Update app.py

app.py

@@ -107,12 +107,12 @@ assistant3 = client.beta.assistants.update(
   tool_resources={"file_search": {"vector_store_ids": [vector_store3.id]}},
 )
 
-def analyze_processing_level(ingredients, assistant_id):
+def analyze_processing_level(ingredients, product_info, assistant_id):
     thread = client.beta.threads.create(
         messages=[
             {
                 "role": "user",
-                "content": f"Categorize the product with the following ingredients: {', '.join(ingredients)} into Group A, Group B, or Group C based on the document. The output must only be the group category name (Group A, Group B, or Group C) alongwith the definition of the respective category. If the group category cannot be determined, output 'NOT FOUND'.",
+                "content": f"Categorize product {product_info["brandName"] + product_info["productName"]} that has following ingredients: {', '.join(ingredients)} into Group A, Group B, or Group C based on the document. The output must only be the group category name (Group A, Group B, or Group C) alongwith the definition of the respective category. If the group category cannot be determined, output 'NOT FOUND'.",
             }
         ]
     )
@@ -137,12 +137,12 @@ def analyze_processing_level(ingredients, assistant_id):
     processing_level_str = message_content.value
     return processing_level_str
 
-def analyze_harmful_ingredients(ingredients, assistant_id):
+def analyze_harmful_ingredients(ingredients, product_info, assistant_id):
     thread = client.beta.threads.create(
         messages=[
             {
                 "role": "user",
-                "content": "Provide detailed information about the following ingredients: " + ', '.join(ingredients) + ". The output must be in JSON format: {<ingredient_name>: <information from the document>}. If information about an ingredient is not found in the documents, the value for that ingredient must start with the prefix '(NOT FOUND IN DOCUMENT)' followed by the LLM's response based on its own knowledge.",
+                "content": "Provide detailed information about product " + product_info["brandName"] + product_info["productName"] +" that has following ingredients: " + ', '.join(ingredients) + ". The output must be in JSON format: {<ingredient_name>: <information from the document>}. If information about an ingredient is not found in the documents, the value for that ingredient must start with the prefix '(NOT FOUND IN DOCUMENT)' followed by the LLM's response based on its own knowledge.",
             }
         ]
     )
@@ -303,10 +303,15 @@ def main():
         # Display a message while analyzing
         with st.spinner("Analyzing the product... This may take a moment."):
             # Perform analysis
-            processing_level = analyze_processing_level(ingredients_list, assistant1.id)
-            harmful_ingredient_analysis = analyze_harmful_ingredients(ingredients_list, assistant2.id)
-            claims_analysis = analyze_claims(claims_list, assistant3.id)
-
+            processing_level = ""
+            harmful_ingredient_analysis = ""
+            if len(ingredients_list) > 0:
+                processing_level = analyze_processing_level(ingredients_list, product_info, assistant1.id)
+                harmful_ingredient_analysis = analyze_harmful_ingredients(ingredients_list, product_info, assistant2.id)
+            claims_analysis = ""
+            if len(claims_list) > 0:
+                claims_analysis = analyze_claims(claims_list, assistant3.id)
+            
             # Generate final analysis
             final_analysis = generate_final_analysis(
                 product_info,