Update llm.py

models/llm.py

@@ -1,4 +1,4 @@
-
+import json
 from transformers import pipeline
 
 class StyleSavvy:
@@ -8,7 +8,6 @@ class StyleSavvy:
         device: int   = -1,     # -1 = CPU, or GPU index
         max_length: int = 150,
     ):
-        # A local instruction-tuned T5 model
         self.pipe = pipeline(
             "text2text-generation",
             model=model_name,
@@ -16,12 +15,11 @@ class StyleSavvy:
             device=device,
         )
         self.max_length = max_length
-    # TODO: Modification: Add more prompts to the advise function
-    # to make it more specific to the user's needs.
-    # The function now takes in the user's body type, face shape, and occasion
-    # and generates style tips accordingly.
 
     def advise(self, items, body_type, face_shape, occasion):
+        # 如果 items 是 JSON 字串，先 parse 回 Python list
+        items = json.loads(items) if isinstance(items, str) else items
+
         prompt = (
             f"The user is {body_type}-shaped with a {face_shape} face, "
             f"attending a {occasion}. They are wearing: "
@@ -29,7 +27,6 @@ class StyleSavvy:
             + ".\n\nPlease list 5 concise style tips as bullet points:"
         )
 
-        # Generate with supported args only
         result = self.pipe(
             prompt,
             max_length=self.max_length,
@@ -37,12 +34,13 @@ class StyleSavvy:
             early_stopping=True,
             do_sample=False
         )[0]["generated_text"].strip()
-        
+
         return result
 
 
 
 
+
 # import torch
 
 # # models/llm.py