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Manik Sheokand commited on
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3e46c4a
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1 Parent(s): 794d924

aded recommendor

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  1. =0.0.8 +187 -0
  2. =0.4.3 +0 -0
  3. app.py +74 -15
  4. data/cosmetics_full_en_zh.jsonl +0 -0
  5. indexes/cosmetics_meta.json +2426 -0
  6. requirements.txt +4 -1
  7. tools/build_index.py +80 -0
  8. tools/reranker.py +25 -0
  9. tools/retriever.py +46 -0
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+ Collecting safetensors
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+ Downloading safetensors-0.6.2-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)
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+ Collecting transformers==4.51.3
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+ Downloading transformers-4.51.3-py3-none-any.whl.metadata (38 kB)
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+ Collecting accelerate==0.32.1
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+ Downloading accelerate-0.32.1-py3-none-any.whl.metadata (18 kB)
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+ Collecting peft==0.17.0
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+ Downloading peft-0.17.0-py3-none-any.whl.metadata (14 kB)
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+ Requirement already satisfied: huggingface_hub<1.0,>=0.23 in /home/zeus/miniconda3/envs/cloudspace/lib/python3.12/site-packages (0.36.0)
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+ Collecting qwen-vl-utils
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+ Downloading qwen_vl_utils-0.0.14-py3-none-any.whl.metadata (9.0 kB)
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+ Collecting gradio==4.44.1
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+ Collecting gradio_client==1.3.0
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+ Downloading gradio_client-1.3.0-py3-none-any.whl.metadata (7.1 kB)
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+ Collecting pydantic==2.10.6
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+ Downloading pydantic-2.10.6-py3-none-any.whl.metadata (30 kB)
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+ Collecting faiss-cpu
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+ Collecting sentence-transformers
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+ Collecting regex!=2019.12.17 (from transformers==4.51.3)
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+ Requirement already satisfied: requests in /home/zeus/miniconda3/envs/cloudspace/lib/python3.12/site-packages (from transformers==4.51.3) (2.32.5)
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+ Collecting pillow
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+ Downloading pillow-10.4.0-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (9.2 kB)
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+ Collecting pydub (from gradio==4.44.1)
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+ Collecting shellingham>=1.3.0 (from typer<1.0,>=0.12->gradio==4.44.1)
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+ Downloading shellingham-1.5.4-py2.py3-none-any.whl.metadata (3.5 kB)
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+ Collecting av (from qwen-vl-utils)
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+ Requirement already satisfied: scikit-learn in /home/zeus/miniconda3/envs/cloudspace/lib/python3.12/site-packages (from sentence-transformers) (1.3.2)
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+ Downloading safetensors-0.6.2-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (485 kB)
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+ Downloading qwen_vl_utils-0.0.14-py3-none-any.whl (8.1 kB)
148
+ Downloading faiss_cpu-1.12.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (31.4 MB)
149
+ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 31.4/31.4 MB 155.9 MB/s 0:00:00
150
+
151
+ Downloading sentence_transformers-5.1.2-py3-none-any.whl (488 kB)
152
+ Downloading regex-2025.10.23-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (803 kB)
153
+ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 803.4/803.4 kB 85.9 MB/s 0:00:00
154
+
155
+ Downloading ruff-0.14.2-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.3 MB)
156
+ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.3/13.3 MB 158.7 MB/s 0:00:00
157
+
158
+ Downloading shellingham-1.5.4-py2.py3-none-any.whl (9.8 kB)
159
+ Downloading av-16.0.1-cp312-cp312-manylinux_2_28_x86_64.whl (40.5 MB)
160
+ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 40.5/40.5 MB 190.9 MB/s 0:00:00
161
+
162
+ Downloading ffmpy-0.6.4-py3-none-any.whl (5.6 kB)
163
+ Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)
164
+ Installing collected packages: pydub, websockets, tomlkit, shellingham, semantic-version, safetensors, ruff, regex, pydantic-core, pillow, orjson, markupsafe, importlib-resources, ffmpy, faiss-cpu, av, aiofiles, qwen-vl-utils, pydantic, typer, tokenizers, gradio_client, transformers, gradio, accelerate, sentence-transformers, peft
165
+ Attempting uninstall: websockets
166
+ Found existing installation: websockets 15.0.1
167
+ Uninstalling websockets-15.0.1:
168
+ Successfully uninstalled websockets-15.0.1
169
+ Attempting uninstall: pydantic-core
170
+ Found existing installation: pydantic_core 2.41.1
171
+ Uninstalling pydantic_core-2.41.1:
172
+ Successfully uninstalled pydantic_core-2.41.1
173
+ Attempting uninstall: pillow
174
+ Found existing installation: pillow 11.3.0
175
+ Uninstalling pillow-11.3.0:
176
+ Successfully uninstalled pillow-11.3.0
177
+ Attempting uninstall: markupsafe
178
+ Found existing installation: MarkupSafe 3.0.3
179
+ Uninstalling MarkupSafe-3.0.3:
180
+ Successfully uninstalled MarkupSafe-3.0.3
181
+ Attempting uninstall: pydantic
182
+ Found existing installation: pydantic 2.12.0
183
+ Uninstalling pydantic-2.12.0:
184
+ Successfully uninstalled pydantic-2.12.0
185
+
186
+
187
+ Successfully installed accelerate-0.32.1 aiofiles-23.2.1 av-16.0.1 faiss-cpu-1.12.0 ffmpy-0.6.4 gradio-4.44.1 gradio_client-1.3.0 importlib-resources-6.5.2 markupsafe-2.1.5 orjson-3.11.4 peft-0.17.0 pillow-10.4.0 pydantic-2.10.6 pydantic-core-2.27.2 pydub-0.25.1 qwen-vl-utils-0.0.14 regex-2025.10.23 ruff-0.14.2 safetensors-0.6.2 semantic-version-2.10.0 sentence-transformers-5.1.2 shellingham-1.5.4 tokenizers-0.21.4 tomlkit-0.12.0 transformers-4.51.3 typer-0.20.0 websockets-12.0
=0.4.3 ADDED
File without changes
app.py CHANGED
@@ -5,11 +5,12 @@ import os
5
  import spaces
6
  import torch
7
  import gradio as gr
 
8
  from PIL import Image
9
  from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
10
  from peft import PeftModel
11
  from qwen_vl_utils import process_vision_info # pip install qwen-vl-utils
12
- import transformers, accelerate, numpy as np
13
 
14
  print("VERSIONS:", transformers.__version__, accelerate.__version__, torch.__version__, np.__version__)
15
  os.environ["ACCELERATE_USE_SLOW_RETRIEVAL"] = "true"
@@ -44,6 +45,52 @@ model = PeftModel.from_pretrained(
44
 
45
  _model_on_gpu = False # once-per-session move
46
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
47
  # ---- Inference on GPU (ZeroGPU pattern) ----
48
  @spaces.GPU(duration=120)
49
  def generate_answer(image, question, temperature=0.7, top_p=0.95, max_tokens=256):
@@ -94,19 +141,31 @@ def generate_answer(image, question, temperature=0.7, top_p=0.95, max_tokens=256
94
  return processor.batch_decode(trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
95
 
96
  # ---- UI ----
97
- with gr.Blocks(title="PetBull‑7B‑VL (ZeroGPU, Qwen2.5‑VL)") as demo:
98
- gr.Markdown("## PetBull‑7B‑VL – Ask a Vet\nUpload a photo and/or type a question.")
99
- with gr.Row():
100
- with gr.Column():
101
- img_in = gr.Image(type="pil", label="Pet photo (optional)")
102
- txt_in = gr.Textbox(lines=3, placeholder="Describe the issue…")
103
- ask = gr.Button("Ask PetBull")
104
- temp = gr.Slider(0.1, 1.5, 0.7, label="Temperature")
105
- topp = gr.Slider(0.1, 1.0, 0.95, label="Top‑p")
106
- max_tok = gr.Slider(32, 512, 256, step=8, label="Max tokens")
107
- with gr.Column():
108
- answer = gr.Textbox(lines=12, label="Assistant", interactive=False)
109
-
110
- ask.click(generate_answer, inputs=[img_in, txt_in, temp, topp, max_tok], outputs=answer)
 
 
 
 
 
 
 
 
 
 
 
 
111
 
112
  demo.queue().launch(show_api=False, share=True)
 
5
  import spaces
6
  import torch
7
  import gradio as gr
8
+ import transformers, accelerate, numpy as np
9
  from PIL import Image
10
  from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
11
  from peft import PeftModel
12
  from qwen_vl_utils import process_vision_info # pip install qwen-vl-utils
13
+ from tools.retriever import search as product_search
14
 
15
  print("VERSIONS:", transformers.__version__, accelerate.__version__, torch.__version__, np.__version__)
16
  os.environ["ACCELERATE_USE_SLOW_RETRIEVAL"] = "true"
 
45
 
46
  _model_on_gpu = False # once-per-session move
47
 
48
+ def format_candidates_for_llm(cands, budget_twd=None):
49
+ lines = []
50
+ rank = 1
51
+ for c in cands:
52
+ price = f"{c.get('price_value')} {c.get('price_currency')}" if c.get("price_value") else "N/A"
53
+ if budget_twd and c.get("price_currency") == "TWD" and c.get("price_value") and c["price_value"] > budget_twd:
54
+ continue
55
+ lines.append(
56
+ f"{rank}. {c.get('brand_en','')}/{c.get('brand_zh','')} — {c.get('product_name_en') or c.get('product_name_zh')}\n"
57
+ f" • Category: {c.get('category_en') or c.get('category_zh')}\n"
58
+ f" • Price: {price}\n"
59
+ f" • Link: {c.get('source_url')}\n"
60
+ )
61
+ rank += 1
62
+ return "\n".join(lines) if lines else "No products matched."
63
+
64
+ DERMA_SAFETY = (
65
+ "Safety notes: For broken/infected skin, pregnancy/lactation, infants, "
66
+ "or if symptoms worsen—seek a qualified dermatologist. Patch-test first."
67
+ )
68
+
69
+ def recommend_products(query_text: str, budget_twd: int | None = None, k: int = 8):
70
+ # 1) Retrieve candidates
71
+ cands = product_search(query_text, k=k)
72
+
73
+ # 2) Build short grounded context
74
+ context = format_candidates_for_llm(cands, budget_twd=budget_twd)
75
+
76
+ # 3) Ask your LLM to pick & explain (plug into your existing generation path)
77
+ system = (
78
+ "You are DermalCare’s assistant. Recommend up to 3 products strictly "
79
+ "from the provided list. Include a one-line why-it-helps and a brief how-to-use. "
80
+ "Respect budget and do not invent products."
81
+ )
82
+ user = f"User need: {query_text}\nCandidate products:\n{context}\n{DERMA_SAFETY}"
83
+
84
+ # --- if you already have Qwen2-VL loaded as text generator, reuse it.
85
+ # Example skeleton (pseudo—replace with your app’s generate() function):
86
+ try:
87
+ # Replace this with your actual text-generation helper:
88
+ answer = f"(LLM picks here)\n\nContext:\n{context}"
89
+ except Exception as e:
90
+ answer = f"❌ Generation error: {e}\n\nHere are candidates:\n{context}"
91
+
92
+ return answer
93
+
94
  # ---- Inference on GPU (ZeroGPU pattern) ----
95
  @spaces.GPU(duration=120)
96
  def generate_answer(image, question, temperature=0.7, top_p=0.95, max_tokens=256):
 
141
  return processor.batch_decode(trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
142
 
143
  # ---- UI ----
144
+ with gr.Blocks(title="DermalCare - Pet & Skincare Assistant") as demo:
145
+ gr.Markdown("# DermalCare - Your AI Assistant for Pet Care and Skincare")
146
+
147
+ with gr.Tabs():
148
+ with gr.Tab("Pet Care"):
149
+ gr.Markdown("## PetBull‑7B‑VL – Ask a Vet\nUpload a photo and/or type a question.")
150
+ with gr.Row():
151
+ with gr.Column():
152
+ img_in = gr.Image(type="pil", label="Pet photo (optional)")
153
+ txt_in = gr.Textbox(lines=3, placeholder="Describe the issue…")
154
+ ask = gr.Button("Ask PetBull")
155
+ temp = gr.Slider(0.1, 1.5, 0.7, label="Temperature")
156
+ topp = gr.Slider(0.1, 1.0, 0.95, label="Top‑p")
157
+ max_tok = gr.Slider(32, 512, 256, step=8, label="Max tokens")
158
+ with gr.Column():
159
+ answer = gr.Textbox(lines=12, label="Assistant", interactive=False)
160
+
161
+ ask.click(generate_answer, inputs=[img_in, txt_in, temp, topp, max_tok], outputs=answer)
162
+
163
+ with gr.Tab("Product Recommender"):
164
+ need = gr.Textbox(label="Describe your need (中文或English)", lines=3,
165
+ value="敏感肌 想找溫和溫和拔乾的保濕潔面，預算 < TWD 400")
166
+ budget = gr.Number(label="Budget (TWD, optional)", value=400, precision=0)
167
+ btn = gr.Button("Recommend", variant="primary")
168
+ out = gr.Textbox(label="Recommendations", lines=14)
169
+ btn.click(fn=recommend_products, inputs=[need, budget], outputs=out, queue=True)
170
 
171
  demo.queue().launch(show_api=False, share=True)
data/cosmetics_full_en_zh.jsonl ADDED
The diff for this file is too large to render. See raw diff
 
indexes/cosmetics_meta.json ADDED
@@ -0,0 +1,2426 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "id": "a_derma_ad001_200ml",
4
+ "brand_en": "A-Derma",
5
+ "brand_zh": "艾芙美",
6
+ "product_name_en": "A‑Derma Biology Micellar Cleansing Milk",
7
+ "product_name_zh": "艾芙美新葉三效卸妝乳200ml",
8
+ "category_en": "Cleansing Milk",
9
+ "category_zh": "卸妝乳",
10
+ "price_value": null,
11
+ "price_currency": null,
12
+ "source_url": "https://www.s3.com.tw/TC/PDContent.aspx?yano=S00014931"
13
+ },
14
+ {
15
+ "id": "a_derma_ad022_400ml",
16
+ "brand_en": "A-Derma",
17
+ "brand_zh": "艾芙美",
18
+ "product_name_en": "A‑Derma Biology Micellar Cleansing Milk",
19
+ "product_name_zh": "艾芙美新葉三效卸妝乳400ml",
20
+ "category_en": "Cleansing Milk",
21
+ "category_zh": "卸妝乳",
22
+ "price_value": 589,
23
+ "price_currency": "TWD",
24
+ "source_url": "https://www.s3.com.tw/TC/PDContent.aspx?yano=S00014931"
25
+ },
26
+ {
27
+ "id": "a_derma_ad012_400ml",
28
+ "brand_en": "A-Derma",
29
+ "brand_zh": "艾芙美",
30
+ "product_name_en": null,
31
+ "product_name_zh": "艾芙美燕麥新葉益護佳乳液400ml",
32
+ "category_en": null,
33
+ "category_zh": null,
34
+ "price_value": null,
35
+ "price_currency": null,
36
+ "source_url": null
37
+ },
38
+ {
39
+ "id": "a_derma_ad026_400ml",
40
+ "brand_en": "A-Derma",
41
+ "brand_zh": "艾芙美",
42
+ "product_name_en": null,
43
+ "product_name_zh": "艾芙美燕麥新葉益護佳營養霜400ml",
44
+ "category_en": null,
45
+ "category_zh": null,
46
+ "price_value": null,
47
+ "price_currency": null,
48
+ "source_url": null
49
+ },
50
+ {
51
+ "id": "a_derma_ad031_400ml",
52
+ "brand_en": "A-Derma",
53
+ "brand_zh": "艾芙美",
54
+ "product_name_en": null,
55
+ "product_name_zh": "艾芙美燕麥新葉益護佳強護霜400ml",
56
+ "category_en": null,
57
+ "category_zh": null,
58
+ "price_value": null,
59
+ "price_currency": null,
60
+ "source_url": null
61
+ },
62
+ {
63
+ "id": "av_ne_av005_300ml",
64
+ "brand_en": "Avène",
65
+ "brand_zh": "雅漾",
66
+ "product_name_en": null,
67
+ "product_name_zh": "雅漾舒護活泉水300ml(雅漾噴霧)",
68
+ "category_en": null,
69
+ "category_zh": null,
70
+ "price_value": null,
71
+ "price_currency": null,
72
+ "source_url": null
73
+ },
74
+ {
75
+ "id": "av_ne_av068_40ml",
76
+ "brand_en": "Avène",
77
+ "brand_zh": "雅漾",
78
+ "product_name_en": null,
79
+ "product_name_zh": "雅漾修護保濕精華乳+-清爽40ml",
80
+ "category_en": null,
81
+ "category_zh": null,
82
+ "price_value": null,
83
+ "price_currency": null,
84
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+ "price_currency": null,
2016
+ "source_url": null
2017
+ },
2018
+ {
2019
+ "id": "the_body_shop_tbs107_30ml",
2020
+ "brand_en": "The Body Shop",
2021
+ "brand_zh": "美體小舖",
2022
+ "product_name_en": null,
2023
+ "product_name_zh": "美體小舖辣木籽更新護手霜30ml",
2024
+ "category_en": null,
2025
+ "category_zh": null,
2026
+ "price_value": null,
2027
+ "price_currency": null,
2028
+ "source_url": null
2029
+ },
2030
+ {
2031
+ "id": "the_body_shop_tbs071_200ml",
2032
+ "brand_en": "The Body Shop",
2033
+ "brand_zh": "美體小舖",
2034
+ "product_name_en": null,
2035
+ "product_name_zh": "美體小舖乳油木果修護身體滋養霜200ml",
2036
+ "category_en": null,
2037
+ "category_zh": null,
2038
+ "price_value": null,
2039
+ "price_currency": null,
2040
+ "source_url": null
2041
+ },
2042
+ {
2043
+ "id": "the_body_shop_tbs072_200ml",
2044
+ "brand_en": "The Body Shop",
2045
+ "brand_zh": "美體小舖",
2046
+ "product_name_en": null,
2047
+ "product_name_zh": "美體小舖辣木籽更新身體滋養霜200ml",
2048
+ "category_en": null,
2049
+ "category_zh": null,
2050
+ "price_value": null,
2051
+ "price_currency": null,
2052
+ "source_url": null
2053
+ },
2054
+ {
2055
+ "id": "the_body_shop_tbs074_200ml",
2056
+ "brand_en": "The Body Shop",
2057
+ "brand_zh": "美體小舖",
2058
+ "product_name_en": null,
2059
+ "product_name_zh": "美體小舖摩洛哥堅果油身體滋養霜200ml",
2060
+ "category_en": null,
2061
+ "category_zh": null,
2062
+ "price_value": null,
2063
+ "price_currency": null,
2064
+ "source_url": null
2065
+ },
2066
+ {
2067
+ "id": "the_body_shop_tbs081_200ml",
2068
+ "brand_en": "The Body Shop",
2069
+ "brand_zh": "美體小舖",
2070
+ "product_name_en": null,
2071
+ "product_name_zh": "美體小舖杏奶舒敏身體滋養霜200ml",
2072
+ "category_en": null,
2073
+ "category_zh": null,
2074
+ "price_value": null,
2075
+ "price_currency": null,
2076
+ "source_url": null
2077
+ },
2078
+ {
2079
+ "id": "the_body_shop_tbs082_200ml",
2080
+ "brand_en": "The Body Shop",
2081
+ "brand_zh": "美體小舖",
2082
+ "product_name_en": null,
2083
+ "product_name_zh": "美體小舖英皇玫瑰嫩膚身體滋養霜200ml",
2084
+ "category_en": null,
2085
+ "category_zh": null,
2086
+ "price_value": null,
2087
+ "price_currency": null,
2088
+ "source_url": null
2089
+ },
2090
+ {
2091
+ "id": "the_body_shop_tbs133_250ml",
2092
+ "brand_en": "The Body Shop",
2093
+ "brand_zh": "美體小舖",
2094
+ "product_name_en": null,
2095
+ "product_name_zh": "美體小舖英皇玫瑰嫩膚沐浴膠250ml",
2096
+ "category_en": null,
2097
+ "category_zh": null,
2098
+ "price_value": null,
2099
+ "price_currency": null,
2100
+ "source_url": null
2101
+ },
2102
+ {
2103
+ "id": "the_body_shop_tbs135_250ml",
2104
+ "brand_en": "The Body Shop",
2105
+ "brand_zh": "美體小舖",
2106
+ "product_name_en": null,
2107
+ "product_name_zh": "美體小舖草莓嫩白沐浴膠250ml",
2108
+ "category_en": null,
2109
+ "category_zh": null,
2110
+ "price_value": null,
2111
+ "price_currency": null,
2112
+ "source_url": null
2113
+ },
2114
+ {
2115
+ "id": "the_body_shop_tbs137_250ml",
2116
+ "brand_en": "The Body Shop",
2117
+ "brand_zh": "美體小舖",
2118
+ "product_name_en": null,
2119
+ "product_name_zh": "美體小舖杏奶舒敏滋養沐浴乳250ml",
2120
+ "category_en": null,
2121
+ "category_zh": null,
2122
+ "price_value": null,
2123
+ "price_currency": null,
2124
+ "source_url": null
2125
+ },
2126
+ {
2127
+ "id": "the_body_shop_tbs160_75ml",
2128
+ "brand_en": "The Body Shop",
2129
+ "brand_zh": "美體小舖",
2130
+ "product_name_en": null,
2131
+ "product_name_zh": "美體小舖漢蔘米糠元氣煥白面膜75ml",
2132
+ "category_en": null,
2133
+ "category_zh": null,
2134
+ "price_value": null,
2135
+ "price_currency": null,
2136
+ "source_url": null
2137
+ },
2138
+ {
2139
+ "id": "the_body_shop_tbs161_75ml",
2140
+ "brand_en": "The Body Shop",
2141
+ "brand_zh": "美體小舖",
2142
+ "product_name_en": null,
2143
+ "product_name_zh": "美體小舖日本抹茶防護抗老面膜75ml",
2144
+ "category_en": null,
2145
+ "category_zh": null,
2146
+ "price_value": null,
2147
+ "price_currency": null,
2148
+ "source_url": null
2149
+ },
2150
+ {
2151
+ "id": "the_body_shop_tbs162_75ml",
2152
+ "brand_en": "The Body Shop",
2153
+ "brand_zh": "美體小舖",
2154
+ "product_name_en": null,
2155
+ "product_name_zh": "美體小舖英皇玫瑰澎潤補水面膜75ml",
2156
+ "category_en": null,
2157
+ "category_zh": null,
2158
+ "price_value": null,
2159
+ "price_currency": null,
2160
+ "source_url": null
2161
+ },
2162
+ {
2163
+ "id": "the_body_shop_tbs054_400ml",
2164
+ "brand_en": "The Body Shop",
2165
+ "brand_zh": "美體小舖",
2166
+ "product_name_en": null,
2167
+ "product_name_zh": "美體小舖薑根鏗活調理洗髮精400ml",
2168
+ "category_en": null,
2169
+ "category_zh": null,
2170
+ "price_value": null,
2171
+ "price_currency": null,
2172
+ "source_url": null
2173
+ },
2174
+ {
2175
+ "id": "other_bi001_biorepair_75ml",
2176
+ "brand_en": "Other",
2177
+ "brand_zh": "其他品牌",
2178
+ "product_name_en": null,
2179
+ "product_name_zh": "Biorepair貝利達全效防護牙膏75ml(綠)",
2180
+ "category_en": null,
2181
+ "category_zh": null,
2182
+ "price_value": null,
2183
+ "price_currency": null,
2184
+ "source_url": null
2185
+ },
2186
+ {
2187
+ "id": "other_bi002_biorepair_75ml",
2188
+ "brand_en": "Other",
2189
+ "brand_zh": "其他品牌",
2190
+ "product_name_en": null,
2191
+ "product_name_zh": "Biorepair貝利達抗敏感牙膏75ml(紅)",
2192
+ "category_en": null,
2193
+ "category_zh": null,
2194
+ "price_value": null,
2195
+ "price_currency": null,
2196
+ "source_url": null
2197
+ },
2198
+ {
2199
+ "id": "other_bi003_biorepair_75ml",
2200
+ "brand_en": "Other",
2201
+ "brand_zh": "其他品牌",
2202
+ "product_name_en": null,
2203
+ "product_name_zh": "Biorepair貝利達護齦牙膏75ml(粉)",
2204
+ "category_en": null,
2205
+ "category_zh": null,
2206
+ "price_value": null,
2207
+ "price_currency": null,
2208
+ "source_url": null
2209
+ },
2210
+ {
2211
+ "id": "other_bi004_biorepair_75ml",
2212
+ "brand_en": "Other",
2213
+ "brand_zh": "其他品牌",
2214
+ "product_name_en": null,
2215
+ "product_name_zh": "Biorepair貝利達亮白修護牙膏75ml(白)",
2216
+ "category_en": null,
2217
+ "category_zh": null,
2218
+ "price_value": null,
2219
+ "price_currency": null,
2220
+ "source_url": null
2221
+ },
2222
+ {
2223
+ "id": "other_cd005_200ml",
2224
+ "brand_en": "Other",
2225
+ "brand_zh": "其他品牌",
2226
+ "product_name_en": null,
2227
+ "product_name_zh": "歐緹麗葡萄水噴霧200ml",
2228
+ "category_en": null,
2229
+ "category_zh": null,
2230
+ "price_value": null,
2231
+ "price_currency": null,
2232
+ "source_url": null
2233
+ },
2234
+ {
2235
+ "id": "other_cd008_100ml",
2236
+ "brand_en": "Other",
2237
+ "brand_zh": "其他品牌",
2238
+ "product_name_en": null,
2239
+ "product_name_zh": "歐緹麗葡萄籽活性化妝水100ml",
2240
+ "category_en": null,
2241
+ "category_zh": null,
2242
+ "price_value": null,
2243
+ "price_currency": null,
2244
+ "source_url": null
2245
+ },
2246
+ {
2247
+ "id": "other_dm001_40ml",
2248
+ "brand_en": "Other",
2249
+ "brand_zh": "其他品牌",
2250
+ "product_name_en": null,
2251
+ "product_name_zh": "朵瑪敏弱舒緩修復乳40ml",
2252
+ "category_en": null,
2253
+ "category_zh": null,
2254
+ "price_value": null,
2255
+ "price_currency": null,
2256
+ "source_url": null
2257
+ },
2258
+ {
2259
+ "id": "other_el001_embryolisse24_75ml",
2260
+ "brand_en": "Other",
2261
+ "brand_zh": "其他品牌",
2262
+ "product_name_en": null,
2263
+ "product_name_zh": "Embryolisse24小時神奇保濕乳75ml",
2264
+ "category_en": null,
2265
+ "category_zh": null,
2266
+ "price_value": null,
2267
+ "price_currency": null,
2268
+ "source_url": null
2269
+ },
2270
+ {
2271
+ "id": "other_el003_embryolisse24_75ml",
2272
+ "brand_en": "Other",
2273
+ "brand_zh": "其他品牌",
2274
+ "product_name_en": null,
2275
+ "product_name_zh": "Embryolisse24小時神奇保濕乳75ml-乾性肌",
2276
+ "category_en": null,
2277
+ "category_zh": null,
2278
+ "price_value": null,
2279
+ "price_currency": null,
2280
+ "source_url": null
2281
+ },
2282
+ {
2283
+ "id": "other_la002_75ml",
2284
+ "brand_en": "Other",
2285
+ "brand_zh": "其他品牌",
2286
+ "product_name_en": null,
2287
+ "product_name_zh": "黎瑞孕膚精華液75ml-兩入組",
2288
+ "category_en": null,
2289
+ "category_zh": null,
2290
+ "price_value": null,
2291
+ "price_currency": null,
2292
+ "source_url": null
2293
+ },
2294
+ {
2295
+ "id": "other_pg001_75ml",
2296
+ "brand_en": "Other",
2297
+ "brand_zh": "其他品牌",
2298
+ "product_name_en": null,
2299
+ "product_name_zh": "倍樂喜牙周保健牙膏75ml-粉",
2300
+ "category_en": null,
2301
+ "category_zh": null,
2302
+ "price_value": null,
2303
+ "price_currency": null,
2304
+ "source_url": null
2305
+ },
2306
+ {
2307
+ "id": "other_sg003_30ml",
2308
+ "brand_en": "Other",
2309
+ "brand_zh": "其他品牌",
2310
+ "product_name_en": null,
2311
+ "product_name_zh": "賽吉兒菁萃柔嫩乳霜30ml",
2312
+ "category_en": null,
2313
+ "category_zh": null,
2314
+ "price_value": null,
2315
+ "price_currency": null,
2316
+ "source_url": null
2317
+ },
2318
+ {
2319
+ "id": "other_sg005_500ml",
2320
+ "brand_en": "Other",
2321
+ "brand_zh": "其他品牌",
2322
+ "product_name_en": null,
2323
+ "product_name_zh": "賽吉兒菁萃潔浴凝露日用型500ml",
2324
+ "category_en": null,
2325
+ "category_zh": null,
2326
+ "price_value": null,
2327
+ "price_currency": null,
2328
+ "source_url": null
2329
+ },
2330
+ {
2331
+ "id": "other_sg009_250ml",
2332
+ "brand_en": "Other",
2333
+ "brand_zh": "其他品牌",
2334
+ "product_name_en": null,
2335
+ "product_name_zh": "賽吉兒菁萃婦潔凝露黃金女郎250ml",
2336
+ "category_en": null,
2337
+ "category_zh": null,
2338
+ "price_value": null,
2339
+ "price_currency": null,
2340
+ "source_url": null
2341
+ },
2342
+ {
2343
+ "id": "other_sg010_500ml",
2344
+ "brand_en": "Other",
2345
+ "brand_zh": "其他品牌",
2346
+ "product_name_en": null,
2347
+ "product_name_zh": "賽吉兒菁萃婦潔凝露500ml",
2348
+ "category_en": null,
2349
+ "category_zh": null,
2350
+ "price_value": null,
2351
+ "price_currency": null,
2352
+ "source_url": null
2353
+ },
2354
+ {
2355
+ "id": "other_ur012_300ml",
2356
+ "brand_en": "Other",
2357
+ "brand_zh": "其他品牌",
2358
+ "product_name_en": null,
2359
+ "product_name_zh": "優麗雅含氧等滲透壓活泉噴霧300ml",
2360
+ "category_en": null,
2361
+ "category_zh": null,
2362
+ "price_value": null,
2363
+ "price_currency": null,
2364
+ "source_url": null
2365
+ },
2366
+ {
2367
+ "id": "other_ur025_4g",
2368
+ "brand_en": "Other",
2369
+ "brand_zh": "其他品牌",
2370
+ "product_name_en": null,
2371
+ "product_name_zh": "優麗雅保濕護唇膏4g",
2372
+ "category_en": null,
2373
+ "category_zh": null,
2374
+ "price_value": null,
2375
+ "price_currency": null,
2376
+ "source_url": null
2377
+ },
2378
+ {
2379
+ "id": "other_vc004_300ml",
2380
+ "brand_en": "Other",
2381
+ "brand_zh": "其他品牌",
2382
+ "product_name_en": null,
2383
+ "product_name_zh": "薇姿溫泉舒緩噴霧300ml",
2384
+ "category_en": null,
2385
+ "category_zh": null,
2386
+ "price_value": null,
2387
+ "price_currency": null,
2388
+ "source_url": null
2389
+ },
2390
+ {
2391
+ "id": "other_vc013_r_360_15ml",
2392
+ "brand_en": "Other",
2393
+ "brand_zh": "其他品牌",
2394
+ "product_name_en": null,
2395
+ "product_name_zh": "薇姿R激光360度全能眼霜15ml",
2396
+ "category_en": null,
2397
+ "category_zh": null,
2398
+ "price_value": null,
2399
+ "price_currency": null,
2400
+ "source_url": null
2401
+ },
2402
+ {
2403
+ "id": "other_vc045_m89_50ml",
2404
+ "brand_en": "Other",
2405
+ "brand_zh": "其他品牌",
2406
+ "product_name_en": null,
2407
+ "product_name_zh": "薇姿M89火山能量微精華50ml",
2408
+ "category_en": null,
2409
+ "category_zh": null,
2410
+ "price_value": null,
2411
+ "price_currency": null,
2412
+ "source_url": null
2413
+ },
2414
+ {
2415
+ "id": "other_xp001_xhekpon_40ml",
2416
+ "brand_en": "Other",
2417
+ "brand_zh": "其他品牌",
2418
+ "product_name_en": null,
2419
+ "product_name_zh": "Xhekpon西班牙頸紋霜40ml",
2420
+ "category_en": null,
2421
+ "category_zh": null,
2422
+ "price_value": null,
2423
+ "price_currency": null,
2424
+ "source_url": null
2425
+ }
2426
+ ]
requirements.txt CHANGED
@@ -16,4 +16,7 @@ gradio_client==1.3.0
16
 
17
  pydantic==2.10.6
18
 
19
- pillow
 
 
 
 
16
 
17
  pydantic==2.10.6
18
 
19
+ pillow
20
+
21
+ faiss-cpu
22
+ sentence-transformers
tools/build_index.py ADDED
@@ -0,0 +1,80 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os, json, faiss, numpy as np
2
+ from pathlib import Path
3
+ from sentence_transformers import SentenceTransformer
4
+
5
+ DATA_CANDIDATES = [
6
+ "data/cosmetics_full_en_zh.jsonl",
7
+ "data/cosmetic_full_en_zh.jsonl", # user variant
8
+ "cosmetics_full_en_zh.jsonl",
9
+ "cosmetic_full_en_zh.jsonl",
10
+ ]
11
+
12
+ def find_dataset():
13
+ for p in DATA_CANDIDATES:
14
+ if os.path.exists(p):
15
+ return p
16
+ raise FileNotFoundError("Could not find dataset. Put it at data/cosmetics_full_en_zh.jsonl (or cosmetic_full_en_zh.jsonl).")
17
+
18
+ DATA_PATH = find_dataset()
19
+ INDEX_DIR = Path("indexes"); INDEX_DIR.mkdir(parents=True, exist_ok=True)
20
+ INDEX_PATH = INDEX_DIR / "cosmetics_faiss_ip.index"
21
+ META_PATH = INDEX_DIR / "cosmetics_meta.json"
22
+
23
+ print(f"✅ Using dataset: {DATA_PATH}")
24
+
25
+ # 1) Load
26
+ records = [json.loads(l) for l in open(DATA_PATH, "r", encoding="utf-8")]
27
+
28
+ # 2) Build the text to embed (EN + ZH)
29
+ def text_of(r):
30
+ return " \n".join([
31
+ str(r.get("product_name_en") or ""),
32
+ str(r.get("product_name_zh") or ""),
33
+ str(r.get("category_en") or ""),
34
+ str(r.get("category_zh") or ""),
35
+ str(r.get("description_en") or ""),
36
+ str(r.get("description_zh") or ""),
37
+ str(r.get("concerns_en") or ""),
38
+ str(r.get("concerns_zh") or ""),
39
+ str(r.get("key_ingredients_en") or ""),
40
+ str(r.get("key_ingredients_zh") or ""),
41
+ str(r.get("usage_en") or ""),
42
+ str(r.get("usage_zh") or ""),
43
+ ]).strip()
44
+
45
+ corpus = [text_of(r) for r in records]
46
+
47
+ # 3) Encode (multilingual)
48
+ model_id = os.environ.get("EMB_MODEL_ID", "intfloat/multilingual-e5-base")
49
+ print(f"🧠 Embedding with: {model_id}")
50
+ model = SentenceTransformer(model_id)
51
+ embs = model.encode(corpus, normalize_embeddings=True, batch_size=64)
52
+
53
+ # 4) FAISS index (cosine via inner product on normalized vectors)
54
+ d = embs.shape[1]
55
+ index = faiss.IndexFlatIP(d)
56
+ index.add(embs.astype("float32"))
57
+ faiss.write_index(index, str(INDEX_PATH))
58
+
59
+ # 5) Light metadata (quick lookup after search)
60
+ meta = []
61
+ for r in records:
62
+ meta.append({
63
+ "id": r.get("id"),
64
+ "brand_en": r.get("brand_en"),
65
+ "brand_zh": r.get("brand_zh"),
66
+ "product_name_en": r.get("product_name_en"),
67
+ "product_name_zh": r.get("product_name_zh"),
68
+ "category_en": r.get("category_en"),
69
+ "category_zh": r.get("category_zh"),
70
+ "price_value": r.get("price_value"),
71
+ "price_currency": r.get("price_currency"),
72
+ "source_url": r.get("source_url"),
73
+ })
74
+
75
+ with open(META_PATH, "w", encoding="utf-8") as f:
76
+ json.dump(meta, f, ensure_ascii=False, indent=2)
77
+
78
+ print("🎉 Built index + metadata")
79
+ print(f" - {INDEX_PATH}")
80
+ print(f" - {META_PATH}")
tools/reranker.py ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # /tools/reranker.py
2
+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
3
+ import torch
4
+
5
+ class Reranker:
6
+ def __init__(self, model_id="BAAI/bge-reranker-base"):
7
+ self.tokenizer = AutoTokenizer.from_pretrained(model_id)
8
+ self.model = AutoModelForSequenceClassification.from_pretrained(model_id)
9
+ self.model.eval()
10
+
11
+ @torch.no_grad()
12
+ def rerank(self, query: str, docs: list[str], top_k: int = 5):
13
+ """Re-rank a list of doc texts for a query and return sorted indices."""
14
+ pairs = [(query, d) for d in docs]
15
+ inputs = self.tokenizer(
16
+ pairs,
17
+ padding=True,
18
+ truncation=True,
19
+ return_tensors="pt",
20
+ max_length=512
21
+ )
22
+ scores = self.model(**inputs, return_dict=True).logits.squeeze(-1)
23
+ sorted_ids = torch.argsort(scores, descending=True)
24
+ top = sorted_ids[:top_k]
25
+ return top.tolist(), scores[top].tolist()
tools/retriever.py ADDED
@@ -0,0 +1,46 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os, json, faiss, numpy as np
2
+ from pathlib import Path
3
+ from sentence_transformers import SentenceTransformer
4
+ # from tools.retriever import search
5
+ # from tools.reranker import Reranker
6
+
7
+ INDEX_PATH = os.environ.get("INDEX_PATH", "indexes/cosmetics_faiss_ip.index")
8
+ META_PATH = os.environ.get("META_PATH", "indexes/cosmetics_meta.json")
9
+ EMB_MODEL = os.environ.get("EMB_MODEL_ID", "intfloat/multilingual-e5-base")
10
+
11
+ _embedder = None
12
+ _index = None
13
+ _meta = None
14
+ # reranker = Reranker()
15
+
16
+ # def refined_search(query, k_initial=20, k_final=5):
17
+ # # 1. retrieve coarse top-20
18
+ # cands = search(query, k=k_initial)
19
+ # texts = [f"{c['brand_en']} {c['product_name_en']} {c['description_en']}" for c in cands]
20
+
21
+ # # 2. re-rank with cross-encoder
22
+ # idxs, scores = reranker.rerank(query, texts, top_k=k_final)
23
+ # return [cands[i] | {"rerank_score": scores[j]} for j, i in enumerate(idxs)]
24
+
25
+ def _load():
26
+ global _embedder, _index, _meta
27
+ if _embedder is None:
28
+ _embedder = SentenceTransformer(EMB_MODEL)
29
+ if _index is None:
30
+ _index = faiss.read_index(INDEX_PATH)
31
+ if _meta is None:
32
+ _meta = json.load(open(META_PATH, "r", encoding="utf-8"))
33
+ return _embedder, _index, _meta
34
+
35
+ def search(query: str, k: int = 8):
36
+ emb, idx, meta = _load()
37
+ q = emb.encode([query], normalize_embeddings=True).astype("float32")
38
+ D, I = idx.search(q, k)
39
+ out = []
40
+ for score, i in zip(D[0], I[0]):
41
+ if int(i) < 0: # in case FAISS returns -1 for empty index
42
+ continue
43
+ m = meta[int(i)]
44
+ m["_score"] = float(score)
45
+ out.append(m)
46
+ return out