app.py

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)
# Here we use a named entity recognition model, but you can use a question answering model if needed
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)

    # You can refine this based on the type of entity or information you want to extract
    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)
    # For now, we'll just return a placeholder for these
    amenities = "No amenities found"
    facilities = "No facilities found"

    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)
    return extracted_info

# 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)