Spaces:
Sleeping
Sleeping
File size: 4,437 Bytes
90084cd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 |
# app/models/llm.py
import re
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from app.utils.config import DEVICE, QWEN_MODEL_ID
tokenizer = AutoTokenizer.from_pretrained(
QWEN_MODEL_ID,
trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
QWEN_MODEL_ID,
device_map="auto" if DEVICE == "cuda" else None,
torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32,
trust_remote_code=True
)
model.eval()
def strip_markdown(text: str) -> str:
text = re.sub(r"\*\*(.*?)\*\*", r"\1", text)
text = re.sub(r"\*(.*?)\*", r"\1", text)
text = re.sub(r"#+\s*", "", text)
text = re.sub(r"-\s+", "", text)
text = re.sub(r"`+", "", text)
text = text.replace("---", "")
return text.strip()
@torch.no_grad()
def infer_common_name(
species: str,
domain: str,
max_tokens: int = 16,
) -> str | None:
"""
Uses LLM to infer the most widely accepted English common name.
Returns None if no common name exists.
"""
messages = [
{
"role": "system",
"content": (
"You are a taxonomy assistant. "
"Return ONLY the most widely used English common name "
"for the given scientific name. "
"Do not explain or add extra text."
"dont respond null for common name make sure u provide a common name"
)
},
{
"role": "user",
"content": f"Scientific name: {species} ({domain})"
}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=max_tokens,
do_sample=False,
temperature=0.0,
pad_token_id=tokenizer.eos_token_id,
)
generated_ids = outputs[:, inputs.input_ids.shape[1]:]
response = tokenizer.decode(
generated_ids[0],
skip_special_tokens=True
).strip()
if not response or response.lower() == "none":
return None
return response
def _build_messages(
species: str,
confidence: float,
domain: str,
top_k: list | None = None,
):
alternatives = ""
if top_k:
alternatives = "\n".join(
[f"{x['species']} ({x['similarity']:.2f})" for x in top_k[1:]]
)
system_message = (
"You are a scientific biodiversity assistant. "
"Provide factual, neutral descriptions of species. "
"Do not mention instructions, rules, or formatting. "
"Do not use markdown or bullet points."
)
user_message = (
f"Species: {species}\n"
f"Confidence: {confidence:.2f}\n\n"
f"Alternative candidates:\n"
f"{alternatives if alternatives else 'None'}\n\n"
"Provide a factual description covering physical traits, "
"natural habitat and distribution, diet or ecological role, "
"conservation status, and relevant human interactions. "
)
return [
{"role": "system", "content": system_message},
{"role": "user", "content": user_message},
]
@torch.no_grad()
def explain_species(
species: str,
confidence: float,
domain: str,
top_k: list | None = None,
max_tokens: int = 512,
):
"""
Returns:
{
"common_name": str | None,
"description": str
}
"""
COMMON_NAME_MIN_CONFIDENCE = 0.01
common_name = None
if confidence >= COMMON_NAME_MIN_CONFIDENCE:
common_name = infer_common_name(species, domain)
messages = _build_messages(species, confidence, domain, top_k)
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(
**model_inputs,
max_new_tokens=max_tokens,
do_sample=False,
temperature=0.0,
pad_token_id=tokenizer.eos_token_id,
)
generated_ids = outputs[:, model_inputs.input_ids.shape[1]:]
response = tokenizer.decode(
generated_ids[0],
skip_special_tokens=True
)
return {
"common_name": common_name,
"description": strip_markdown(response),
}
|