """}
+        ]
+    )
+
+    data_for_rda_analysis_part_1 = completion.choices[0].message.content
+    print(f"data_for_rda_analysis_part_1 : {data_for_rda_analysis_part_1}")
+
+    data_for_rda_analysis = {}
+    data_for_rda_analysis.update({'nutritionPerServing' : data_for_rda_analysis_part_1.replace("```", "").replace("json", "")})
+    data_for_rda_analysis.update({'userServingSize' : product_info_from_db_servingSize})
+    
+    return data_for_rda_analysis
+
+def find_product_nutrients(product_info_from_db):
     #GET Response: {'_id': '6714f0487a0e96d7aae2e839',
     #'brandName': 'Parle', 'claims': ['This product does not contain gold'],
     #'fssaiLicenseNumbers': [10013022002253],
@@ -56,6 +91,21 @@ def find_product_type(product_info_from_db):
     #'servingsPerPack': 3.98,
     #'shelfLife': '7 months from packaging'}
 
+    ###rda###
+    #nutrition_data = {
+    #'energy': 250,
+    #'protein': 10,
+    #'carbohydrates': 30,
+    #'addedSugars': 5,
+    #'dietaryFiber': 3,
+    #'totalFat': 10,
+    #'saturatedFat': 3,
+    #'monounsaturatedFat': 2,
+    #'polyunsaturatedFat': 1,
+    #'transFat': 0,
+    #'sodium': 200,
+    #'servingSize': 100  # Serving size of the provided nutrition values
+    #}
 
     product_type = None
     calories = None
@@ -64,14 +114,13 @@ def find_product_type(product_info_from_db):
     added_sugar = None
     salt = None
     serving_size = None
-    
+
     if product_info_from_db["servingSize"]["unit"] == "g":
         product_type = "solid"
     elif product_info_from_db["servingSize"]["unit"] == "ml":
         product_type = "liquid"
     serving_size = product_info_from_db["servingSize"]["quantity"]
-    
-    for item in product_info_from_db["nutritionalInformation"]:
+            
         if 'energy' in item['name'].lower():
             calories = item['values'][0]['value']
         if 'total sugar' in item['name'].lower():
@@ -86,6 +135,7 @@ def find_product_type(product_info_from_db):
         sugar = added_sugar
     elif total_sugar is not None and total_sugar > 0 and added_sugar is None and sugar is None:
         sugar = total_sugar
+
     return product_type, calories, sugar, salt, serving_size
     
 # Initialize assistants and vector stores
@@ -391,13 +441,23 @@ def analyze_product(product_info_raw, system_prompt):
         product_name = product_info_from_db.get("productName", "")
         ingredients_list = [ingredient["name"] for ingredient in product_info_from_db.get("ingredients", [])]
         claims_list = product_info_from_db.get("claims", [])
+        nutritional_information = product_info_from_db['nutritionalInformation']
+        serving_size = product_info_from_db["servingSize"]["quantity"]
 
-        if len(ingredients_list) > 0:
+        if nutritional_information:
             product_type, calories, sugar, salt, serving_size = find_product_type(product_info_from_db)
             nutrient_analysis = analyze_nutrients(product_type, calories, sugar, salt, serving_size)
             print(f"DEBUG ! nutrient analysis is {nutrient_analysis}")
+            
+            nutrient_analysis_rda_data = rda_analysis(nutritional_information, serving_size)
+            print(f"DEBUG ! Data for RDA nutrient analysis is {nutrient_analysis_rda_data}")
+            nutrient_analysis_rda = find_nutrition(nutrient_analysis_rda_data)
+            print(f"DEBUG ! RDA nutrient analysis is {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 ""
+        
         if len(claims_list) > 0:                    
             claims_analysis = analyze_claims(claims_list, ingredients_list, product_name, assistant3.id) if claims_list else ""