Delete app.py

app.py

@@ -1,121 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[ ]:
-
-
-import gradio as gr
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from bs4 import BeautifulSoup
-from transformers import pipeline
-import time
-
-# Set up Selenium with headless Chrome
-def setup_driver():
-    options = Options()
-    options.headless = True
-    driver = webdriver.Chrome(options=options)  # Make sure you have 'chromedriver' installed
-    return driver
-
-# Function to extract text from the URL using Selenium
-def extract_text(url):
-    try:
-        driver = setup_driver()
-        driver.get(url)
-        time.sleep(3)  # Wait for page to load completely
-        page_source = driver.page_source
-        driver.quit()
-
-        soup = BeautifulSoup(page_source, "html.parser")
-        text = ' '.join(soup.stripped_strings)
-        return text
-    except Exception 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)
-