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llm.py

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+
+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]
+
+
+

vision.py

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+
+# models/vision.py -- Working
+
+from transformers import pipeline
+from PIL import Image
+
+class VisionModel:
+    def __init__(
+        self,
+        model_name: str = "valentinafeve/yolos-fashionpedia",
+        threshold: float = 0.7
+    ):
+        self.pipe = pipeline("object-detection", model=model_name)
+        self.threshold = threshold
+
+    def detect(self, image: Image.Image):
+        # 1) Ensure RGB
+        if image.mode != "RGB":
+            image = image.convert("RGB")
+
+        # 2) Run detection
+        results = self.pipe(image)
+
+        # 3) Process & filter
+        processed = []
+        for r in results:
+            score = float(r["score"])
+            if score < self.threshold:
+                continue
+
+            # r["box"] is a dict: {"xmin":..., "ymin":..., "xmax":..., "ymax":...}
+            box = r["box"]
+            coords = [
+                float(box["xmin"]),
+                float(box["ymin"]),
+                float(box["xmax"]),
+                float(box["ymax"]),
+            ]
+
+            processed.append({
+                "label": r["label"],
+                "score": score,
+                "box": coords
+            })
+
+        return processed
+
+
+
+