Create app.py

app.py

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+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()