Update app.py

app.py

@@ -1,92 +1,23 @@
-import requests
-from bs4 import BeautifulSoup
+import gradio as gr
 import pandas as pd
-import time
+from url_validation import URLValidator  # Import your validator class
 
-class URLValidator:
-    """
-    A production-ready URL validation class that evaluates the credibility of a webpage
-    using multiple factors: domain trust, content relevance, fact-checking, bias detection, and citations.
-    """
-
-    def __init__(self):
-        pass  # No external models used in this simplified version
-
-    def fetch_page_content(self, url: str) -> str:
-        """ Fetches and extracts text content from the given URL. """
-        try:
-            response = requests.get(url, timeout=10)
-            response.raise_for_status()
-            soup = BeautifulSoup(response.text, "html.parser")
-            return " ".join([p.text for p in soup.find_all("p")])
-        except requests.RequestException as e:
-            print(f"Error fetching URL: {url} - {e}")
-            return ""
-
-    def get_domain_trust(self, url: str) -> int:
-        """ Simulated function to assess domain trust. """
-        return (len(url) % 5) + 1  # Mock domain trust rating (1-5)
-
-    def compute_similarity_score(self, user_query: str, content: str) -> int:
-        """ Simulated function to compute similarity between user query and content. """
-        if not content:
-            return 1  # If no content, assign lowest rating
-        return (len(user_query) % 5) + 1  # Mock similarity rating (1-5)
-
-    def rate_url_validity(self, user_query: str, url: str) -> int:
-        """ Evaluates webpage credibility based on multiple scores. """
-        content = self.fetch_page_content(url)
-        domain_trust = self.get_domain_trust(url)
-        similarity_score = self.compute_similarity_score(user_query, content)
-
-        # Final function rating (mock logic)
-        func_rating = round((domain_trust + similarity_score) / 2)
-        return func_rating
-
-# Sample Queries and URLs
-sample_queries = [
-    "How does climate change impact global weather?",
-    "What are the latest advancements in AI?",
-    "How does diet influence mental health?",
-    "What are the effects of space travel on astronauts?",
-    "Is cryptocurrency a safe investment?",
-    "What are the advantages of renewable energy?",
-    "How does deep learning work?",
-    "What are the health risks of 5G technology?",
-    "Is intermittent fasting effective for weight loss?",
-    "How do electric vehicles compare to gas cars?"
-]
-
-sample_urls = [
-    "https://www.nationalgeographic.com/environment/article/climate-change",
-    "https://www.technologyreview.com/2023/05/01/latest-ai-advancements/",
-    "https://www.health.harvard.edu/mind-and-mood/foods-linked-to-better-brainpower",
-    "https://www.nasa.gov/hrp/long-term-health-risks-of-space-travel",
-    "https://www.investopedia.com/terms/c/cryptocurrency.asp",
-    "https://www.energy.gov/eere/renewable-energy",
-    "https://www.ibm.com/cloud/deep-learning",
-    "https://www.who.int/news-room/questions-and-answers/item/radiation-5g-mobile-networks-and-health",
-    "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167940/",
-    "https://www.tesla.com/blog/benefits-of-electric-vehicles"
-]
-
-# Initialize Validator
+# Instantiate Validator
 validator = URLValidator()
 
-# Prepare Data
-data_rows = []
-for query, url in zip(sample_queries, sample_urls):
-    func_rating = validator.rate_url_validity(query, url)
-    custom_rating = min(func_rating + 1, 5)  # Adjusted user rating, max 5
-    data_rows.append([query, url, func_rating, custom_rating])
-
-# Create DataFrame and Save to CSV
-csv_filename = "url_validation_results.csv"
-df = pd.DataFrame(data_rows, columns=["user_prompt", "url_to_check", "func_rating", "custom_rating"])
-df.to_csv(csv_filename, index=False)
-
-print(f"✅ CSV file '{csv_filename}' has been created successfully!")
-
-# Keep the app running so Hugging Face Space does not stop
-while True:
-    time.sleep(60)  # Keep running indefinitely
+# Define function for Gradio UI
+def validate_url(user_query, url_to_check):
+    result = validator.rate_url_validity(user_query, url_to_check)
+    return result
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=validate_url,
+    inputs=["text", "text"],
+    outputs="json",
+    title="URL Validator",
+    description="Enter a query and a URL to check its credibility."
+)
+
+# Launch the Gradio App
+iface.launch()