app.py

import streamlit as st
import requests
import torch
import numpy as np
import os
import plotly.graph_objects as go
import plotly.express as px
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from datetime import datetime
import pandas as pd
import re
from urllib.parse import urlparse
 
# ─────────────────────────────────────────────
# 🔑 API KEY — PASTE YOUR NewsAPI KEY HERE
NEWS_API_KEY = os.getenv("NEWS_API_KEY")
# Get your free key at https://newsapi.org/register
# ─────────────────────────────────────────────
 
# ─────────────────────────────────────────────
# Model: Pre-trained fine-tuned BERT for fake news
# No training needed — loaded directly from HuggingFace Hub
MODEL_NAME = "jy46604790/Fake-News-Bert-Detect"
# ─────────────────────────────────────────────
 
# Source credibility database
SOURCE_CREDIBILITY = {
    "bbc.com": 0.97, "bbc.co.uk": 0.97,
    "reuters.com": 0.96, "apnews.com": 0.95,
    "theguardian.com": 0.93, "nytimes.com": 0.92,
    "washingtonpost.com": 0.91, "npr.org": 0.92,
    "bloomberg.com": 0.90, "economist.com": 0.92,
    "ft.com": 0.91, "nature.com": 0.97,
    "science.org": 0.97, "who.int": 0.98,
    "cdc.gov": 0.97, "gov.uk": 0.94,
    "thehindu.com": 0.88, "ndtv.com": 0.82,
    "hindustantimes.com": 0.80, "timesofindia.com": 0.79,
    "cnn.com": 0.78, "foxnews.com": 0.65,
    "huffpost.com": 0.70, "buzzfeed.com": 0.62,
    "vice.com": 0.68, "vox.com": 0.74,
    "medium.com": 0.52, "substack.com": 0.50,
    "infowars.com": 0.05, "naturalnews.com": 0.08,
    "beforeitsnews.com": 0.06, "worldnewsdailyreport.com": 0.04,
    "empirenews.net": 0.04, "theonion.com": 0.10,
}
 
CREDIBILITY_LABELS = {
    (0.85, 1.0): ("🟢 Highly Credible", "#22c55e"),
    (0.65, 0.85): ("🟡 Moderately Credible", "#f59e0b"),
    (0.40, 0.65): ("🟠 Low Credibility", "#f97316"),
    (0.0, 0.40): ("🔴 Very Low / Known Misinformation", "#ef4444"),
}
 
FAKE_INDICATORS = [
    (r'\b(SHOCKING|BOMBSHELL|BREAKING|EXCLUSIVE)\b', "ALL-CAPS sensational trigger words"),
    (r'(!{2,}|\?{2,})', "Excessive punctuation (!! or ??)"),
    (r'\b(they don\'t want you to know|mainstream media won\'t tell)\b', "Anti-establishment conspiracy framing"),
    (r'\b(miracle|cure|secret|censored|banned)\b', "Clickbait / pseudoscience language"),
    (r'\b(100%|proven fact|scientists hate)\b', "Overconfident absolute claims"),
    (r'(share before deleted|share before banned)', "Urgency/fear-of-censorship manipulation"),
    (r'\b(deep state|new world order|illuminati|cabal)\b', "Conspiracy theory terminology"),
    (r'\baccording to sources\b(?!.*\bnamed\b)', "Vague anonymous sourcing"),
]
 
 
@st.cache_resource(show_spinner=False)
def load_model():
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
    model.eval()
    return tokenizer, model
 
 
def classify_text(text, tokenizer, model):
    inputs = tokenizer(text, return_tensors="pt", truncation=True,
                       max_length=512, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=1).squeeze().numpy()
 
    labels = model.config.id2label
    fake_idx = next((i for i, l in labels.items() if "fake" in l.lower() or "0" == str(i)), 0)
    real_idx = 1 - fake_idx
 
    fake_prob = float(probs[fake_idx])
    real_prob = float(probs[real_idx])
    prediction = "FAKE" if fake_prob > real_prob else "REAL"
    confidence = max(fake_prob, real_prob)
    return prediction, confidence, fake_prob, real_prob
 
 
def get_source_credibility(url_or_domain):
    if not url_or_domain:
        return None, 0.5, "Unknown Source", "#94a3b8"
    try:
        domain = urlparse(url_or_domain).netloc.lower().replace("www.", "")
    except Exception:
        domain = url_or_domain.lower().replace("www.", "")
 
    if domain in SOURCE_CREDIBILITY:
        score = SOURCE_CREDIBILITY[domain]
    else:
        score = 0.45
        if domain.endswith(".gov") or domain.endswith(".edu"):
            score = 0.90
        elif domain.endswith(".org"):
            score = 0.65
 
    label, color = "Unknown", "#94a3b8"
    for (low, high), (lbl, clr) in CREDIBILITY_LABELS.items():
        if low <= score <= high:
            label, color = lbl, clr
            break
    return domain, score, label, color
 
 
def detect_fake_indicators(text):
    found = []
    for pattern, description in FAKE_INDICATORS:
        if re.search(pattern, text, re.IGNORECASE):
            found.append(description)
    return found
 
 
def fetch_news(query, api_key, max_articles=6):
    if not api_key or api_key == "YOUR_NEWSAPI_KEY_HERE":
        return None, "⚠️ No API key provided. Add your NewsAPI key in app.py."
    url = (
        f"https://newsapi.org/v2/everything?"
        f"q={requests.utils.quote(query)}&language=en&sortBy=publishedAt"
        f"&pageSize={max_articles}&apiKey={api_key}"
    )
    try:
        resp = requests.get(url, timeout=10)
        data = resp.json()
        if data.get("status") != "ok":
            return None, data.get("message", "API error")
        return data.get("articles", []), None
    except Exception as e:
        return None, str(e)
 
 
def make_confidence_gauge(fake_prob, real_prob):
    fig = go.Figure(go.Indicator(
        mode="gauge+number+delta",
        value=round(fake_prob * 100, 1),
        domain={"x": [0, 1], "y": [0, 1]},
        title={"text": "Fake Probability %", "font": {"size": 18, "color": "#e2e8f0"}},
        number={"font": {"size": 36, "color": "#f8fafc"}, "suffix": "%"},
        gauge={
            "axis": {"range": [0, 100], "tickcolor": "#64748b",
                     "tickfont": {"color": "#94a3b8"}},
            "bar": {"color": "#6366f1"},
            "steps": [
                {"range": [0, 30], "color": "#14532d"},
                {"range": [30, 55], "color": "#713f12"},
                {"range": [55, 100], "color": "#7f1d1d"},
            ],
            "threshold": {
                "line": {"color": "#fbbf24", "width": 4},
                "thickness": 0.85,
                "value": 50,
            },
        },
    ))
    fig.update_layout(
        paper_bgcolor="rgba(0,0,0,0)",
        plot_bgcolor="rgba(0,0,0,0)",
        font={"color": "#e2e8f0"},
        height=280,
        margin=dict(t=50, b=10, l=30, r=30),
    )
    return fig
 
 
def make_prob_bar(fake_prob, real_prob):
    fig = go.Figure()
    fig.add_trace(go.Bar(
        x=["FAKE", "REAL"],
        y=[fake_prob * 100, real_prob * 100],
        marker_color=["#ef4444", "#22c55e"],
        text=[f"{fake_prob*100:.1f}%", f"{real_prob*100:.1f}%"],
        textposition="outside",
        textfont=dict(color="#f8fafc", size=14),
        width=0.45,
    ))
    fig.update_layout(
        paper_bgcolor="rgba(0,0,0,0)",
        plot_bgcolor="rgba(0,0,0,0)",
        font=dict(color="#e2e8f0"),
        yaxis=dict(range=[0, 115], gridcolor="#1e293b", ticksuffix="%",
                   tickfont=dict(color="#64748b")),
        xaxis=dict(tickfont=dict(color="#e2e8f0", size=14)),
        height=260,
        margin=dict(t=10, b=10, l=10, r=10),
        showlegend=False,
    )
    return fig
 
 
def credibility_bar_chart(domain, score):
    fig = go.Figure(go.Bar(
        x=[score * 100],
        y=[domain or "Unknown"],
        orientation="h",
        marker=dict(
            color=score * 100,
            colorscale=[[0, "#ef4444"], [0.5, "#f59e0b"], [1, "#22c55e"]],
            cmin=0, cmax=100,
        ),
        text=[f"{score*100:.0f}/100"],
        textposition="outside",
        textfont=dict(color="#f8fafc"),
    ))
    fig.update_layout(
        paper_bgcolor="rgba(0,0,0,0)",
        plot_bgcolor="rgba(0,0,0,0)",
        xaxis=dict(range=[0, 115], gridcolor="#1e293b", ticksuffix="",
                   tickfont=dict(color="#64748b")),
        yaxis=dict(tickfont=dict(color="#e2e8f0", size=13)),
        height=120,
        margin=dict(t=5, b=5, l=10, r=60),
    )
    return fig
 
 
# ══════════════════════════════════════════════
# STREAMLIT UI
# ══════════════════════════════════════════════
st.set_page_config(
    page_title="FakeScope — AI News Verifier",
    page_icon="🔍",
    layout="wide",
    initial_sidebar_state="expanded",
)
 
st.markdown("""
<style>
@import url('https://fonts.googleapis.com/css2?family=Syne:wght@400;600;700;800&family=JetBrains+Mono:wght@400;500&display=swap');
 
html, body, [class*="css"] {
    font-family: 'Syne', sans-serif;
    background-color: #050a14;
    color: #e2e8f0;
}
.stApp { background: #050a14; }
 
.hero {
    background: linear-gradient(135deg, #0f172a 0%, #1a0a2e 50%, #0f172a 100%);
    border: 1px solid #1e293b;
    border-radius: 20px;
    padding: 2.5rem 3rem;
    margin-bottom: 2rem;
    position: relative;
    overflow: hidden;
}
.hero::before {
    content: '';
    position: absolute;
    top: -60px; right: -60px;
    width: 300px; height: 300px;
    background: radial-gradient(circle, rgba(99,102,241,0.15) 0%, transparent 70%);
    border-radius: 50%;
}
.hero h1 {
    font-size: 3rem; font-weight: 800;
    background: linear-gradient(90deg, #818cf8, #c084fc, #f472b6);
    -webkit-background-clip: text; -webkit-text-fill-color: transparent;
    margin: 0; letter-spacing: -1px;
}
.hero p { color: #94a3b8; font-size: 1.05rem; margin-top: 0.5rem; margin-bottom: 0; }
 
.card {
    background: #0f172a;
    border: 1px solid #1e293b;
    border-radius: 16px;
    padding: 1.5rem;
    margin-bottom: 1rem;
}
.verdict-fake {
    border: 2px solid #ef4444;
    background: linear-gradient(135deg, #1a0000, #0f172a);
    border-radius: 16px;
    padding: 1.5rem;
    text-align: center;
}
.verdict-real {
    border: 2px solid #22c55e;
    background: linear-gradient(135deg, #001a00, #0f172a);
    border-radius: 16px;
    padding: 1.5rem;
    text-align: center;
}
.verdict-label {
    font-size: 2.5rem; font-weight: 800; letter-spacing: 4px;
}
.fake-label { color: #ef4444; }
.real-label { color: #22c55e; }
 
.indicator-pill {
    display: inline-block;
    background: #1e1030;
    border: 1px solid #7c3aed;
    color: #c084fc;
    border-radius: 99px;
    padding: 0.3rem 0.9rem;
    font-size: 0.82rem;
    margin: 0.25rem;
    font-family: 'JetBrains Mono', monospace;
}
 
.news-card {
    background: #0f172a;
    border: 1px solid #1e293b;
    border-radius: 12px;
    padding: 1.2rem;
    margin-bottom: 0.8rem;
    transition: border-color 0.2s;
}
.news-card:hover { border-color: #6366f1; }
.news-card h4 { color: #e2e8f0; font-size: 0.95rem; margin: 0 0 0.4rem 0; }
.news-card p { color: #64748b; font-size: 0.82rem; margin: 0; }
 
section[data-testid="stSidebar"] {
    background: #080d1a;
    border-right: 1px solid #1e293b;
}
 
.stTextArea textarea, .stTextInput input {
    background: #0f172a !important;
    border: 1px solid #334155 !important;
    color: #e2e8f0 !important;
    border-radius: 10px !important;
    font-family: 'JetBrains Mono', monospace !important;
}
.stButton > button {
    background: linear-gradient(135deg, #4f46e5, #7c3aed) !important;
    color: white !important;
    border: none !important;
    border-radius: 10px !important;
    font-family: 'Syne', sans-serif !important;
    font-weight: 700 !important;
    font-size: 1rem !important;
    padding: 0.6rem 2rem !important;
    transition: opacity 0.2s !important;
    width: 100%;
}
.stButton > button:hover { opacity: 0.85 !important; }
 
.section-title {
    font-size: 0.75rem; font-weight: 700; letter-spacing: 3px;
    color: #6366f1; text-transform: uppercase; margin-bottom: 0.75rem;
}
 
.metric-box {
    background: #0f172a;
    border: 1px solid #1e293b;
    border-radius: 12px;
    padding: 1rem 1.2rem;
    text-align: center;
}
.metric-box .val { font-size: 1.8rem; font-weight: 800; color: #818cf8; }
.metric-box .lbl { font-size: 0.75rem; color: #64748b; letter-spacing: 1px; margin-top: 2px; }
 
div[data-testid="stMetricValue"] { color: #818cf8 !important; font-family: 'Syne', sans-serif !important; }
</style>
""", unsafe_allow_html=True)
 
# ── Sidebar ──────────────────────────────────
with st.sidebar:
    st.markdown("## 🔍 FakeScope")
    st.markdown("---")
    mode = st.radio("**Mode**", ["📝 Paste Article / Text", "🌐 Fetch Live News"])
    st.markdown("---")
    st.markdown("**About the Model**")
    st.caption(f"`{MODEL_NAME}`")
    st.caption("Fine-tuned BERT — no local training required.")
    st.markdown("---")
    st.markdown("**Credibility DB**")
    st.caption(f"{len(SOURCE_CREDIBILITY)} known sources indexed.")
    st.markdown("---")
    st.caption("Built with 🤗 Transformers + Streamlit")
 
# ── Hero ─────────────────────────────────────
st.markdown("""
<div class="hero">
  <h1>🔎 FakeScope</h1>
  <p>AI-powered fake news detector · BERT · Source Credibility · Real-time News · Explainability</p>
</div>
""", unsafe_allow_html=True)
 
# ── Load model ───────────────────────────────
with st.spinner("⚡ Loading BERT model from HuggingFace (first run only)…"):
    try:
        tokenizer, model = load_model()
        st.success("✅ Model loaded successfully!", icon="🤖")
    except Exception as e:
        st.error(f"Model load failed: {e}")
        st.stop()
 
# ════════════════════════════════════════════
#  MODE 1 — Paste Text
# ════════════════════════════════════════════
if mode == "📝 Paste Article / Text":
    st.markdown('<div class="section-title">Paste news article or headline</div>', unsafe_allow_html=True)
 
    col_in, col_meta = st.columns([3, 1])
    with col_in:
        news_text = st.text_area("", height=180,
                                 placeholder="Paste a news headline, paragraph, or full article here…",
                                 label_visibility="collapsed")
    with col_meta:
        source_url = st.text_input("Source URL (optional)",
                                   placeholder="https://bbc.com/…")
        analyze_btn = st.button("🔍 Analyze", use_container_width=True)
 
    if analyze_btn:
        if not news_text.strip():
            st.warning("Please paste some text to analyze.")
        else:
            with st.spinner("Running BERT inference…"):
                prediction, confidence, fake_prob, real_prob = classify_text(
                    news_text, tokenizer, model)
                indicators = detect_fake_indicators(news_text)
                domain, cred_score, cred_label, cred_color = get_source_credibility(source_url)
 
            # ── Verdict ──────────────────────────────
            st.markdown("---")
            vcol1, vcol2, vcol3 = st.columns([1, 2, 1])
            with vcol2:
                if prediction == "FAKE":
                    low_conf = confidence < 0.75
                    warning = (
                        "<div style='color:#fbbf24;font-size:0.85rem;margin-top:0.5rem'>"
                        "⚠ Low confidence — verify manually before concluding</div>"
                        if low_conf else ""
                    )
                    st.markdown(
                        f"""
                        <div class="verdict-fake">
                            <div class="verdict-label fake-label">⚠ FAKE NEWS</div>
                            <div style="color:#94a3b8;margin-top:0.4rem;font-size:0.95rem;">
                                Confidence: <b style="color:#f8fafc">{confidence*100:.1f}%</b>
                            </div>
                            {warning}
                        </div>""",
                        unsafe_allow_html=True,
                    )
                else:
                    st.markdown(
                        f"""
                        <div class="verdict-real">
                            <div class="verdict-label real-label">✅ LIKELY REAL</div>
                            <div style="color:#94a3b8;margin-top:0.4rem;font-size:0.95rem;">
                                Confidence: <b style="color:#f8fafc">{confidence*100:.1f}%</b>
                            </div>
                        </div>""",
                        unsafe_allow_html=True,
                    )
 
            st.markdown("<br>", unsafe_allow_html=True)
 
            # ── Charts ───────────────────────────────
            ch1, ch2 = st.columns(2)
            with ch1:
                st.markdown('<div class="section-title">Confidence Gauge</div>', unsafe_allow_html=True)
                st.plotly_chart(make_confidence_gauge(fake_prob, real_prob),
                                use_container_width=True, config={"displayModeBar": False})
            with ch2:
                st.markdown('<div class="section-title">Probability Distribution</div>', unsafe_allow_html=True)
                st.plotly_chart(make_prob_bar(fake_prob, real_prob),
                                use_container_width=True, config={"displayModeBar": False})
 
            # ── Source Credibility ───────────────────
            st.markdown('<div class="section-title">Source Credibility Score</div>', unsafe_allow_html=True)
            st.markdown(
                f"""
                <div class="card">
                  <span style="font-size:1.1rem">{cred_label}</span>
                  <span style="color:#64748b;font-family:monospace;font-size:0.85rem;margin-left:1rem">
                    {domain or 'Unknown domain'}
                  </span>
                </div>""",
                unsafe_allow_html=True,
            )
            st.plotly_chart(credibility_bar_chart(domain or "Unknown", cred_score),
                            use_container_width=True, config={"displayModeBar": False})
 
            # ── Why it might be fake ─────────────────
            st.markdown('<div class="section-title">🧠 Explanation — Why it may be Fake</div>',
                        unsafe_allow_html=True)
            with st.container():
                if indicators:
                    st.markdown("**Linguistic red flags detected:**")
                    pills_html = "".join(
                        f'<span class="indicator-pill">⚠ {i}</span>' for i in indicators)
                    st.markdown(pills_html, unsafe_allow_html=True)
                else:
                    st.success("No obvious linguistic red flags detected in the text.")
 
                if prediction == "FAKE":
                    reasons = []
                    if fake_prob > 0.85:
                        reasons.append("Very high BERT fake-probability score (>85%)")
                    if cred_score < 0.5:
                        reasons.append(
                            f"Source '{domain}' has very low credibility ({cred_score*100:.0f}/100)")
                    if indicators:
                        reasons.append(
                            f"{len(indicators)} sensational/clickbait linguistic patterns found")
                    if reasons:
                        st.markdown("**Key reasons for FAKE classification:**")
                        for r in reasons:
                            st.markdown(f"&nbsp;&nbsp;🔸 {r}")
 
            # ── Stats ────────────────────────────────
            st.markdown('<div class="section-title">Analytics Summary</div>', unsafe_allow_html=True)
            m1, m2, m3, m4 = st.columns(4)
            with m1:
                st.markdown(
                    f'<div class="metric-box"><div class="val">{fake_prob*100:.0f}%</div>'
                    f'<div class="lbl">FAKE PROB</div></div>',
                    unsafe_allow_html=True)
            with m2:
                st.markdown(
                    f'<div class="metric-box"><div class="val">{real_prob*100:.0f}%</div>'
                    f'<div class="lbl">REAL PROB</div></div>',
                    unsafe_allow_html=True)
            with m3:
                st.markdown(
                    f'<div class="metric-box"><div class="val">{cred_score*100:.0f}</div>'
                    f'<div class="lbl">SOURCE SCORE</div></div>',
                    unsafe_allow_html=True)
            with m4:
                st.markdown(
                    f'<div class="metric-box"><div class="val">{len(indicators)}</div>'
                    f'<div class="lbl">RED FLAGS</div></div>',
                    unsafe_allow_html=True)
 
# ════════════════════════════════════════════
#  MODE 2 — Live News Feed
# ════════════════════════════════════════════
else:
    st.markdown('<div class="section-title">Fetch & analyze live news articles</div>',
                unsafe_allow_html=True)
 
    qcol, bcol = st.columns([4, 1])
    with qcol:
        query = st.text_input("", placeholder="Search topic e.g. 'climate change', 'election 2024'…",
                              label_visibility="collapsed")
    with bcol:
        fetch_btn = st.button("📡 Fetch News", use_container_width=True)
 
    if fetch_btn:
        if not query.strip():
            st.warning("Enter a search query.")
        else:
            with st.spinner(f"Fetching news for: **{query}**…"):
                articles, err = fetch_news(query, NEWS_API_KEY)
 
            if err:
                st.error(f"NewsAPI error: {err}")
            elif not articles:
                st.info("No articles found. Try a different query.")
            else:
                results = []
                progress = st.progress(0)
                for i, art in enumerate(articles):
                    text = (art.get("title") or "") + " " + (art.get("description") or "")
                    if text.strip():
                        pred, conf, fp, rp = classify_text(text, tokenizer, model)
                        domain, cscore, clabel, ccolor = get_source_credibility(
                            art.get("url", ""))
                        indicators = detect_fake_indicators(text)
                        results.append({
                            "title": art.get("title", "No title"),
                            "source": art.get("source", {}).get("name", "Unknown"),
                            "url": art.get("url", "#"),
                            "publishedAt": art.get("publishedAt", ""),
                            "prediction": pred,
                            "confidence": conf,
                            "fake_prob": fp,
                            "real_prob": rp,
                            "cred_score": cscore,
                            "cred_label": clabel,
                            "indicators": indicators,
                        })
                    progress.progress((i + 1) / len(articles))
                progress.empty()
 
                fake_count = sum(1 for r in results if r["prediction"] == "FAKE")
                real_count = len(results) - fake_count
                avg_conf = np.mean([r["confidence"] for r in results]) * 100
 
                st.markdown("---")
                st.markdown('<div class="section-title">Batch Analysis Summary</div>',
                            unsafe_allow_html=True)
                sm1, sm2, sm3, sm4 = st.columns(4)
                with sm1:
                    st.markdown(
                        f'<div class="metric-box"><div class="val">{len(results)}</div>'
                        f'<div class="lbl">ARTICLES</div></div>',
                        unsafe_allow_html=True)
                with sm2:
                    st.markdown(
                        f'<div class="metric-box"><div class="val" style="color:#ef4444">{fake_count}</div>'
                        f'<div class="lbl">FLAGGED FAKE</div></div>',
                        unsafe_allow_html=True)
                with sm3:
                    st.markdown(
                        f'<div class="metric-box"><div class="val" style="color:#22c55e">{real_count}</div>'
                        f'<div class="lbl">LIKELY REAL</div></div>',
                        unsafe_allow_html=True)
                with sm4:
                    st.markdown(
                        f'<div class="metric-box"><div class="val">{avg_conf:.0f}%</div>'
                        f'<div class="lbl">AVG CONFIDENCE</div></div>',
                        unsafe_allow_html=True)
 
                st.markdown("<br>", unsafe_allow_html=True)
                titles_short = [r["title"][:45] + "…" if len(r["title"]) > 45 else r["title"]
                                for r in results]
                colors = ["#ef4444" if r["prediction"] == "FAKE" else "#22c55e" for r in results]
                fig_batch = go.Figure(go.Bar(
                    y=titles_short,
                    x=[r["fake_prob"] * 100 for r in results],
                    orientation="h",
                    marker_color=colors,
                    text=[f"{r['fake_prob']*100:.0f}%" for r in results],
                    textposition="outside",
                    textfont=dict(color="#e2e8f0", size=11),
                ))
                fig_batch.update_layout(
                    paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)",
                    xaxis=dict(range=[0, 120], ticksuffix="%", gridcolor="#1e293b",
                               tickfont=dict(color="#64748b")),
                    yaxis=dict(tickfont=dict(color="#e2e8f0", size=11)),
                    height=max(300, len(results) * 55),
                    margin=dict(t=10, b=10, l=10, r=80),
                    title=dict(text="Fake Probability per Article",
                               font=dict(color="#94a3b8", size=13)),
                )
                st.plotly_chart(fig_batch, use_container_width=True,
                                config={"displayModeBar": False})
 
                st.markdown('<div class="section-title">Individual Article Results</div>',
                            unsafe_allow_html=True)
                for r in results:
                    badge_color = "#ef4444" if r["prediction"] == "FAKE" else "#22c55e"
                    badge_text = "⚠ FAKE" if r["prediction"] == "FAKE" else "✅ REAL"
                    ind_html = "".join(
                        f'<span class="indicator-pill">{ind}</span>'
                        for ind in r["indicators"][:2]
                    ) if r["indicators"] else ""
                    st.markdown(
                        f"""
                        <div class="news-card">
                          <div style="display:flex;justify-content:space-between;align-items:flex-start">
                            <h4>{r['title']}</h4>
                            <span style="background:{badge_color}22;color:{badge_color};
                              border:1px solid {badge_color};border-radius:99px;
                              padding:0.2rem 0.8rem;font-size:0.8rem;font-weight:700;
                              white-space:nowrap;margin-left:1rem">{badge_text}</span>
                          </div>
                          <p>📰 {r['source']} &nbsp;·&nbsp; Confidence: {r['confidence']*100:.1f}%
                             &nbsp;·&nbsp; Source credibility: {r['cred_label']}</p>
                          {ind_html}
                          <p style="margin-top:0.5rem"><a href="{r['url']}" target="_blank"
                             style="color:#6366f1;font-size:0.8rem">Read original →</a></p>
                        </div>""",
                        unsafe_allow_html=True,
                    )
 
# ── Footer ───────────────────────────────────
st.markdown("---")
st.markdown(
    '<p style="text-align:center;color:#334155;font-size:0.8rem">'
    'FakeScope · Powered by 🤗 Transformers · For educational use only'
    '</p>',
    unsafe_allow_html=True,
)