First Repo

api_wrapper.py

@@ -0,0 +1,184 @@
+# ==========================================
+# API WRAPPER FOR FLASK
+# ==========================================
+import sqlite3
+
+def run_fact_check_api(claim):
+    """
+    API-friendly version that returns structured data instead of printing.
+    Returns dict with evidence, NLI results, and metadata.
+    
+    Note: This is a simplified version for demo. For full functionality,
+    install all dependencies from requirements.txt
+    """
+    try:
+        # Try to import the model
+        from model import (
+            init_db, clear_db, embed_model, fetch_rss, fetch_gdelt, fetch_newsapi,
+            fetch_wikipedia, fetch_duckduckgo, build_faiss,
+            load_all_evidence, nli_model, FAISS_FILE
+        )
+        import faiss
+        
+        # Full implementation
+        init_db()
+        clear_db()
+
+        claim_emb = embed_model.encode([claim], normalize_embeddings=True)
+
+        # Fetch evidence from all sources
+        fetch_rss(claim_emb)
+        gdelt_count = fetch_gdelt(claim, claim_emb)
+        newsapi_count = fetch_newsapi(claim, claim_emb)
+        fetch_wikipedia(claim)
+
+        # Count evidence
+        with sqlite3.connect("evidence.db") as conn:
+            cur = conn.cursor()
+            cur.execute("SELECT COUNT(*) FROM evidence")
+            total_count = cur.fetchone()[0]
+
+        activate_fallback = False
+
+        if (gdelt_count + newsapi_count) == 0 or total_count < 3:
+            activate_fallback = True
+
+        faiss_ready = build_faiss()
+
+        if faiss_ready:
+            index = faiss.read_index(FAISS_FILE)
+            D, _ = index.search(claim_emb, 1)
+            if len(D) > 0 and len(D[0]) > 0 and D[0][0] < 0.30:
+                activate_fallback = True
+
+        if activate_fallback:
+            fetch_duckduckgo(claim, claim_emb)
+            faiss_ready = build_faiss()
+
+        if not faiss_ready:
+            return {
+                "success": False,
+                "error": "No relevant evidence found.",
+                "evidence": [],
+                "nli_results": []
+            }
+
+        index = faiss.read_index(FAISS_FILE)
+        D, I = index.search(claim_emb, 5)
+
+        rows = load_all_evidence()
+
+        # Build evidence list
+        evidence_list = []
+        for i, idx in enumerate(I[0]):
+            with sqlite3.connect("evidence.db") as conn:
+                cur = conn.cursor()
+                cur.execute("SELECT text, source FROM evidence WHERE id = ?", (rows[idx][0],))
+                result = cur.fetchone()
+                if result:
+                    evidence_list.append({
+                        "text": result[0],
+                        "source": result[1],
+                        "similarity": float(D[0][i])
+                    })
+
+        # Build NLI results
+        nli_results = []
+        for idx in I[0]:
+            evidence_text = rows[idx][1]
+            nli_input = f"{evidence_text} [SEP] {claim}"
+            result = nli_model(nli_input)
+            
+            # Extract label and score
+            if result and len(result) > 0:
+                nli_results.append({
+                    "evidence": evidence_text[:200],
+                    "label": result[0].get("label", "unknown"),
+                    "score": float(result[0].get("score", 0.0))
+                })
+
+        # Calculate Final Verdict
+        verdict = "Uncertain"
+        confidence = 0.0
+
+        if nli_results:
+            # Simple heuristic: averaged or max voting
+            entail_scores = [r['score'] for r in nli_results if r['label'] == 'entailment']
+            contra_scores = [r['score'] for r in nli_results if r['label'] == 'contradiction']
+            
+            avg_entail = sum(entail_scores) / len(entail_scores) if entail_scores else 0
+            avg_contra = sum(contra_scores) / len(contra_scores) if contra_scores else 0
+
+            if avg_entail > avg_contra and avg_entail > 0.4:
+                verdict = "True"
+                confidence = avg_entail
+            elif avg_contra > avg_entail and avg_contra > 0.4:
+                verdict = "False"
+                confidence = avg_contra
+            else:
+                verdict = "Mixture/Uncertain"
+                confidence = max(avg_entail, avg_contra)
+
+        return {
+            "success": True,
+            "claim": claim,
+            "verdict": verdict,
+            "confidence": round(confidence, 2),
+            "evidence": evidence_list,
+            "nli_results": nli_results,
+            "total_evidence": len(evidence_list)
+        }
+        
+    except ImportError as e:
+        print(f"DEBUG: ImportError in api_wrapper: {e}")
+        # Return demo data if dependencies are missing
+        return {
+            "success": True,
+            "claim": claim,
+            "evidence": [
+                {
+                    "text": "This is demo evidence from RSS feed. Install dependencies from requirements.txt for real fact-checking.",
+                    "source": "RSS",
+                    "similarity": 0.85
+                },
+                {
+                    "text": "This is demo evidence from GDELT. The full system searches multiple news sources and databases.",
+                    "source": "GDELT",
+                    "similarity": 0.78
+                },
+                {
+                    "text": "This is demo evidence from Wikipedia. Install all dependencies to enable real-time fact verification.",
+                    "source": "Wikipedia",
+                    "similarity": 0.72
+                }
+            ],
+            "nli_results": [
+                {
+                    "evidence": "Demo evidence showing entailment (supports the claim)",
+                    "label": "entailment",
+                    "score": 0.89
+                },
+                {
+                    "evidence": "Demo evidence showing neutral stance",
+                    "label": "neutral",
+                    "score": 0.65
+                },
+                {
+                    "evidence": "Demo evidence showing contradiction",
+                    "label": "contradiction",
+                    "score": 0.45
+                }
+            ],
+            "total_evidence": 3
+        }
+
+    except Exception as e:
+        print(f"DEBUG: General Exception in api_wrapper: {e}")
+        import traceback
+        traceback.print_exc()
+        return {
+            "success": False,
+            "error": str(e),
+            "evidence": [],
+            "nli_results": []
+        }

app.py

@@ -0,0 +1,72 @@
+from flask import Flask, render_template, request, jsonify, redirect, url_for, session
+from api_wrapper import run_fact_check_api
+import os
+
+app = Flask(__name__)
+app.secret_key = os.urandom(24)  # For session management
+
+# Configure logging
+import sys
+import logging
+logging.basicConfig(filename='app.log', level=logging.DEBUG)
+# Redirect stdout/stderr to logger
+class StreamToLogger(object):
+   def __init__(self, logger, log_level=logging.INFO):
+      self.logger = logger
+      self.log_level = log_level
+      self.linebuf = ''
+   def write(self, buf):
+      for line in buf.rstrip().splitlines():
+         self.logger.log(self.log_level, line.rstrip())
+   def flush(self):
+      pass
+sys.stdout = StreamToLogger(logging.getLogger('STDOUT'), logging.INFO)
+sys.stderr = StreamToLogger(logging.getLogger('STDERR'), logging.ERROR)
+
+@app.route('/')
+def index():
+    """Main landing page with claim submission form"""
+    return render_template('index.html')
+
+@app.route('/check', methods=['POST'])
+def check_claim():
+    """Process fact-check request"""
+    claim = request.form.get('claim', '').strip()
+    
+    if not claim:
+        return jsonify({
+            "success": False,
+            "error": "Claim cannot be empty"
+        }), 400
+    
+    # Run fact check
+    result = run_fact_check_api(claim)
+    
+    # Store result in session for results page
+    session['last_result'] = result
+    
+    return jsonify(result)
+
+@app.route('/results')
+def results():
+    """Display fact-check results"""
+    result = session.get('last_result')
+    
+    if not result:
+        return redirect(url_for('index'))
+    
+    return render_template('results.html', result=result)
+
+@app.errorhandler(404)
+def not_found(error):
+    return render_template('index.html'), 404
+
+@app.errorhandler(500)
+def internal_error(error):
+    return jsonify({
+        "success": False,
+        "error": "Internal server error"
+    }), 500
+
+if __name__ == '__main__':
+    app.run(debug=True, host='0.0.0.0', port=5000)

model.py

@@ -0,0 +1,316 @@
+# ==========================================
+# IMPORTS
+# ==========================================
+import os
+import requests
+import sqlite3
+import faiss
+import numpy as np
+import urllib.parse
+from bs4 import BeautifulSoup
+import feedparser
+from sentence_transformers import SentenceTransformer
+from transformers import pipeline
+
+# ==========================================
+# CONFIG
+# ==========================================
+DB_FILE = "evidence.db"
+FAISS_FILE = "faiss.index"
+
+embed_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+nli_model = pipeline(
+    "text-classification",
+    model="facebook/bart-large-mnli"
+)
+
+# ==========================================
+# DATABASE
+# ==========================================
+def init_db():
+    with sqlite3.connect(DB_FILE) as conn:
+        cur = conn.cursor()
+        cur.execute("""
+            CREATE TABLE IF NOT EXISTS evidence (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                text TEXT,
+                source TEXT
+            )
+        """)
+        conn.commit()
+
+def clear_db():
+    with sqlite3.connect(DB_FILE) as conn:
+        conn.execute("DELETE FROM evidence")
+        conn.commit()
+
+def save_evidence(text, source):
+    with sqlite3.connect(DB_FILE) as conn:
+        conn.execute(
+            "INSERT INTO evidence (text, source) VALUES (?, ?)",
+            (text, source)
+        )
+        conn.commit()
+
+def load_all_evidence():
+    with sqlite3.connect(DB_FILE) as conn:
+        rows = conn.execute("SELECT id, text FROM evidence").fetchall()
+    return rows
+
+# ==========================================
+# RELEVANCE CHECK
+# ==========================================
+def is_relevant(claim_emb, text, threshold=0.15):
+    emb = embed_model.encode([text], normalize_embeddings=True)
+    sim = float(np.dot(claim_emb, emb[0]))
+    print(f"[DEBUG] Checking relevance for: '{text[:50]}...' Score: {sim:.4f}")
+    return sim >= threshold
+
+# ==========================================
+# RSS FETCH
+# ==========================================
+def fetch_rss(claim_emb):
+    print("[RSS] Fetching...")
+    feeds = [
+        "http://feeds.bbci.co.uk/news/rss.xml",
+        "http://rss.cnn.com/rss/edition.rss",
+        "https://timesofindia.indiatimes.com/rss.cms",
+        "https://www.hindustantimes.com/feeds/rss/topstories.rss",
+        "https://cfo.economictimes.indiatimes.com/rss",
+        "https://www.business-standard.com/rss/",
+    ]
+    count = 0
+    for url in feeds:
+        try:
+            feed = feedparser.parse(url)
+            print(f"[RSS] Parsed {url}, found {len(feed.entries)} entries")
+            for entry in feed.entries[:5]:
+                title = entry.title
+                if len(title) > 50 and is_relevant(claim_emb, title):
+                    save_evidence(title, "RSS")
+                    count += 1
+        except Exception as e:
+            print(f"[RSS] Error parsing {url}: {e}")
+    print(f"[RSS] Saved {count} items.")
+
+# ==========================================
+# GDELT FETCH
+# ==========================================
+def fetch_gdelt(claim, claim_emb):
+    print("[GDELT] Fetching...")
+    url = "https://api.gdeltproject.org/api/v2/doc/doc"
+    params = {
+        "query": claim,
+        "mode": "ArtList",
+        "format": "json",
+        "maxrecords": 5
+    }
+
+    added = 0
+    try:
+        r = requests.get(url, params=params, timeout=10)
+        r.raise_for_status()
+        data = r.json()
+        articles = data.get("articles", [])
+        print(f"[GDELT] Found {len(articles)} articles")
+
+        for art in articles:
+            title = art.get("title", "")
+            if len(title) > 80 and is_relevant(claim_emb, title):
+                save_evidence(title, "GDELT")
+                added += 1
+    except Exception as e:
+        print("[WARNING] GDELT failed:", e)
+
+    print(f"[GDELT] Saved {added} items.")
+    return added
+
+# ==========================================
+# NEWS API FETCH
+# ==========================================
+def fetch_newsapi(claim, claim_emb):
+    print("[NewsAPI] Fetching...")
+    # Using provided key
+    api_key = "126afa219039487aa89acedca060c8fd"
+    url = "https://newsapi.org/v2/everything"
+    params = {
+        "q": claim,
+        "apiKey": api_key,
+        "language": "en",
+        "sortBy": "relevancy",
+        "pageSize": 5
+    }
+
+    added = 0
+    try:
+        r = requests.get(url, params=params, timeout=10)
+        data = r.json()
+        
+        if r.status_code != 200:
+            print(f"[WARNING] NewsAPI Error: {data.get('message', 'Unknown error')}")
+            return 0
+
+        articles = data.get("articles", [])
+        print(f"[NewsAPI] Found {len(articles)} articles")
+
+        for art in articles:
+            title = art.get("title", "")
+            description = art.get("description", "") or ""
+            content = f"{title}. {description}"
+            
+            # Using low threshold as confirmed previously
+            if len(content) > 50 and is_relevant(claim_emb, content, threshold=0.05):
+                save_evidence(content, f"NewsAPI: {art.get('source', {}).get('name', 'Unknown')}")
+                added += 1
+    except Exception as e:
+        print("[WARNING] NewsAPI failed:", e)
+
+    print(f"[NewsAPI] Saved {added} items.")
+    return added
+
+# ==========================================
+# WIKIPEDIA (REST API)
+# ==========================================
+def fetch_wikipedia(claim):
+    print("[Wikipedia] Fetching...")
+    try:
+        query = urllib.parse.quote(claim)
+        url = f"https://en.wikipedia.org/w/api.php?action=query&list=search&srsearch={query}&format=json"
+        # Add User-Agent to avoid 403 Forbidden
+        headers = {
+            "User-Agent": "FactCheckBot/1.0 (https://github.com/yourusername/factcheckbot; your-email@example.com)"
+        }
+        r = requests.get(url, headers=headers, timeout=10)
+        data = r.json()
+
+        results = data.get("query", {}).get("search", [])
+        print(f"[Wikipedia] Found {len(results)} search results")
+        
+        saved = 0
+        for result in results[:3]:
+            title = result["title"]
+            page_url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{urllib.parse.quote(title)}"
+            r2 = requests.get(page_url, headers=headers, timeout=5)
+            if r2.status_code == 200:
+                extract = r2.json().get("extract", "")
+                # Further relaxed length check and threshold
+                if len(extract) > 20:
+                    # Using a very low threshold to ensure WE GET SOMETHING
+                    if is_relevant(embed_model.encode([claim], normalize_embeddings=True), extract, threshold=0.05):
+                        save_evidence(extract, f"Wikipedia: {title}")
+                        saved += 1
+        print(f"[Wikipedia] Saved {saved} items.")
+
+    except Exception as e:
+        print("[WARNING] Wikipedia failed:", e)
+
+# ==========================================
+# DUCKDUCKGO FALLBACK
+# ==========================================
+def fetch_duckduckgo(claim, claim_emb):
+    print("[Fallback] DuckDuckGo activated...")
+    try:
+        query = urllib.parse.quote(claim)
+        url = f"https://duckduckgo.com/html/?q={query}"
+        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"
+        }
+        r = requests.get(url, headers=headers, timeout=10)
+        soup = BeautifulSoup(r.text, "html.parser")
+
+        results = soup.find_all("a", class_="result__a", limit=5)
+        print(f"[DuckDuckGo] Found {len(results)} results")
+        
+        saved = 0
+        for res in results:
+            text = res.get_text()
+            # drastically lower threshold for fallback
+            if len(text) > 30 and is_relevant(claim_emb, text, 0.05):
+                save_evidence(text, "DuckDuckGo")
+                saved += 1
+        print(f"[DuckDuckGo] Saved {saved} items")
+    except Exception as e:
+        print("[WARNING] DuckDuckGo failed:", e)
+
+# ==========================================
+# BUILD FAISS
+# ==========================================
+def build_faiss():
+    rows = load_all_evidence()
+    if not rows:
+        return False
+
+    texts = [row[1] for row in rows]
+    embeddings = embed_model.encode(texts, normalize_embeddings=True)
+
+    index = faiss.IndexFlatIP(embeddings.shape[1])
+    index.add(np.array(embeddings))
+
+    faiss.write_index(index, FAISS_FILE)
+    return True
+
+# ==========================================
+# MAIN PIPELINE
+# ==========================================
+def run_fact_check(claim):
+    print("\n[FACT CHECK]", claim)
+
+    init_db()
+    clear_db()
+
+    claim_emb = embed_model.encode([claim], normalize_embeddings=True)
+
+    fetch_rss(claim_emb)
+    gdelt_count = fetch_gdelt(claim, claim_emb)
+    fetch_wikipedia(claim)
+
+    with sqlite3.connect(DB_FILE) as conn:
+        total_count = conn.execute("SELECT COUNT(*) FROM evidence").fetchone()[0]
+
+    activate_fallback = (gdelt_count == 0 or total_count < 3)
+
+    if build_faiss():
+        if os.path.exists(FAISS_FILE):
+            index = faiss.read_index(FAISS_FILE)
+            D, _ = index.search(claim_emb, 1)
+            if len(D) > 0 and len(D[0]) > 0:
+                similarity = D[0][0]
+                if similarity < 0.30:
+                    activate_fallback = True
+
+    if activate_fallback:
+        fetch_duckduckgo(claim, claim_emb)
+        build_faiss()
+
+    if not os.path.exists(FAISS_FILE):
+        print("[ERROR] No evidence found.")
+        return
+
+    index = faiss.read_index(FAISS_FILE)
+    D, I = index.search(claim_emb, 5)
+    rows = load_all_evidence()
+
+    print("\n[EVIDENCE]")
+    for idx in I[0]:
+        if idx < len(rows):
+            print("-", rows[idx][1][:200])
+
+    print("\n[NLI RESULTS]")
+    for idx in I[0]:
+        if idx < len(rows):
+            evidence_text = rows[idx][1]
+            result = nli_model({
+                "premise": evidence_text,
+                "hypothesis": claim
+            })
+            print(result)
+
+# ==========================================
+# RUN
+# ==========================================
+if __name__ == "__main__":
+    claim = input("Enter claim: ").strip()
+    if claim:
+        run_fact_check(claim)
+    else:
+        print("Claim cannot be empty.")

reproduce_issue.py

@@ -0,0 +1,32 @@
+import requests
+import json
+import time
+
+url = "http://127.0.0.1:5000/check"
+data = {"claim": "The Earth is flat"}
+
+print(f"Sending request to {url}...")
+start_time = time.time()
+try:
+    response = requests.post(url, data=data)
+    end_time = time.time()
+    print(f"Response status: {response.status_code}")
+    print(f"Time taken: {end_time - start_time:.2f} seconds")
+    
+    try:
+        json_resp = response.json()
+        print(json.dumps(json_resp, indent=2))
+        
+        # Check if it's demo data
+        evidence = json_resp.get("evidence", [])
+        if evidence and "demo evidence" in evidence[0].get("text", "").lower():
+            print("\n[!] DETECTED DEMO DATA: The backend failed to load the real model.")
+        else:
+            print("\n[+] Real model execution (or empty result).")
+            
+    except Exception as e:
+        print(f"Failed to parse JSON: {e}")
+        print(response.text)
+        
+except Exception as e:
+    print(f"Request failed: {e}")

reproduce_issue_v2.py

@@ -0,0 +1,31 @@
+import requests
+import json
+import time
+
+url = "http://127.0.0.1:5000/check"
+# Test cases
+claims = [
+    "The Earth revolves around the Sun",
+    "The Earth is flat"
+]
+
+for claim in claims:
+    print(f"\nTesting Claim: '{claim}'")
+    data = {"claim": claim}
+    
+    try:
+        response = requests.post(url, data=data)
+        json_resp = response.json()
+        
+        print(f"Verdict: {json_resp.get('verdict')}")
+        print(f"Confidence: {json_resp.get('confidence')}")
+        
+        evidence = json_resp.get('evidence', [])
+        print(f"Evidence Found: {len(evidence)}")
+        
+        # Print sources
+        sources = set([e.get('source', 'Unknown') for e in evidence])
+        print(f"Sources: {sources}")
+        
+    except Exception as e:
+        print(f"Error: {e}")

requirements.txt

@@ -0,0 +1,9 @@
+flask>=3.0.0
+requests>=2.31.0
+beautifulsoup4>=4.12.2
+feedparser>=6.0.10
+sentence-transformers>=2.2.2
+transformers>=4.35.0
+faiss-cpu>=1.9.0
+numpy>=1.24.3
+torch>=2.1.0

static/style.css

@@ -0,0 +1,859 @@
+/* ==========================================
+   RESET & BASE STYLES
+   ========================================== */
+* {
+    margin: 0;
+    padding: 0;
+    box-sizing: border-box;
+}
+
+:root {
+    /* Color Palette */
+    --primary: #6366f1;
+    --primary-dark: #4f46e5;
+    --primary-light: #818cf8;
+    --accent: #f59e0b;
+    --success: #10b981;
+    --error: #ef4444;
+    --warning: #f59e0b;
+    --neutral: #6b7280;
+
+    /* Gradients */
+    --gradient-primary: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    --gradient-accent: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);
+    --gradient-success: linear-gradient(135deg, #4ade80 0%, #22c55e 100%);
+    --gradient-bg: linear-gradient(135deg, #667eea 0%, #764ba2 50%, #f093fb 100%);
+
+    /* Backgrounds */
+    --bg-primary: #0f172a;
+    --bg-secondary: #1e293b;
+    --bg-card: rgba(255, 255, 255, 0.05);
+    --bg-glass: rgba(255, 255, 255, 0.1);
+
+    /* Text */
+    --text-primary: #f8fafc;
+    --text-secondary: #cbd5e1;
+    --text-muted: #94a3b8;
+
+    /* Shadows */
+    --shadow-sm: 0 2px 8px rgba(0, 0, 0, 0.1);
+    --shadow-md: 0 4px 16px rgba(0, 0, 0, 0.2);
+    --shadow-lg: 0 8px 32px rgba(0, 0, 0, 0.3);
+    --shadow-glow: 0 0 40px rgba(102, 126, 234, 0.4);
+
+    /* Spacing */
+    --spacing-xs: 0.5rem;
+    --spacing-sm: 1rem;
+    --spacing-md: 1.5rem;
+    --spacing-lg: 2rem;
+    --spacing-xl: 3rem;
+
+    /* Border Radius */
+    --radius-sm: 8px;
+    --radius-md: 12px;
+    --radius-lg: 16px;
+    --radius-xl: 24px;
+
+    /* Transitions */
+    --transition-fast: 0.2s ease;
+    --transition-normal: 0.3s ease;
+    --transition-slow: 0.5s ease;
+}
+
+body {
+    font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif;
+    background: var(--bg-primary);
+    color: var(--text-primary);
+    line-height: 1.6;
+    min-height: 100vh;
+    overflow-x: hidden;
+    position: relative;
+}
+
+/* ==========================================
+   GRADIENT BACKGROUND
+   ========================================== */
+.gradient-bg {
+    position: fixed;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    background: var(--gradient-bg);
+    opacity: 0.15;
+    z-index: -1;
+    animation: gradientShift 15s ease infinite;
+}
+
+@keyframes gradientShift {
+
+    0%,
+    100% {
+        transform: scale(1) rotate(0deg);
+    }
+
+    50% {
+        transform: scale(1.1) rotate(5deg);
+    }
+}
+
+/* ==========================================
+   CONTAINER & LAYOUT
+   ========================================== */
+.container {
+    max-width: 1200px;
+    margin: 0 auto;
+    padding: var(--spacing-lg);
+    position: relative;
+    z-index: 1;
+}
+
+/* ==========================================
+   HEADER & HERO
+   ========================================== */
+.hero {
+    text-align: center;
+    padding: var(--spacing-xl) 0;
+    margin-bottom: var(--spacing-lg);
+}
+
+.logo {
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: var(--spacing-sm);
+    margin-bottom: var(--spacing-md);
+}
+
+.logo-icon {
+    font-size: 3rem;
+    animation: float 3s ease-in-out infinite;
+}
+
+@keyframes float {
+
+    0%,
+    100% {
+        transform: translateY(0);
+    }
+
+    50% {
+        transform: translateY(-10px);
+    }
+}
+
+.logo h1 {
+    font-size: 3.5rem;
+    font-weight: 800;
+    background: var(--gradient-primary);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    background-clip: text;
+    letter-spacing: -0.02em;
+}
+
+.accent {
+    background: var(--gradient-accent);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    background-clip: text;
+}
+
+.tagline {
+    font-size: 1.25rem;
+    color: var(--text-secondary);
+    font-weight: 300;
+}
+
+/* ==========================================
+   CARDS
+   ========================================== */
+.card {
+    background: var(--bg-card);
+    border-radius: var(--radius-lg);
+    padding: var(--spacing-lg);
+    margin-bottom: var(--spacing-lg);
+    border: 1px solid rgba(255, 255, 255, 0.1);
+    transition: all var(--transition-normal);
+}
+
+.glass-card {
+    background: var(--bg-glass);
+    backdrop-filter: blur(20px);
+    -webkit-backdrop-filter: blur(20px);
+    box-shadow: var(--shadow-lg);
+}
+
+.card:hover {
+    transform: translateY(-2px);
+    box-shadow: var(--shadow-glow);
+    border-color: rgba(102, 126, 234, 0.3);
+}
+
+.card-title {
+    font-size: 2rem;
+    font-weight: 700;
+    margin-bottom: var(--spacing-sm);
+    background: var(--gradient-primary);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    background-clip: text;
+}
+
+.card-subtitle {
+    color: var(--text-secondary);
+    margin-bottom: var(--spacing-lg);
+    font-size: 1.05rem;
+}
+
+/* ==========================================
+   FORM ELEMENTS
+   ========================================== */
+.claim-form {
+    margin-bottom: var(--spacing-lg);
+}
+
+.input-group {
+    position: relative;
+    margin-bottom: var(--spacing-md);
+}
+
+textarea {
+    width: 100%;
+    padding: var(--spacing-md);
+    background: rgba(255, 255, 255, 0.05);
+    border: 2px solid rgba(255, 255, 255, 0.1);
+    border-radius: var(--radius-md);
+    color: var(--text-primary);
+    font-family: inherit;
+    font-size: 1.05rem;
+    resize: vertical;
+    transition: all var(--transition-normal);
+}
+
+textarea:focus {
+    outline: none;
+    border-color: var(--primary);
+    background: rgba(255, 255, 255, 0.08);
+    box-shadow: 0 0 0 4px rgba(99, 102, 241, 0.1);
+}
+
+textarea::placeholder {
+    color: var(--text-muted);
+}
+
+.char-counter {
+    position: absolute;
+    bottom: var(--spacing-xs);
+    right: var(--spacing-sm);
+    color: var(--text-muted);
+    font-size: 0.875rem;
+}
+
+/* ==========================================
+   BUTTONS
+   ========================================== */
+.btn {
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    gap: var(--spacing-sm);
+    padding: var(--spacing-md) var(--spacing-xl);
+    font-size: 1.1rem;
+    font-weight: 600;
+    border: none;
+    border-radius: var(--radius-md);
+    cursor: pointer;
+    transition: all var(--transition-normal);
+    text-decoration: none;
+    font-family: inherit;
+}
+
+.btn-primary {
+    background: var(--gradient-primary);
+    color: white;
+    box-shadow: var(--shadow-md);
+}
+
+.btn-primary:hover {
+    transform: translateY(-2px);
+    box-shadow: var(--shadow-glow);
+}
+
+.btn-primary:active {
+    transform: translateY(0);
+}
+
+.btn-icon {
+    font-size: 1.5rem;
+    transition: transform var(--transition-fast);
+}
+
+.btn:hover .btn-icon {
+    transform: translateX(4px);
+}
+
+/* ==========================================
+   LOADING STATE
+   ========================================== */
+.loading-state {
+    text-align: center;
+    padding: var(--spacing-xl);
+}
+
+.spinner {
+    width: 60px;
+    height: 60px;
+    margin: 0 auto var(--spacing-md);
+    border: 4px solid rgba(255, 255, 255, 0.1);
+    border-top-color: var(--primary);
+    border-radius: 50%;
+    animation: spin 1s linear infinite;
+}
+
+@keyframes spin {
+    to {
+        transform: rotate(360deg);
+    }
+}
+
+.loading-text {
+    font-size: 1.2rem;
+    color: var(--text-secondary);
+    margin-bottom: var(--spacing-lg);
+}
+
+.loading-steps {
+    display: flex;
+    flex-direction: column;
+    gap: var(--spacing-sm);
+    max-width: 400px;
+    margin: 0 auto;
+}
+
+.step {
+    padding: var(--spacing-sm) var(--spacing-md);
+    background: rgba(255, 255, 255, 0.03);
+    border-radius: var(--radius-sm);
+    color: var(--text-muted);
+    transition: all var(--transition-normal);
+    opacity: 0.5;
+}
+
+.step.active {
+    background: rgba(99, 102, 241, 0.2);
+    color: var(--primary-light);
+    opacity: 1;
+    transform: translateX(8px);
+}
+
+/* ==========================================
+   ERROR STATE
+   ========================================== */
+.error-state {
+    text-align: center;
+    padding: var(--spacing-lg);
+    background: rgba(239, 68, 68, 0.1);
+    border: 1px solid rgba(239, 68, 68, 0.3);
+    border-radius: var(--radius-md);
+}
+
+.error-icon {
+    font-size: 2rem;
+    display: block;
+    margin-bottom: var(--spacing-sm);
+}
+
+.error-text {
+    color: #fca5a5;
+    font-size: 1.05rem;
+}
+
+/* ==========================================
+   FEATURES SECTION
+   ========================================== */
+.features {
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(280px, 1fr));
+    gap: var(--spacing-lg);
+    margin-top: var(--spacing-xl);
+}
+
+.feature {
+    text-align: center;
+    padding: var(--spacing-lg);
+    background: rgba(255, 255, 255, 0.03);
+    border-radius: var(--radius-md);
+    border: 1px solid rgba(255, 255, 255, 0.05);
+    transition: all var(--transition-normal);
+}
+
+.feature:hover {
+    background: rgba(255, 255, 255, 0.05);
+    transform: translateY(-4px);
+}
+
+.feature-icon {
+    font-size: 3rem;
+    display: block;
+    margin-bottom: var(--spacing-sm);
+}
+
+.feature h3 {
+    font-size: 1.3rem;
+    margin-bottom: var(--spacing-xs);
+    color: var(--text-primary);
+}
+
+.feature p {
+    color: var(--text-secondary);
+    font-size: 0.95rem;
+}
+
+/* ==========================================
+   RESULTS PAGE
+   ========================================== */
+.header-small {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    padding: var(--spacing-md) 0;
+    margin-bottom: var(--spacing-lg);
+}
+
+.back-link {
+    color: var(--text-secondary);
+    text-decoration: none;
+    font-weight: 500;
+    transition: color var(--transition-fast);
+}
+
+.back-link:hover {
+    color: var(--primary-light);
+}
+
+.logo-small {
+    display: flex;
+    align-items: center;
+    gap: var(--spacing-xs);
+}
+
+.logo-small h1 {
+    font-size: 1.5rem;
+    font-weight: 700;
+    background: var(--gradient-primary);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    background-clip: text;
+}
+
+.logo-small .logo-icon {
+    font-size: 1.5rem;
+}
+
+.claim-card {
+    margin-bottom: var(--spacing-xl);
+}
+
+.card-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: var(--spacing-md);
+}
+
+.card-header h2 {
+    font-size: 1.5rem;
+    color: var(--text-primary);
+}
+
+.badge {
+    padding: var(--spacing-xs) var(--spacing-md);
+    border-radius: var(--radius-sm);
+    font-size: 0.875rem;
+    font-weight: 600;
+}
+
+.badge-success {
+    background: rgba(16, 185, 129, 0.2);
+    color: #6ee7b7;
+}
+
+.claim-text {
+    font-size: 1.3rem;
+    line-height: 1.8;
+    color: var(--text-primary);
+    margin-bottom: var(--spacing-md);
+    font-weight: 500;
+}
+
+.meta-info {
+    display: flex;
+    gap: var(--spacing-lg);
+    color: var(--text-secondary);
+    font-size: 0.95rem;
+}
+
+.meta-item strong {
+    color: var(--primary-light);
+}
+
+/* ==========================================
+   SECTIONS
+   ========================================== */
+.section {
+    margin-bottom: var(--spacing-xl);
+}
+
+.section-title {
+    display: flex;
+    align-items: center;
+    gap: var(--spacing-sm);
+    font-size: 1.8rem;
+    margin-bottom: var(--spacing-sm);
+    color: var(--text-primary);
+}
+
+.section-icon {
+    font-size: 2rem;
+}
+
+.section-subtitle {
+    color: var(--text-secondary);
+    margin-bottom: var(--spacing-lg);
+}
+
+/* ==========================================
+   EVIDENCE CARDS
+   ========================================== */
+.evidence-grid {
+    display: grid;
+    gap: var(--spacing-md);
+}
+
+.evidence-card {
+    animation: slideIn 0.5s ease forwards;
+    opacity: 0;
+}
+
+@keyframes slideIn {
+    from {
+        opacity: 0;
+        transform: translateY(20px);
+    }
+
+    to {
+        opacity: 1;
+        transform: translateY(0);
+    }
+}
+
+.evidence-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: var(--spacing-sm);
+}
+
+.source-badge {
+    padding: 4px 12px;
+    border-radius: var(--radius-sm);
+    font-size: 0.75rem;
+    font-weight: 700;
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+}
+
+.source-rss {
+    background: rgba(245, 158, 11, 0.2);
+    color: #fbbf24;
+}
+
+.source-gdelt {
+    background: rgba(59, 130, 246, 0.2);
+    color: #93c5fd;
+}
+
+.source-wikipedia {
+    background: rgba(139, 92, 246, 0.2);
+    color: #c4b5fd;
+}
+
+.source-duckduckgo {
+    background: rgba(236, 72, 153, 0.2);
+    color: #f9a8d4;
+}
+
+.similarity-score {
+    color: var(--text-muted);
+    font-size: 0.875rem;
+    font-weight: 600;
+}
+
+.evidence-text {
+    color: var(--text-secondary);
+    line-height: 1.7;
+}
+
+/* ==========================================
+   NLI CARDS
+   ========================================== */
+.nli-grid {
+    display: grid;
+    gap: var(--spacing-md);
+}
+
+.nli-card {
+    animation: slideIn 0.5s ease forwards;
+    opacity: 0;
+    border-left: 4px solid;
+}
+
+.nli-card.nli-entailment {
+    border-left-color: var(--success);
+    background: rgba(16, 185, 129, 0.05);
+}
+
+.nli-card.nli-contradiction {
+    border-left-color: var(--error);
+    background: rgba(239, 68, 68, 0.05);
+}
+
+.nli-card.nli-neutral {
+    border-left-color: var(--neutral);
+    background: rgba(107, 114, 128, 0.05);
+}
+
+.nli-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: var(--spacing-sm);
+}
+
+.nli-label {
+    font-weight: 700;
+    font-size: 1.05rem;
+}
+
+.nli-entailment .nli-label {
+    color: #6ee7b7;
+}
+
+.nli-contradiction .nli-label {
+    color: #fca5a5;
+}
+
+.nli-neutral .nli-label {
+    color: #9ca3af;
+}
+
+.nli-confidence {
+    color: var(--text-muted);
+    font-size: 0.875rem;
+    font-weight: 600;
+}
+
+.nli-evidence {
+    color: var(--text-secondary);
+    line-height: 1.7;
+    margin-bottom: var(--spacing-sm);
+}
+
+.confidence-bar {
+    height: 6px;
+    background: rgba(255, 255, 255, 0.1);
+    border-radius: 3px;
+    overflow: hidden;
+}
+
+.confidence-fill {
+    height: 100%;
+    background: var(--gradient-primary);
+    border-radius: 3px;
+    transition: width var(--transition-slow);
+}
+
+/* ==========================================
+   SUMMARY CARD
+   ========================================== */
+.summary-card {
+    margin-top: var(--spacing-xl);
+}
+
+.summary-card h3 {
+    font-size: 1.5rem;
+    margin-bottom: var(--spacing-md);
+    color: var(--text-primary);
+}
+
+.summary-stats {
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(120px, 1fr));
+    gap: var(--spacing-md);
+}
+
+.stat {
+    text-align: center;
+    padding: var(--spacing-md);
+    background: rgba(255, 255, 255, 0.03);
+    border-radius: var(--radius-md);
+}
+
+.stat-value {
+    font-size: 2.5rem;
+    font-weight: 800;
+    background: var(--gradient-primary);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    background-clip: text;
+    margin-bottom: var(--spacing-xs);
+}
+
+.stat-label {
+    color: var(--text-secondary);
+    font-size: 0.875rem;
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+}
+
+/* ==========================================
+   ACTIONS
+   ========================================== */
+.actions {
+    text-align: center;
+    margin-top: var(--spacing-xl);
+}
+
+/* ==========================================
+   UTILITY CLASSES
+   ========================================== */
+.hidden {
+    display: none !important;
+}
+
+.empty-state {
+    text-align: center;
+    padding: var(--spacing-xl);
+    color: var(--text-muted);
+}
+
+/* ==========================================
+   RESPONSIVE
+   ========================================== */
+@media (max-width: 768px) {
+    .logo h1 {
+        font-size: 2.5rem;
+    }
+
+    .card-title {
+        font-size: 1.5rem;
+    }
+
+    .features {
+        grid-template-columns: 1fr;
+    }
+
+    .summary-stats {
+        grid-template-columns: repeat(2, 1fr);
+    }
+
+    .container {
+        padding: var(--spacing-md);
+    }
+}
+
+/* ==========================================
+   VERDICT CARD
+   ========================================== */
+.verdict-card {
+    text-align: center;
+    border: 2px solid rgba(255, 255, 255, 0.1);
+    background: rgba(255, 255, 255, 0.05);
+    margin-bottom: var(--spacing-xl);
+}
+
+.verdict-card.true {
+    border-color: var(--success);
+    background: rgba(16, 185, 129, 0.1);
+}
+
+.verdict-card.false {
+    border-color: var(--error);
+    background: rgba(239, 68, 68, 0.1);
+}
+
+.verdict-card.uncertain {
+    border-color: var(--warning);
+    background: rgba(245, 158, 11, 0.1);
+}
+
+.verdict-header {
+    margin-bottom: var(--spacing-md);
+}
+
+.verdict-badge {
+    display: inline-block;
+    padding: var(--spacing-xs) var(--spacing-lg);
+    border-radius: var(--radius-xl);
+    font-size: 1.5rem;
+    font-weight: 800;
+    text-transform: uppercase;
+    letter-spacing: 2px;
+    margin-top: var(--spacing-xs);
+}
+
+.verdict-badge.true {
+    background: var(--success);
+    color: white;
+    box-shadow: 0 0 20px rgba(16, 185, 129, 0.4);
+}
+
+.verdict-badge.false {
+    background: var(--error);
+    color: white;
+    box-shadow: 0 0 20px rgba(239, 68, 68, 0.4);
+}
+
+.verdict-badge.uncertain {
+    background: var(--warning);
+    color: white;
+    box-shadow: 0 0 20px rgba(245, 158, 11, 0.4);
+}
+
+.verdict-text {
+    font-size: 1.25rem;
+    color: var(--text-primary);
+    margin-bottom: var(--spacing-lg);
+}
+
+.verdict-text strong {
+    color: white;
+}
+
+.confidence-bar-large {
+    height: 12px;
+    background: rgba(255, 255, 255, 0.1);
+    border-radius: 6px;
+    overflow: hidden;
+    max-width: 600px;
+    margin: 0 auto;
+}
+
+.confidence-fill-large {
+    height: 100%;
+    border-radius: 6px;
+    transition: width 1s ease-out;
+}
+
+.confidence-fill-large.true {
+    background: var(--success);
+}
+
+.confidence-fill-large.false {
+    background: var(--error);
+}
+
+.confidence-fill-large.uncertain {
+    background: var(--warning);
+}

templates/index.html

@@ -0,0 +1,163 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>FactCheck AI - Verify Claims with Evidence</title>
+    <meta name="description" content="Advanced AI-powered fact-checking system that verifies claims using multiple sources and natural language inference.">
+    <link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
+    <link rel="preconnect" href="https://fonts.googleapis.com">
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800&display=swap" rel="stylesheet">
+</head>
+<body>
+    <div class="gradient-bg"></div>
+    
+    <div class="container">
+        <header class="hero">
+            <div class="logo">
+                <span class="logo-icon">🔍</span>
+                <h1>FactCheck<span class="accent">AI</span></h1>
+            </div>
+            <p class="tagline">Verify claims with AI-powered evidence analysis</p>
+        </header>
+
+        <main class="main-content">
+            <div class="card glass-card">
+                <h2 class="card-title">Enter a Claim to Verify</h2>
+                <p class="card-subtitle">Our AI will search multiple sources and analyze the evidence using natural language inference</p>
+                
+                <form id="claimForm" class="claim-form">
+                    <div class="input-group">
+                        <textarea 
+                            id="claimInput" 
+                            name="claim" 
+                            placeholder="e.g., Climate change is caused by human activities"
+                            rows="4"
+                            required
+                            maxlength="500"
+                        ></textarea>
+                        <div class="char-counter">
+                            <span id="charCount">0</span>/500
+                        </div>
+                    </div>
+                    
+                    <button type="submit" class="btn btn-primary" id="submitBtn">
+                        <span class="btn-text">Verify Claim</span>
+                        <span class="btn-icon">→</span>
+                    </button>
+                </form>
+
+                <div id="loadingState" class="loading-state hidden">
+                    <div class="spinner"></div>
+                    <p class="loading-text">Analyzing claim and gathering evidence...</p>
+                    <div class="loading-steps">
+                        <div class="step active">📡 Fetching from news sources</div>
+                        <div class="step">🌍 Searching GDELT database</div>
+                        <div class="step">📚 Checking Wikipedia</div>
+                        <div class="step">🧪 Running NLI analysis</div>
+                    </div>
+                </div>
+
+                <div id="errorState" class="error-state hidden">
+                    <span class="error-icon">⚠️</span>
+                    <p class="error-text"></p>
+                </div>
+            </div>
+
+            <div class="features">
+                <div class="feature">
+                    <span class="feature-icon">🌐</span>
+                    <h3>Multi-Source</h3>
+                    <p>Aggregates evidence from RSS feeds, GDELT, Wikipedia, and more</p>
+                </div>
+                <div class="feature">
+                    <span class="feature-icon">🤖</span>
+                    <h3>AI-Powered</h3>
+                    <p>Uses advanced NLI models to determine claim validity</p>
+                </div>
+                <div class="feature">
+                    <span class="feature-icon">⚡</span>
+                    <h3>Real-Time</h3>
+                    <p>Get instant results with comprehensive evidence analysis</p>
+                </div>
+            </div>
+        </main>
+    </div>
+
+    <script>
+        const form = document.getElementById('claimForm');
+        const input = document.getElementById('claimInput');
+        const charCount = document.getElementById('charCount');
+        const submitBtn = document.getElementById('submitBtn');
+        const loadingState = document.getElementById('loadingState');
+        const errorState = document.getElementById('errorState');
+
+        // Character counter
+        input.addEventListener('input', () => {
+            charCount.textContent = input.value.length;
+        });
+
+        // Form submission
+        form.addEventListener('submit', async (e) => {
+            e.preventDefault();
+            
+            const claim = input.value.trim();
+            if (!claim) {
+                showError('Please enter a claim to verify');
+                return;
+            }
+
+            // Show loading state
+            form.classList.add('hidden');
+            errorState.classList.add('hidden');
+            loadingState.classList.remove('hidden');
+            
+            // Animate loading steps
+            animateLoadingSteps();
+
+            try {
+                const formData = new FormData();
+                formData.append('claim', claim);
+
+                const response = await fetch('/check', {
+                    method: 'POST',
+                    body: formData
+                });
+
+                const data = await response.json();
+
+                if (data.success) {
+                    // Redirect to results page
+                    window.location.href = '/results';
+                } else {
+                    showError(data.error || 'Failed to verify claim');
+                }
+            } catch (error) {
+                showError('Network error. Please try again.');
+            }
+        });
+
+        function showError(message) {
+            loadingState.classList.add('hidden');
+            form.classList.remove('hidden');
+            errorState.classList.remove('hidden');
+            errorState.querySelector('.error-text').textContent = message;
+        }
+
+        function animateLoadingSteps() {
+            const steps = document.querySelectorAll('.loading-steps .step');
+            let currentStep = 0;
+
+            const interval = setInterval(() => {
+                if (currentStep < steps.length) {
+                    steps[currentStep].classList.add('active');
+                    currentStep++;
+                } else {
+                    clearInterval(interval);
+                }
+            }, 1500);
+        }
+    </script>
+</body>
+</html>

templates/results.html

@@ -0,0 +1,201 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Results - FactCheck AI</title>
+    <meta name="description" content="Fact-checking results with evidence and NLI analysis">
+    <link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
+    <link rel="preconnect" href="https://fonts.googleapis.com">
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800&display=swap"
+        rel="stylesheet">
+</head>
+
+<body>
+    <div class="gradient-bg"></div>
+
+    <div class="container">
+        <header class="header-small">
+            <a href="/" class="back-link">← Back to Home</a>
+            <div class="logo-small">
+                <span class="logo-icon">🔍</span>
+                <h1>FactCheck<span class="accent">AI</span></h1>
+            </div>
+        </header>
+
+        <main class="results-content">
+            {% if result.success %}
+            <!-- Claim Card -->
+            <div class="card glass-card claim-card">
+                <div class="card-header">
+                    <h2>Analyzed Claim</h2>
+                    <span class="badge badge-success">✓ Analysis Complete</span>
+                </div>
+                <p class="claim-text">{{ result.claim }}</p>
+                <div class="meta-info">
+                    <span class="meta-item">
+                        <strong>{{ result.total_evidence }}</strong> evidence sources found
+                    </span>
+                    <span class="meta-item">
+                        <strong>{{ result.nli_results|length }}</strong> NLI analyses performed
+                    </span>
+                </div>
+            </div>
+
+            <!-- Verdict Section -->
+            {% if result.verdict %}
+            <div class="card glass-card verdict-card {{ result.verdict|lower }}">
+                <div class="verdict-header">
+                    <h2>Final Verdict</h2>
+                    <br>
+                    <span class="verdict-badge {{ result.verdict|lower }}">{{ result.verdict }}</span>
+                </div>
+                <p class="verdict-text">
+                    Based on the evidence, this claim is likely <strong>{{ result.verdict }}</strong> with <strong>{{
+                        (result.confidence * 100)|round(0) }}%</strong> confidence.
+                </p>
+                <div class="confidence-bar-large">
+                    <div class="confidence-fill-large {{ result.verdict|lower }}"
+                        style="width: {{ (result.confidence * 100)|round(0) }}%"></div>
+                </div>
+            </div>
+            {% endif %}
+
+            <!-- Evidence Section -->
+            <div class="section">
+                <h2 class="section-title">
+                    <span class="section-icon">🔎</span>
+                    Evidence Sources
+                </h2>
+
+                {% if result.evidence %}
+                <div class="evidence-grid">
+                    {% for evidence in result.evidence %}
+                    <div class="card evidence-card" style="animation-delay: {{ loop.index0 * 0.1 }}s">
+                        <div class="evidence-header">
+                            <span class="source-badge source-{{ evidence.source|lower }}">
+                                {{ evidence.source }}
+                            </span>
+                            <span class="similarity-score">
+                                {{ (evidence.similarity * 100)|round(1) }}% match
+                            </span>
+                        </div>
+                        <p class="evidence-text">{{ evidence.text }}</p>
+                    </div>
+                    {% endfor %}
+                </div>
+                {% else %}
+                <div class="card empty-state">
+                    <p>No evidence found</p>
+                </div>
+                {% endif %}
+            </div>
+
+            <!-- NLI Results Section -->
+            <div class="section">
+                <h2 class="section-title">
+                    <span class="section-icon">🧪</span>
+                    Natural Language Inference Results
+                </h2>
+                <p class="section-subtitle">AI analysis of how the evidence relates to your claim</p>
+
+                {% if result.nli_results %}
+                <div class="nli-grid">
+                    {% for nli in result.nli_results %}
+                    <div class="card nli-card nli-{{ nli.label|lower }}"
+                        style="animation-delay: {{ loop.index0 * 0.1 }}s">
+                        <div class="nli-header">
+                            <span class="nli-label">
+                                {% if 'entail' in nli.label|lower %}
+                                ✓ Supports
+                                {% elif 'contradict' in nli.label|lower %}
+                                ✗ Contradicts
+                                {% else %}
+                                ◐ Neutral
+                                {% endif %}
+                            </span>
+                            <span class="nli-confidence">
+                                {{ (nli.score * 100)|round(1) }}% confidence
+                            </span>
+                        </div>
+                        <p class="nli-evidence">{{ nli.evidence }}</p>
+                        <div class="confidence-bar">
+                            <div class="confidence-fill" style="width: {{ (nli.score * 100)|round(1) }}%"></div>
+                        </div>
+                    </div>
+                    {% endfor %}
+                </div>
+                {% else %}
+                <div class="card empty-state">
+                    <p>No NLI results available</p>
+                </div>
+                {% endif %}
+            </div>
+
+            <!-- Summary Section -->
+            <div class="card glass-card summary-card">
+                <h3>Analysis Summary</h3>
+                <div class="summary-stats">
+                    <div class="stat">
+                        <div class="stat-value">{{ result.evidence|length }}</div>
+                        <div class="stat-label">Evidence Pieces</div>
+                    </div>
+                    <div class="stat">
+                        <div class="stat-value">
+                            {% set supporting = namespace(count=0) %}
+                            {% for nli in result.nli_results %}
+                            {% if 'entail' in nli.label|lower %}
+                            {% set supporting.count = supporting.count + 1 %}
+                            {% endif %}
+                            {% endfor %}
+                            {{ supporting.count }}
+                        </div>
+                        <div class="stat-label">Supporting</div>
+                    </div>
+                    <div class="stat">
+                        <div class="stat-value">
+                            {% set contradicting = namespace(count=0) %}
+                            {% for nli in result.nli_results %}
+                            {% if 'contradict' in nli.label|lower %}
+                            {% set contradicting.count = contradicting.count + 1 %}
+                            {% endif %}
+                            {% endfor %}
+                            {{ contradicting.count }}
+                        </div>
+                        <div class="stat-label">Contradicting</div>
+                    </div>
+                    <div class="stat">
+                        <div class="stat-value">
+                            {% set neutral = namespace(count=0) %}
+                            {% for nli in result.nli_results %}
+                            {% if 'entail' not in nli.label|lower and 'contradict' not in nli.label|lower %}
+                            {% set neutral.count = neutral.count + 1 %}
+                            {% endif %}
+                            {% endfor %}
+                            {{ neutral.count }}
+                        </div>
+                        <div class="stat-label">Neutral</div>
+                    </div>
+                </div>
+            </div>
+
+            <div class="actions">
+                <a href="/" class="btn btn-primary">Check Another Claim</a>
+            </div>
+
+            {% else %}
+            <!-- Error State -->
+            <div class="card glass-card error-card">
+                <span class="error-icon-large">⚠️</span>
+                <h2>Analysis Failed</h2>
+                <p class="error-message">{{ result.error }}</p>
+                <a href="/" class="btn btn-primary">Try Again</a>
+            </div>
+            {% endif %}
+        </main>
+    </div>
+</body>
+
+</html>