Spaces:
Running
Running
File size: 3,506 Bytes
dacef8c d4c182e 7076d7a dacef8c d4c182e 8dd92a1 dacef8c 7076d7a dacef8c ca2193d d4c182e ca2193d d4c182e ca2193d d4c182e ca2193d d4c182e dacef8c ca2193d dacef8c 1b68a38 dacef8c fbef45a dacef8c bd9e29c dacef8c 19ac67f |
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 |
import lang
import requests
import json
import httpx
#import aiofiles
from imgsearch import search_img
from re import match, sub
from env import HOST_URL
from PIL import Image
from env import LLM_API_KEY
def remove_numbering(text):
"""Removes list-like numberings (e.g., 1., 2. etc.) from a string.
Args:
text: The string to process.
Returns:
The string with numberings removed.
"""
pattern = r"\d+\.\s?" # Matches one or more digits followed by a dot and optional space
return sub(pattern, "", text)
async def tldr(content, l=lang.VI_VN):
async with httpx.AsyncClient() as client:
_r = await client.post(
url="https://api.awanllm.com/v1/chat/completions",
headers={'Content-Type': 'application/json', 'Authorization': f'Bearer {LLM_API_KEY}'},
data=json.dumps({
"model": "Meta-Llama-3-8B-Instruct",
"messages": [
{"role": "user", "content": f"tl;dr in {l}: {content}"}
],
"presence_penalty":0.3,
"temperature":0.55
},
timeout=None)
)
_summary = _r.json()
# print(_summary)
return _summary['choices'][0]['message']['content'].split('\n',1)[-1].strip()
# import io
# import asyncio
# from functools import partial
# def optimize_image(data, _fn):
# with Image.open(io.BytesIO(data)) as img:
# img.save(f'{_fn}.webp', optimize=True)
# async def download_img(_url, _prefix=HOST_URL):
# _r = None
# async with httpx.AsyncClient() as client:
# _r = await client.get(url=_url, timeout=None)
# _fn = f'img_{str(hash(_url))}'
# if _r.status_code//100 == 2:
# _loop = asyncio.get_running_loop()
# _out = await _loop.run_in_executor(None, partial(optimize_image, _r.content, _fn))
# return f"{_prefix}/images/{_fn}.webp"
async def fetch_img_for_words(words: list[str], __url_prefix=None):
print("fetching images...")
_img_link = [search_img(r) for r in words]
return [(word,img) for (word, img) in zip(words, _img_link)]
async def get_definitions_from_words(words: list[str], summary: str = "", lang: str = lang.VI_VN):
print("running inferrence")
_r = requests.post(
url="https://api.awanllm.com/v1/chat/completions",
headers={'Content-Type': 'application/json', 'Authorization': f'Bearer {LLM_API_KEY}'},
data=json.dumps({
"model": "Meta-Llama-3-8B-Instruct",
"messages": [
{"role": "user", "content": f"{summary}. Give concise, easy-to-understand definitions for the following keywords: {'; '.join(words)}. DO NOT include the keywords inside their respective definition. Use {lang}."}
],
"presence_penalty":0.3,
"temperature":0.55
})
)
# print(_r.json()['choices'][0]['message']['content'].split('\n'))
print(_r.json()['choices'][0]['message']['content'].split('\n'))
rets = []
for _x in _r.json()['choices'][0]['message']['content'].split('\n'):
try:
k, v = _x.split(':')
k = k.replace('*','').strip()
k = remove_numbering(k)
v = v.strip()
if (v != '' and not "note: " in k.lower()):
rets.append((k, v))
except:
continue
return rets[:-1]
def get_imgs_from_words(words: list[str]):
pass
def classify_words(words: list[str], deep: bool = False):
pass
|