Delete app.py

app.py

@@ -1,125 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[ ]:
-
-
-import gradio as gr
-import requests
-from bs4 import BeautifulSoup
-from transformers import pipeline
-import os
-
-# Function to extract text from the URL using requests
-def extract_text(url):
-    try:
-        # Enhanced headers to simulate a real browser request
-        headers = {
-            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
-            'Accept-Language': 'en-US,en;q=0.9',
-            'Accept-Encoding': 'gzip, deflate, br',
-            'Connection': 'keep-alive',
-            'Referer': 'https://www.mansionglobal.com/',  # Adding referer might help bypass restrictions
-            'Upgrade-Insecure-Requests': '1',  # Can sometimes help
-            'Cache-Control': 'max-age=0'
-        }
-
-        # Sending GET request with headers
-        response = requests.get(url, headers=headers)
-
-        # Raise an error for bad status codes
-        response.raise_for_status()
-
-        # Parse HTML and extract text
-        soup = BeautifulSoup(response.text, "html.parser")
-        text = ' '.join(soup.stripped_strings)
-        return text
-    except requests.exceptions.RequestException as e:
-        return f"Error extracting text from URL: {str(e)}"
-
-# Load Hugging Face model (for extracting named entities or QA)
-try:
-    ner_model = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english")
-except Exception as e:
-    ner_model = None
-    print(f"Error loading model: {str(e)}")
-
-# Function to extract information using Hugging Face model
-def extract_info_with_model(text):
-    if not ner_model:
-        return {
-            "Keytags": "Model loading failed.",
-            "Amenities": "Model loading failed.",
-            "Facilities": "Model loading failed.",
-            "Seller Name": "Model loading failed.",
-            "Location Details": "Model loading failed."
-        }
-    
-    try:
-        # Apply named entity recognition (NER) to extract entities from the text
-        ner_results = ner_model(text)
-
-        # Initialize variables
-        keytags = []
-        seller_name = ""
-        location_details = ""
-        amenities = ""
-        facilities = ""
-
-        # Search for relevant named entities
-        for entity in ner_results:
-            if entity['label'] == 'ORG':
-                keytags.append(entity['word'])  # Example: Company or key term (this can be changed)
-            elif entity['label'] == 'PERSON':
-                seller_name = entity['word']  # If a person is mentioned, consider it the seller name
-            elif entity['label'] == 'GPE':
-                location_details = entity['word']  # Geopolitical entity as location
-
-        # For amenities and facilities, you can modify the logic or use additional models (e.g., question-answering models)
-        amenities = "No amenities found"  # Placeholder for the amenities
-        facilities = "No facilities found"  # Placeholder for the facilities
-
-        return {
-            "Keytags": ", ".join(keytags) if keytags else "No keytags found",
-            "Amenities": amenities,
-            "Facilities": facilities,
-            "Seller Name": seller_name if seller_name else "No seller name found",
-            "Location Details": location_details if location_details else "No location details found"
-        }
-    except Exception as e:
-        return {
-            "Keytags": f"Error processing text: {str(e)}",
-            "Amenities": f"Error processing text: {str(e)}",
-            "Facilities": f"Error processing text: {str(e)}",
-            "Seller Name": f"Error processing text: {str(e)}",
-            "Location Details": f"Error processing text: {str(e)}"
-        }
-
-# Function to combine the extraction process (from URL + model processing)
-def get_info(url):
-    text = extract_text(url)
-    if "Error" in text:
-        return text, text, text, text, text  # Return the error message for all outputs
-    
-    extracted_info = extract_info_with_model(text)
-    
-    return (
-        extracted_info["Keytags"],
-        extracted_info["Amenities"],
-        extracted_info["Facilities"],
-        extracted_info["Seller Name"],
-        extracted_info["Location Details"]
-    )
-
-# Gradio Interface to allow user input and display output
-demo = gr.Interface(
-    fn=get_info,
-    inputs="text",  # Input is a URL
-    outputs=["text", "text", "text", "text", "text"],  # Outputs for each field (Keytags, Amenities, etc.)
-    title="Real Estate Info Extractor",
-    description="Extract Keytags, Amenities, Facilities, Seller Name, and Location Details from a real estate article URL."
-)
-
-if __name__ == "__main__":
-    demo.launch(show_api=False)
-