import gradio as gr import torch import spaces import logging from deep_translator import GoogleTranslator import pandas as pd from tqdm import tqdm import urllib from bs4 import BeautifulSoup # Configure logging to write messages to a file logging.basicConfig(filename='app.log', level=logging.ERROR) # Configuration max_seq_length = 2048 dtype = None # Auto detection of dtype load_in_4bit = True # Use 4-bit quantization to reduce memory usage # peft_model_name = "limitedonly41/website_qwen2_7b_2" # peft_model_name = "limitedonly41/website_mistral7b_v02" peft_model_name = "unsloth/mistral-7b-instruct-v0.3-bnb-4bit" # Initialize model and tokenizer variables model = None tokenizer = None 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 # Set your desired timeout value in seconds try: # Make the request using urllib req = urllib.request.Request(url, headers=headers) with urllib.request.urlopen(req, timeout=timeout) as response: response_content = response.read() soup = BeautifulSoup(response_content, 'html.parser', from_encoding=encoding) title = soup.find('title').text description = soup.find('meta', attrs={'name': 'description'}) description = description.get("content") if description and "content" in description.attrs else "" keywords = soup.find('meta', attrs={'name': 'keywords'}) keywords = keywords.get("content") if keywords and "content" in keywords.attrs else "" h1_all = ". ".join(h.text for h in soup.find_all('h1')) paragraphs_all = ". ".join(p.text for p in soup.find_all('p')) h2_all = ". ".join(h.text for h in soup.find_all('h2')) h3_all = ". ".join(h.text for h in soup.find_all('h3')) allthecontent = f"{title} {description} {h1_all} {h2_all} {h3_all} {paragraphs_all}"[:4999] # Clean up the text h1_all = h1_all.replace(r'\xa0', ' ').replace('\n', ' ').replace('\t', ' ') h2_all = h2_all.replace(r'\xa0', ' ').replace('\n', ' ').replace('\t', ' ') h3_all = h3_all.replace(r'\xa0', ' ').replace('\n', ' ').replace('\t', ' ') 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: print(url, e) return { 'url': url, 'title': None, 'description': None, 'keywords': None, 'h1': None, 'h2': None, 'h3': None, 'paragraphs': None, 'text': None } def main(urls): results = [] for url in tqdm(urls): result = fetch_data(url) results.append(result) return results @spaces.GPU() def classify_website(url): from unsloth import FastLanguageModel # Import moved to the top for model loading global model, tokenizer # Declare model and tokenizer as global variables if model is None or tokenizer is None: # Load the model and tokenizer during initialization (in the main process) model, tokenizer = FastLanguageModel.from_pretrained( model_name=peft_model_name, max_seq_length=max_seq_length, dtype=dtype, load_in_4bit=load_in_4bit, ) FastLanguageModel.for_inference(model) # Enable native 2x faster inference urls = [url] results_shop = main(urls) # Convert results to DataFrame df_result_train_more = pd.DataFrame(results_shop) text = df_result_train_more['text'][0] translated = GoogleTranslator(source='auto', target='en').translate(text[:4990]) 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) 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 # Create a Gradio interface iface = gr.Interface( fn=classify_website, inputs="text", outputs="text", title="Website Topic", description="Enter a URL to get a topic summary of the website content." ) # Launch the interface iface.launch() # 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 # import spaces # # 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 # model_name = "unsloth/mistral-7b-instruct-v0.3-bnb-4bit" # # Initialize model and tokenizer variables # model = None # tokenizer = None # @spaces.GPU() # def summarize_url(url): # global model, tokenizer # Declare model and tokenizer as global variables # # Load the model # max_seq_length = 2048 # dtype = None # load_in_4bit = True # if model is None or tokenizer is None: # from unsloth import FastLanguageModel # # Load the model and tokenizer # model, tokenizer = FastLanguageModel.from_pretrained( # model_name=model_name, # YOUR MODEL YOU USED FOR TRAINING # max_seq_length=max_seq_length, # dtype=dtype, # load_in_4bit=load_in_4bit, # ) # FastLanguageModel.for_inference(model) # Enable native 2x faster inference # result = asyncio.run(fetch_data(url)) # text = concatenate_text(result) # translated_text = translate_text(text) # if len(translated_text) < 100: # return 'not scraped or short 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 # # # Create the Gradio interface within a `Blocks` context, like the working example # # with gr.Blocks() as demo: # # # Add title and description to the interface # # gr.HTML("

Website Summary Generator

") # # gr.HTML("

Enter a URL to get a one-word topic summary of the website content..

") # # # Define input and output elements # # with gr.Row(): # # prompt = gr.Textbox(label="Enter Website URL", placeholder="https://example.com") # # output_text = gr.Textbox(label="Topic", interactive=False) # # # Add the button to trigger the function # # submit = gr.Button("Classify") # # # Define the interaction between inputs and outputs # # submit.click(fn=summarize_url, inputs=prompt, outputs=output_text) # # # Add the `if __name__ == "__main__":` block to launch the interface # # if __name__ == "__main__": # # demo.launch() # # with gr as demo: # # # Define Gradio interface # # demo = demo.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." # # ) # # if __name__ == "__main__": # # demo.launch() # # Create a 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 interface # iface.launch()