import streamlit as st
import requests
from bs4 import BeautifulSoup
from transformers import pipeline
from sentence_transformers import SentenceTransformer, util

class URLValidator:
    def __init__(self):
        self.similarity_model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
        self.fake_news_classifier = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")
        self.sentiment_analyzer = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment")

    def fetch_page_content(self, 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:
            return ""

    def compute_similarity_score(self, user_query, content):
        if not content:
            return 0
        return int(util.pytorch_cos_sim(
            self.similarity_model.encode(user_query),
            self.similarity_model.encode(content)
        ).item() * 100)

    def detect_bias(self, content):
        if not content:
            return 50
        sentiment_result = self.sentiment_analyzer(content[:512])[0]
        return 100 if sentiment_result["label"] == "POSITIVE" else 50 if sentiment_result["label"] == "NEUTRAL" else 30

    def get_star_rating(self, score: float):
        stars = max(1, min(5, round(score / 20)))
        return stars, "⭐" * stars

    def generate_explanation(self, domain_trust, similarity_score, fact_check_score, bias_score, citation_score, final_score):
        reasons = []
        if domain_trust < 50:
            reasons.append("The source has low domain authority.")
        if similarity_score < 50:
            reasons.append("The content is not highly relevant to your query.")
        if fact_check_score < 50:
            reasons.append("Limited fact-checking verification found.")
        if bias_score < 50:
            reasons.append("Potential bias detected in the content.")
        if citation_score < 30:
            reasons.append("Few citations found for this content.")
        return " ".join(reasons) if reasons else "This source is highly credible and relevant."

    def rate_url_validity(self, user_query, url):
        content = self.fetch_page_content(url)
        similarity_score = self.compute_similarity_score(user_query, content)
        bias_score = self.detect_bias(content)
        
        domain_trust = 60  # Placeholder
        fact_check_score = 70  # Placeholder
        citation_score = 50  # Placeholder

        final_score = (0.3 * domain_trust) + (0.3 * similarity_score) + (0.2 * fact_check_score) + (0.1 * bias_score) + (0.1 * citation_score)

        stars, icon = self.get_star_rating(final_score)
        explanation = self.generate_explanation(domain_trust, similarity_score, fact_check_score, bias_score, citation_score, final_score)

        return {
            "Final Score": f"{final_score:.2f}%",
            "Star Rating": icon,
            "Explanation": explanation
        }

# Initialize validator
validator = URLValidator()

st.set_page_config(page_title="URL Credibility Checker", layout="centered")
st.title("🌐 URL Credibility Checker")
st.markdown("**Analyze the credibility of a website based on its content, trustworthiness, and relevance.**")

url = st.text_input("🔗 Enter URL")
query = st.text_input("🔍 Enter Search Query")

if st.button("✅ Check Credibility"):
    if url and query:
        with st.spinner("Analyzing credibility..."):
            result = validator.rate_url_validity(query, url)
            st.success("Analysis Complete!")
            st.metric(label="Final Score", value=result["Final Score"])
            st.write("**Star Rating:**", result["Star Rating"])
            st.markdown(f"**📌 Explanation:** {result['Explanation']}")
    else:
        st.warning("⚠️ Please enter both a URL and a search query.")