Create app.py

app.py

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+import gradio as gr
+import json
+import requests
+from bs4 import BeautifulSoup
+
+try:
+    from sentence_transformers import SentenceTransformer, util
+    from transformers import pipeline
+    MODULES_AVAILABLE = True
+except (ModuleNotFoundError, ImportError):
+    print("Warning: Required ML modules are missing. Running in fallback mode.")
+    MODULES_AVAILABLE = False
+
+class URLValidator:
+    def _init_(self):
+        if MODULES_AVAILABLE:
+            self.similarity_model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
+            self.sentiment_analyzer = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment")
+        else:
+            self.similarity_model = None
+            self.sentiment_analyzer = None
+
+    def fetch_page_content(self, url):
+        """Fetches webpage text content."""
+        headers = {
+            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+        }
+        try:
+            response = requests.get(url, headers=headers, 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:
+            return "ERROR: Unable to fetch webpage content."
+
+    def rate_url_validity(self, user_query, url):
+        """Validates URL credibility."""
+        content = self.fetch_page_content(url)
+        if not content:
+            return {
+                "status": "error",
+                "message": "ERROR: Failed to retrieve webpage content.",
+                "suggestion": "Try another URL or check if the website blocks bots."
+            }
+
+        if not MODULES_AVAILABLE:
+            return {
+                "status": "warning",
+                "message": "Machine learning models unavailable.",
+                "suggestion": "Install necessary ML modules."
+            }
+
+        similarity_score = int(util.pytorch_cos_sim(
+            self.similarity_model.encode(user_query),
+            self.similarity_model.encode(content)
+        ).item() * 100)
+
+        sentiment_result = self.sentiment_analyzer(content[:512])[0]
+        bias_score = 100 if sentiment_result["label"].upper() == "POSITIVE" else 50 if sentiment_result["label"].upper() == "NEUTRAL" else 30
+        final_score = round((0.5 * similarity_score) + (0.5 * bias_score), 2)
+
+        return {
+            "Content Relevance Score": f"{similarity_score} / 100",
+            "Bias Score": f"{bias_score} / 100",
+            "Final Validity Score": f"{final_score} / 100"
+        }
+
+# Sample queries and URLs
+sample_queries = [
+    "What are the benefits of a plant-based diet?"
+    "How does quantum computing work?"
+    "What are the causes of climate change?"
+    "Explain the basics of blockchain technology."
+    "How can I learn a new language quickly?"
+    "What are the symptoms of diabetes?"
+    "What are the best books for personal development?"
+    "How does 5G technology impact daily life?"
+    "What are the career opportunities in data science?"
+    "What are the ethical concerns surrounding AI?"
+]
+
+sample_urls = [
+    https://www.healthline.com/nutrition/plant-based-diet-guide
+    https://www.ibm.com/quantum-computing/what-is-quantum-computing
+    https://climate.nasa.gov/evidence/
+    https://www.investopedia.com/terms/b/blockchain.asp
+    https://www.duolingo.com/
+    https://www.diabetes.org/diabetes
+    https://jamesclear.com/book-summaries
+    https://www.qualcomm.com/news/onq/2020/01/10/what-5g-and-how-it-changing-everything
+    https://datasciencedegree.wisconsin.edu/data-science/what-do-data-scientists-do/
+    https://aiethicslab.com/
+]
+
+validator = URLValidator()
+
+def validate_url(user_query, url):
+    """Gradio function to validate URLs."""
+    result = validator.rate_url_validity(user_query, url)
+    return json.dumps(result, indent=2)
+
+with gr.Blocks() as demo:
+    gr.Markdown("# URL Credibility Validator")
+    gr.Markdown("### Validate the credibility of any webpage using AI")
+
+    user_query = gr.Dropdown(choices=sample_queries, label="Select a search query:")
+    url_input = gr.Dropdown(choices=sample_urls, label="Select a URL to validate:")
+    
+    output = gr.Textbox(label="Validation Results")  
+    
+    validate_button = gr.Button("Validate URL")
+    validate_button.click(validate_url, inputs=[user_query, url_input], outputs=output)
+
+if _name_ == "_main_":
+    demo.launch(server_name="0.0.0.0", server_port=7860)