Spaces:
Runtime error
Runtime error
File size: 4,924 Bytes
b288f4d 59fb33a b288f4d 59fb33a b288f4d |
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 |
import gradio as gr
import asyncio
import requests
from bs4 import BeautifulSoup
import pandas as pd
from tqdm import tqdm
import urllib
from deep_translator import GoogleTranslator
from unsloth import FastLanguageModel
import torch
import re
# Define helper functions
async def fetch_data(url):
headers = {
'Accept': '*/*',
'Accept-Language': 'ru-RU,ru;q=0.9,en-US;q=0.8,en;q=0.7',
'Connection': 'keep-alive',
'Referer': f'{url}',
'Sec-Fetch-Dest': 'empty',
'Sec-Fetch-Mode': 'cors',
'Sec-Fetch-Site': 'cross-site',
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36',
'sec-ch-ua': '"Google Chrome";v="125", "Chromium";v="125", "Not.A/Brand";v="24"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"macOS"',
}
encoding = 'utf-8'
timeout = 10
try:
def get_content():
req = urllib.request.Request(url, headers=headers)
with urllib.request.urlopen(req, timeout=timeout) as response:
return response.read()
response_content = await asyncio.get_event_loop().run_in_executor(None, get_content)
soup = BeautifulSoup(response_content, 'html.parser', from_encoding=encoding)
title = soup.find('title').text
description = soup.find('meta', attrs={'name': 'description'})
if description and "content" in description.attrs:
description = description.get("content")
else:
description = ""
keywords = soup.find('meta', attrs={'name': 'keywords'})
if keywords and "content" in keywords.attrs:
keywords = keywords.get("content")
else:
keywords = ""
h1_all = " ".join(h.text for h in soup.find_all('h1'))
h2_all = " ".join(h.text for h in soup.find_all('h2'))
h3_all = " ".join(h.text for h in soup.find_all('h3'))
paragraphs_all = " ".join(p.text for p in soup.find_all('p'))
allthecontent = f"{title} {description} {h1_all} {h2_all} {h3_all} {paragraphs_all}"
allthecontent = allthecontent[:4999]
return {
'url': url,
'title': title,
'description': description,
'keywords': keywords,
'h1': h1_all,
'h2': h2_all,
'h3': h3_all,
'paragraphs': paragraphs_all,
'text': allthecontent
}
except Exception as e:
return {
'url': url,
'title': None,
'description': None,
'keywords': None,
'h1': None,
'h2': None,
'h3': None,
'paragraphs': None,
'text': None
}
def concatenate_text(data):
text_parts = [str(data[col]) for col in ['url', 'title', 'description', 'keywords', 'h1', 'h2', 'h3'] if data[col]]
text = ' '.join(text_parts)
text = text.replace(r'\xa0', ' ').replace('\n', ' ').replace('\t', ' ')
text = re.sub(r'\s{2,}', ' ', text)
return text
def translate_text(text):
try:
text = text[:4990]
translated_text = GoogleTranslator(source='auto', target='en').translate(text)
return translated_text
except Exception as e:
print(f"An error occurred during translation: {e}")
return None
@spaces.GPU()
def summarize_url(url):
# Load the model
max_seq_length = 2048
dtype = None
load_in_4bit = True
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
max_seq_length=max_seq_length,
dtype=dtype,
load_in_4bit=load_in_4bit,
)
# Enable native 2x faster inference
FastLanguageModel.for_inference(model)
result = asyncio.run(fetch_data(url))
text = concatenate_text(result)
translated_text = translate_text(text)
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
Describe the website text into one word topic:
### Input:
{}
### Response:
"""
prompt = alpaca_prompt.format(translated_text)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(inputs.input_ids, max_new_tokens=64, use_cache=True)
summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
final_answer = summary.split("### Response:")[1].strip()
return final_answer
# Define Gradio interface
iface = gr.Interface(
fn=summarize_url,
inputs="text",
outputs="text",
title="Website Summary Generator",
description="Enter a URL to get a one-word topic summary of the website content."
)
# Launch the Gradio app
iface.launch()
|