Delete llm.py

llm.py

@@ -1,126 +0,0 @@
-
-from transformers import pipeline
-
-class StyleSavvy:
-    def __init__(
-        self,
-        model_name: str = "google/flan-t5-large",
-        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,
-            tokenizer=model_name,
-            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):
-        prompt = (
-            f"The user is {body_type}-shaped with a {face_shape} face, "
-            f"attending a {occasion}. They are wearing: "
-            + ", ".join(i["label"] for i in items)
-            + ".\n\nPlease list 5 concise style tips as bullet points:"
-        )
-
-        # Generate with supported args only
-        result = self.pipe(
-            prompt,
-            max_length=self.max_length,
-            num_beams=4,
-            early_stopping=True,
-            do_sample=False
-        )[0]["generated_text"].strip()
-        
-        return result
-
-
-
-
-# import torch
-
-# # models/llm.py
-
-# # models/llm.py
-
-# import os
-# from typing import List
-# from transformers import pipeline, Pipeline
-
-# # Force CPU modes (avoid any MPS/CUDA issues on macOS)
-# os.environ["CUDA_VISIBLE_DEVICES"] = ""
-# os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "0"
-
-# class StyleSavvy:
-#     def __init__(
-#         self,
-#         model_name: str = "openlm-research/open_llama_3b_v2",
-#         device: int   = -1,        # -1 = CPU
-#         max_new_tokens: int = 100,
-#         temperature: float   = 0.7,
-#         top_p: float         = 0.9,
-#     ):
-#         """
-#         Uses OpenLLaMA-3B-v2 (≈3B params) for fast, local inference.
-#         """
-#         # Setup a causal text-generation pipeline
-#         self.pipe: Pipeline = pipeline(
-#             "text-generation",
-#             model=model_name,
-#             tokenizer=model_name,
-#             device=device,
-#         )
-#         # GPT‐style models need a pad token to avoid warnings
-#         if self.pipe.tokenizer.pad_token_id is None:
-#             self.pipe.tokenizer.pad_token = self.pipe.tokenizer.eos_token
-        
-#         self.max_new_tokens = max_new_tokens
-#         self.temperature    = temperature
-#         self.top_p          = top_p
-
-#     def advise(
-#         self,
-#         items: List[str],
-#         body_type: str,
-#         face_shape: str,
-#         occasion: str
-#     ) -> List[str]:
-#         """
-#         Builds a strict instruction prompt and returns exactly five "- " bullets.
-#         """
-#         labels = ", ".join(items) if items else "an outfit"
-#         prompt = (
-#             "You are a professional fashion consultant.\n"
-#             f"The user is {body_type}-shaped with a {face_shape} face, attending {occasion}.\n"
-#             f"They are wearing: {labels}.\n\n"
-#             "Please provide exactly five concise style tips, each on its own line, "
-#             "and starting with \"- \". No extra text."
-#         )
-
-#         # Generate
-#         output = self.pipe(
-#             prompt,
-#             max_new_tokens=self.max_new_tokens,
-#             do_sample=True,
-#             temperature=self.temperature,
-#             top_p=self.top_p,
-#             return_full_text=False,
-#         )[0]["generated_text"]
-
-#         # Extract bullets
-#         tips = [ln.strip() for ln in output.splitlines() if ln.strip().startswith("- ")]
-#         # Fallback: split on sentences if fewer than 5 bullets
-#         if len(tips) < 5:
-#             candidates = [s.strip() for s in output.replace("\n"," ").split(".") if s.strip()]
-#             tips = [f"- {candidates[i]}" for i in range(min(5, len(candidates)))]
-        
-#         return tips[:5]
-
-
-