Create app.py

app.py

@@ -0,0 +1,68 @@
+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)