Delete app.py

app.py

@@ -1,77 +0,0 @@
-import gradio as gr
-from urllib.request import urlopen, Request
-from bs4 import BeautifulSoup
-from transformers import pipeline
-import os
-
-# Function to extract text from the URL
-def extract_text(url):
-    req = Request(url, headers={'User-Agent': 'Mozilla/5.0'})
-    html = urlopen(req).read()
-    text = ' '.join(BeautifulSoup(html, "html.parser").stripped_strings)
-    return text
-
-# Load Hugging Face model (for extracting named entities or QA)
-ner_model = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english")
-
-# Function to extract information using Hugging Face model
-def extract_info_with_model(text):
-    # 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"
-    }
-
-# Function to combine the extraction process (from URL + model processing)
-def get_info(url):
-    text = extract_text(url)
-    extracted_info = extract_info_with_model(text)
-    
-    # Print debug to understand what's being returned
-    print(extracted_info)
-
-    # Ensure the information is returned in the expected format
-    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)