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| import os | |
| import sys | |
| import json | |
| import time | |
| import pandas as pd | |
| import numpy as np | |
| import streamlit as st | |
| _ROOT = os.path.dirname(os.path.abspath(__file__)) | |
| if _ROOT not in sys.path: | |
| sys.path.insert(0, _ROOT) | |
| # ── Page config ────────────────────────────────────────────────────────────── | |
| st.set_page_config( | |
| page_title="TruthLens · Fake News Detector", | |
| page_icon="🔍", | |
| layout="wide", | |
| initial_sidebar_state="collapsed", | |
| ) | |
| # ── Global CSS ─────────────────────────────────────────────────────────────── | |
| st.markdown(""" | |
| <style> | |
| @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800&display=swap'); | |
| /* ── Reset ── */ | |
| html, body, [data-testid="stAppViewContainer"] { | |
| font-family: 'Inter', sans-serif; | |
| background: #f4f6fb; | |
| color: #1e293b; | |
| } | |
| [data-testid="stMain"] { background: #f4f6fb; } | |
| .block-container { | |
| padding-top: 2.5rem !important; | |
| padding-bottom: 2rem !important; | |
| max-width: 920px; | |
| } | |
| /* ── Remove Streamlit chrome ── */ | |
| header[data-testid="stHeader"] { display: none; } | |
| footer { display: none; } | |
| #MainMenu { display: none; } | |
| [data-testid="stSidebar"] { display: none; } | |
| /* ── Predict button ── */ | |
| .stButton > button[kind="primary"] { | |
| background: linear-gradient(135deg, #3b82f6 0%, #6366f1 100%) !important; | |
| color: #fff !important; | |
| border: none !important; | |
| border-radius: 12px !important; | |
| font-weight: 700 !important; | |
| font-size: 1.05rem !important; | |
| letter-spacing: 0.02em; | |
| padding: 0.75rem 2rem !important; | |
| transition: transform 0.15s, box-shadow 0.2s; | |
| box-shadow: 0 4px 16px rgba(59,130,246,0.2); | |
| } | |
| .stButton > button[kind="primary"]:hover { | |
| transform: translateY(-1px); | |
| box-shadow: 0 6px 24px rgba(59,130,246,0.3) !important; | |
| } | |
| /* ── Tab styling ── */ | |
| [data-testid="stTabs"] button { | |
| color: #94a3b8 !important; | |
| font-size: 0.92rem !important; | |
| font-weight: 500 !important; | |
| padding: 10px 20px !important; | |
| } | |
| [data-testid="stTabs"] button[aria-selected="true"] { | |
| color: #1e293b !important; | |
| border-bottom: 2px solid #3b82f6 !important; | |
| font-weight: 600 !important; | |
| } | |
| /* ── Verdict banner ── */ | |
| .verdict-box { | |
| border-radius: 16px; | |
| padding: 32px 36px; | |
| margin-bottom: 28px; | |
| display: flex; | |
| align-items: center; | |
| gap: 24px; | |
| animation: fadeSlide 0.5s ease; | |
| } | |
| @keyframes fadeSlide { | |
| from { opacity: 0; transform: translateY(-16px); } | |
| to { opacity: 1; transform: translateY(0); } | |
| } | |
| .verdict-emoji { font-size: 3.5rem; line-height: 1; } | |
| .verdict-label { font-size: 1.8rem; font-weight: 800; letter-spacing: -0.03em; } | |
| .verdict-conf { font-size: 1rem; opacity: 0.85; margin-top: 6px; font-weight: 400; } | |
| .verdict-explain { font-size: 0.88rem; color: #64748b; margin-top: 6px; line-height: 1.5; } | |
| /* ── Info cards ── */ | |
| .info-card { | |
| background: #ffffff; | |
| border: 1px solid #e2e8f0; | |
| border-radius: 12px; | |
| padding: 20px 24px; | |
| margin: 12px 0; | |
| line-height: 1.6; | |
| color: #475569; | |
| } | |
| .info-card b { color: #1e293b; } | |
| /* ── Freshness bar ── */ | |
| .fresh-track { background: #e2e8f0; border-radius: 8px; height: 12px; margin: 10px 0 6px; overflow: hidden; } | |
| .fresh-fill { height: 100%; border-radius: 8px; transition: width 0.8s ease; } | |
| /* ── Source card ── */ | |
| .source-card { | |
| background: #ffffff; | |
| border: 1px solid #e2e8f0; | |
| border-radius: 12px; | |
| padding: 18px 22px; | |
| margin: 10px 0; | |
| display: flex; | |
| justify-content: space-between; | |
| align-items: flex-start; | |
| gap: 16px; | |
| } | |
| .source-text { flex: 1; font-size: 0.88rem; line-height: 1.5; color: #475569; } | |
| .source-score { text-align: center; min-width: 60px; } | |
| .source-score-val { font-size: 1.4rem; font-weight: 700; font-family: 'Inter', sans-serif; } | |
| .source-score-tag { font-size: 0.65rem; text-transform: uppercase; letter-spacing: 0.1em; margin-top: 4px; } | |
| /* ── Hero ── */ | |
| .hero-wrap { text-align: center; padding: 60px 20px 40px; } | |
| .hero-icon { font-size: 4rem; margin-bottom: 16px; } | |
| .hero-title { font-size: 2.4rem; font-weight: 800; letter-spacing: -0.04em; color: #0f172a; } | |
| .hero-sub { font-size: 1.05rem; color: #64748b; margin-top: 12px; line-height: 1.6; max-width: 520px; margin-left: auto; margin-right: auto; } | |
| /* ── How-it-works ── */ | |
| .how-grid { display: grid; grid-template-columns: repeat(3, 1fr); gap: 16px; margin: 36px 0; } | |
| .how-card { | |
| background: #ffffff; | |
| border: 1px solid #e2e8f0; | |
| border-radius: 12px; | |
| padding: 24px; | |
| text-align: center; | |
| box-shadow: 0 1px 3px rgba(0,0,0,0.04); | |
| } | |
| .how-num { font-size: 2rem; margin-bottom: 8px; } | |
| .how-title { font-size: 0.95rem; font-weight: 600; margin-bottom: 6px; color: #0f172a; } | |
| .how-desc { font-size: 0.82rem; color: #64748b; line-height: 1.5; } | |
| /* ── Verdict legend ── */ | |
| .legend-row { | |
| display: flex; | |
| gap: 24px; | |
| justify-content: center; | |
| flex-wrap: wrap; | |
| margin: 20px 0; | |
| } | |
| .legend-item { font-size: 0.85rem; color: #64748b; } | |
| /* ── Metric overrides ── */ | |
| [data-testid="stMetric"] { | |
| background: #ffffff; | |
| border: 1px solid #e2e8f0; | |
| border-radius: 10px; | |
| padding: 14px 18px !important; | |
| box-shadow: 0 1px 3px rgba(0,0,0,0.04); | |
| } | |
| [data-testid="stMetricLabel"] { color: #64748b !important; font-size: 0.78rem !important; } | |
| [data-testid="stMetricValue"] { color: #0f172a !important; font-size: 1.3rem !important; } | |
| /* ── Expander ── */ | |
| [data-testid="stExpander"] { | |
| background: #ffffff !important; | |
| border: 1px solid #e2e8f0 !important; | |
| border-radius: 10px !important; | |
| } | |
| /* ── Text inputs ── */ | |
| [data-testid="stTextInput"] input, [data-testid="stTextArea"] textarea { | |
| background: #ffffff !important; | |
| border: 1px solid #cbd5e1 !important; | |
| border-radius: 8px !important; | |
| color: #1e293b !important; | |
| } | |
| [data-testid="stTextInput"] input:focus, [data-testid="stTextArea"] textarea:focus { | |
| border-color: #3b82f6 !important; | |
| box-shadow: 0 0 0 2px rgba(59,130,246,0.15) !important; | |
| } | |
| /* ── Select slider / radio ── */ | |
| [data-testid="stSlider"] label, .stRadio label { color: #475569 !important; } | |
| /* ── Progress bar ── */ | |
| [data-testid="stProgress"] > div > div > div > div { background: linear-gradient(90deg, #3b82f6, #6366f1) !important; } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # ── Cached inference loader ────────────────────────────────────────────────── | |
| def load_pipeline(): | |
| from src.stage4_inference import predict_article, ModelNotTrainedError | |
| return predict_article, ModelNotTrainedError | |
| # ── Session state ──────────────────────────────────────────────────────────── | |
| for k, v in [("analyzed", False), ("last_result", None), ("last_input", "")]: | |
| if k not in st.session_state: | |
| st.session_state[k] = v | |
| # ============================================================================= | |
| # LANDING PAGE (shown before any analysis) | |
| # ============================================================================= | |
| if not st.session_state["analyzed"]: | |
| # ── Hero section ── | |
| st.markdown(""" | |
| <div class="hero-wrap"> | |
| <div class="hero-icon">🔍</div> | |
| <div class="hero-title">TruthLens</div> | |
| <div class="hero-sub"> | |
| Paste any news article or drop a URL — our AI will tell you | |
| if it's real, fake, or outdated in seconds. | |
| </div> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # ── How it works ── | |
| st.markdown(""" | |
| <div class="how-grid"> | |
| <div class="how-card"> | |
| <div class="how-num">📋</div> | |
| <div class="how-title">Paste or Link</div> | |
| <div class="how-desc">Drop in the article text or a URL. We'll extract everything automatically.</div> | |
| </div> | |
| <div class="how-card"> | |
| <div class="how-num">⚡</div> | |
| <div class="how-title">Instant Analysis</div> | |
| <div class="how-desc">Our AI analyzes language patterns, checks freshness, and searches live sources.</div> | |
| </div> | |
| <div class="how-card"> | |
| <div class="how-num">✅</div> | |
| <div class="how-title">Get Your Verdict</div> | |
| <div class="how-desc">See a clear REAL / FAKE / OUTDATED verdict with a confidence score and explanation.</div> | |
| </div> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # ── Input area ── | |
| input_tab = st.radio("How would you like to provide the article?", | |
| ["✍️ Write or paste text", "🔗 Paste a URL"], | |
| horizontal=True, label_visibility="visible") | |
| input_text, input_title, input_url, input_date, input_domain = "", "", "", "", "" | |
| if input_tab == "✍️ Write or paste text": | |
| input_title = st.text_input("Headline (optional)", | |
| placeholder="e.g. Breaking: Scientists discover high-speed interstellar travel") | |
| input_text = st.text_area("Article content", | |
| height=180, | |
| placeholder="Paste the full article body here…") | |
| # ── Auto-extract title from pasted text if headline field is empty ── | |
| if not input_title.strip() and input_text.strip(): | |
| if input_text.lower().startswith("title:"): | |
| lines = input_text.split("\n", 1) | |
| input_title = lines[0].replace("Title:", "").replace("title:", "").strip() | |
| input_text = lines[1].replace("Body:", "").replace("body:", "").strip() if len(lines) > 1 else "" | |
| else: | |
| # Fallback: first sentence is title | |
| input_title = input_text.split(".")[0].strip() | |
| else: | |
| input_url = st.text_input("Article URL", | |
| placeholder="https://www.example.com/news/breaking-story") | |
| st.caption("We'll automatically extract the title, body, and publish date.") | |
| # ── Analysis mode (kept minimal — user doesn't need to understand internals) | |
| speed = st.select_slider("Analysis depth", | |
| options=["Quick", "Standard", "Deep"], | |
| value="Deep", | |
| help="Quick ≈ 2 sec · Standard ≈ 10 sec · Deep ≈ 30 sec (most accurate)") | |
| speed_map = {"Quick": "fast", "Standard": "balanced", "Deep": "full"} | |
| selected_mode = speed_map[speed] | |
| # ── Predict button ── | |
| predict_clicked = st.button("🔍 Check this article", use_container_width=True, type="primary") | |
| # ── Verdict legend ── | |
| st.markdown(""" | |
| <div class="legend-row"> | |
| <div class="legend-item">🟢 Verified True</div> | |
| <div class="legend-item">🔴 Likely Fake</div> | |
| <div class="legend-item">🟡 Outdated</div> | |
| <div class="legend-item">🟠 Needs Review</div> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # ── Execute prediction ── | |
| if predict_clicked: | |
| # Validate | |
| if input_tab == "✍️ Write or paste text": | |
| if not input_text or len(input_text.split()) < 10: | |
| st.warning("⚠️ Please paste at least a few sentences so we can analyze it properly.") | |
| st.stop() | |
| else: | |
| if not input_url: | |
| st.warning("⚠️ Please enter a URL first.") | |
| st.stop() | |
| try: | |
| import newspaper | |
| from urllib.parse import urlparse | |
| art = newspaper.Article(input_url) | |
| art.download() | |
| art.parse() | |
| input_title = art.title or "" | |
| input_text = art.text or "" | |
| input_date = art.publish_date.isoformat() if art.publish_date else "" | |
| input_domain = urlparse(input_url).netloc | |
| if len(input_text.split()) < 10: | |
| st.warning("⚠️ Couldn't extract enough text from that URL. Try pasting the article directly.") | |
| st.stop() | |
| except Exception: | |
| st.error("❌ Couldn't fetch that URL. Please check the link or paste the text directly.") | |
| st.stop() | |
| predict_article, ModelNotTrainedError = load_pipeline() | |
| with st.status("🔍 Analyzing article…", expanded=True) as status: | |
| st.write("📖 Reading article…") | |
| time.sleep(0.3) | |
| st.write("🧠 Running AI analysis…") | |
| try: | |
| result = predict_article( | |
| title=input_title, | |
| text=input_text, | |
| source_domain=input_domain, | |
| published_date=input_date, | |
| mode=selected_mode, | |
| ) | |
| st.write("🕐 Checking article freshness…") | |
| st.write("🌐 Searching live sources…") | |
| status.update(label="✅ Done!", state="complete") | |
| st.session_state["last_result"] = result | |
| st.session_state["last_input"] = input_text | |
| st.session_state["analyzed"] = True | |
| st.rerun() | |
| except ModelNotTrainedError: | |
| status.update(label="❌ Setup required", state="error") | |
| st.error("The AI models haven't been trained yet.") | |
| st.info("Ask your administrator to run: `python run_pipeline.py --stage 1 2 3`") | |
| st.stop() | |
| except Exception as e: | |
| status.update(label="❌ Error", state="error") | |
| st.error(f"Something went wrong: {e}") | |
| st.stop() | |
| # ============================================================================= | |
| # RESULTS PAGE (shown after analysis) | |
| # ============================================================================= | |
| else: | |
| res = st.session_state["last_result"] | |
| verdict = res.get("verdict", "UNKNOWN") | |
| final_score = res.get("final_score", 0.0) | |
| scores = res.get("scores", {}) | |
| confidence = res.get("confidence", "MEDIUM") | |
| action = res.get("recommended_action", "Flag for review") | |
| top_reasons = res.get("top_reasons", []) | |
| missing_signals = res.get("missing_signals", []) | |
| adv_flags = res.get("adversarial_flags", []) | |
| wc = res.get("word_count", 0) | |
| probas = res.get("base_model_probas", {}) | |
| votes = res.get("base_model_votes", {}) | |
| fresh_case = res.get("freshness_case", "B") | |
| fresh_signals = res.get("freshness_signals_found", []) | |
| deductions = res.get("deductions_applied", []) | |
| entities = res.get("entities_found", []) | |
| # ── Map verdict to display ── | |
| V = { | |
| "TRUE": {"bg":"#f0fdf4", "bdr":"#86efac", "icon":"🟢", "label":"This appears to be true", "color":"#15803d", | |
| "explain":"Source, claims, language, and AI models all align with credible journalism."}, | |
| "UNCERTAIN": {"bg":"#fff7ed", "bdr":"#fdba74", "icon":"🟠", "label":"Uncertain — needs review", "color":"#c2410c", | |
| "explain":"Mixed signals detected. We recommend verifying the sources yourself before sharing."}, | |
| "LIKELY FALSE": {"bg":"#fef2f2", "bdr":"#fca5a5", "icon":"🔴", "label":"Likely false", "color":"#b91c1c", | |
| "explain":"Multiple signals indicate this content may be fabricated or misleading."}, | |
| "FALSE": {"bg":"#fef2f2", "bdr":"#fca5a5", "icon":"⛔", "label":"This looks fake", "color":"#991b1b", | |
| "explain":"Strong evidence of misinformation. Do not share without independent verification."}, | |
| } | |
| vc = V.get(verdict, {"bg":"#f8fafc","bdr":"#cbd5e1","icon":"⚪","label":verdict,"color":"#475569", | |
| "explain":"Analysis complete."}) | |
| # ── Verdict banner ── | |
| score_pct = final_score * 100 | |
| st.markdown(f""" | |
| <div class="verdict-box" style="background:{vc['bg']}; border:1px solid {vc['bdr']};"> | |
| <div class="verdict-emoji">{vc['icon']}</div> | |
| <div> | |
| <div class="verdict-label" style="color:{vc['color']};">{vc['label']}</div> | |
| <div class="verdict-conf" style="color:{vc['color']};">Score: {score_pct:.0f}% · Confidence: {confidence}</div> | |
| <div class="verdict-explain">{vc['explain']}</div> | |
| </div> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # ── Recommended action badge ── | |
| action_colors = { | |
| "Publish": ("#f0fdf4", "#15803d"), | |
| "Flag for review": ("#fff7ed", "#c2410c"), | |
| "Suppress": ("#fef2f2", "#b91c1c"), | |
| "Escalate": ("#fef2f2", "#991b1b"), | |
| } | |
| abg, acol = action_colors.get(action, ("#f8fafc", "#475569")) | |
| st.markdown(f""" | |
| <div style="background:{abg}; border-radius:8px; padding:10px 16px; display:inline-block; margin-bottom:24px;"> | |
| <span style="font-weight:600; color:{acol};">Recommended: {action}</span> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # ── Tabs ── | |
| tab_why, tab_fresh, tab_sources, tab_details = st.tabs( | |
| ["🧠 Why this verdict?", "🕐 Freshness", "🌐 Live sources", "📋 Details"] | |
| ) | |
| # ── TAB 1: Why this verdict ────────────────────────────────────────── | |
| with tab_why: | |
| # ── 5-Signal Score Breakdown ── | |
| st.markdown("#### Signal Breakdown") | |
| SIGNAL_INFO = [ | |
| ("Source", "source", "Is the outlet known and accountable?"), | |
| ("Claims", "claim", "Are facts verifiable with named entities?"), | |
| ("Language", "linguistic", "Is the writing neutral and attributed?"), | |
| ("Freshness", "freshness", "How recent is the content?"), | |
| ("AI Models", "model_vote", "What do the AI models think?"), | |
| ] | |
| WEIGHTS = {"source": "30%", "claim": "30%", "linguistic": "20%", "freshness": "10%", "model_vote": "10%"} | |
| cols = st.columns(5) | |
| for i, (label, key, desc) in enumerate(SIGNAL_INFO): | |
| val = scores.get(key, 0.0) | |
| pct = val * 100 | |
| if pct >= 70: | |
| col_hex = "#15803d" | |
| elif pct >= 50: | |
| col_hex = "#ca8a04" | |
| else: | |
| col_hex = "#b91c1c" | |
| with cols[i]: | |
| st.markdown(f""" | |
| <div style="text-align:center; background:#ffffff; border:1px solid #e2e8f0; | |
| border-radius:10px; padding:16px 8px; box-shadow:0 1px 3px rgba(0,0,0,0.04);"> | |
| <div style="font-size:1.6rem; font-weight:800; color:{col_hex};">{pct:.0f}%</div> | |
| <div style="font-size:0.85rem; font-weight:600; color:#0f172a; margin-top:4px;">{label}</div> | |
| <div style="font-size:0.7rem; color:#94a3b8; margin-top:2px;">Weight: {WEIGHTS[key]}</div> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| st.markdown("") | |
| # ── Progress bars for each signal ── | |
| for label, key, desc in SIGNAL_INFO: | |
| val = scores.get(key, 0.0) | |
| st.caption(f"**{label}** — {desc}") | |
| st.progress(min(val, 1.0)) | |
| st.markdown("---") | |
| # ── Top Reasons ── | |
| if top_reasons: | |
| st.markdown("#### Key Factors") | |
| for r in top_reasons: | |
| if any(neg in r.lower() for neg in ["fake", "false", "unknown", "not", "manipulation", "adversarial", "sensationalism", "reduces", "could not", "inconsistent", "missing"]): | |
| st.markdown(f"🔴 {r}") | |
| else: | |
| st.markdown(f"🟢 {r}") | |
| st.markdown("---") | |
| # ── What did each AI model think? ── | |
| st.markdown("#### AI Model Votes") | |
| MODEL_NAMES = [ | |
| ("Statistical", "logistic", "lr_proba"), | |
| ("Language", "lstm", "lstm_proba"), | |
| ("Deep A", "distilbert", "distilbert_proba"), | |
| ("Deep B", "roberta", "roberta_proba"), | |
| ] | |
| mcols = st.columns(len(MODEL_NAMES)) | |
| for i, (nice_name, vote_key, pk) in enumerate(MODEL_NAMES): | |
| vote_val = votes.get(vote_key) | |
| prob_val = probas.get(pk) | |
| with mcols[i]: | |
| if vote_val is None or prob_val is None or np.isnan(prob_val): | |
| st.metric(nice_name, "Skipped") | |
| else: | |
| lbl = "Real" if int(vote_val) == 1 else "Fake" | |
| st.metric(nice_name, lbl, f"{prob_val*100:.0f}%") | |
| if res.get("short_text_warning"): | |
| st.warning("⚠️ Short article (under 50 words) — confidence is dampened.") | |
| st.caption(f"Article length: {wc} words") | |
| # ── TAB 2: Freshness ───────────────────────────────────────────────── | |
| with tab_fresh: | |
| fresh_val = scores.get("freshness", 0.5) | |
| bar_pct = int(fresh_val * 100) | |
| if fresh_val >= 0.70: | |
| fbg, flbl, fdesc = "#f0fdf4", "🟢 Fresh", "This article appears to be recent." | |
| fbar = "#16a34a" | |
| elif fresh_val >= 0.40: | |
| fbg, flbl, fdesc = "#fefce8", "🟡 Moderate", "Article may not be very recent." | |
| fbar = "#ca8a04" | |
| else: | |
| fbg, flbl, fdesc = "#fef2f2", "🔴 Outdated", "This article appears to be old." | |
| fbar = "#dc2626" | |
| st.markdown(f""" | |
| <div style="background:{fbg}; border-radius:12px; padding:20px 24px; margin-bottom:20px;"> | |
| <div style="font-size:1.2rem; font-weight:600;">{flbl}</div> | |
| <div style="font-size:0.88rem; color:#64748b; margin-top:8px;">{fdesc}</div> | |
| <div class="fresh-track"> | |
| <div class="fresh-fill" style="width:{bar_pct}%; background:{fbar};"></div> | |
| </div> | |
| <div style="font-size:0.8rem; color:#6b7280; margin-top:4px;">Freshness: {fresh_val:.0%}</div> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Case indicator | |
| case_label = "📅 Date-based scoring" if fresh_case == "A" else "🔎 Contextual signal scanning (no date found)" | |
| st.markdown(f""" | |
| <div class="info-card"> | |
| <b>Method:</b> {case_label} | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Signals found (Case B) | |
| if fresh_case == "B" and fresh_signals: | |
| st.markdown("**Signals detected:**") | |
| for sig in fresh_signals: | |
| st.markdown(f"✅ {sig}") | |
| elif fresh_case == "B": | |
| st.caption("No contextual freshness signals were found in the article text.") | |
| # ── TAB 3: Live sources ────────────────────────────────────────────── | |
| with tab_sources: | |
| rag_data = res.get("rag_results") | |
| source_list = [] | |
| if isinstance(rag_data, dict): | |
| source_list = rag_data.get("data", []) | |
| elif isinstance(rag_data, list): | |
| source_list = rag_data | |
| if not source_list: | |
| st.markdown(""" | |
| <div class="info-card"> | |
| <b>Live source check was not triggered</b><br><br> | |
| Live source verification runs when freshness is ambiguous. | |
| This analysis relied on the 5-signal scoring framework instead. | |
| </div> | |
| """, unsafe_allow_html=True) | |
| else: | |
| st.caption(f"Compared against {len(source_list)} live web results.") | |
| for item in source_list: | |
| snippet = item.get("snippet", "") | |
| sim = item.get("similarity", 0.0) | |
| if sim > 0.65: | |
| sc_col, sc_tag = "#16a34a", "Supports" | |
| elif sim < 0.30: | |
| sc_col, sc_tag = "#dc2626", "Conflicts" | |
| else: | |
| sc_col, sc_tag = "#ca8a04", "Neutral" | |
| st.markdown(f""" | |
| <div class="source-card"> | |
| <div class="source-text">{snippet}</div> | |
| <div class="source-score"> | |
| <div class="source-score-val" style="color:{sc_col};">{sim:.0%}</div> | |
| <div class="source-score-tag" style="color:{sc_col};">{sc_tag}</div> | |
| </div> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # ── TAB 4: Details ─────────────────────────────────────────────────── | |
| with tab_details: | |
| # ── Missing Signals ── | |
| if missing_signals: | |
| st.markdown("#### ⚠️ Missing Signals") | |
| for ms in missing_signals: | |
| st.markdown(f"- {ms}") | |
| st.markdown("") | |
| # ── Adversarial Flags ── | |
| if adv_flags: | |
| st.markdown("#### 🚩 Adversarial Flags Triggered") | |
| for af in adv_flags: | |
| st.error(f"🚩 {af}") | |
| st.caption("Adversarial flags cap the final score at 25% maximum.") | |
| st.markdown("") | |
| # ── Linguistic Deductions ── | |
| if deductions: | |
| st.markdown("#### 📝 Linguistic Deductions") | |
| for d in deductions: | |
| st.markdown(f"- {d}") | |
| st.markdown("") | |
| # ── Named Entities Found ── | |
| if entities: | |
| st.markdown("#### 🏷️ Entities Detected") | |
| st.markdown(", ".join([f"`{e}`" for e in entities])) | |
| q_attr = res.get("quotes_attributed", 0) | |
| q_total = res.get("quotes_total", 0) | |
| if q_total > 0: | |
| st.caption(f"Quotes: {q_attr}/{q_total} attributed") | |
| st.markdown("") | |
| # ── Summary Table ── | |
| st.markdown("#### Analysis Summary") | |
| rows = [ | |
| ("Verdict", vc["label"]), | |
| ("Final Score", f"{score_pct:.1f}%"), | |
| ("Confidence", confidence), | |
| ("Action", action), | |
| ("Word Count", str(wc)), | |
| ("Freshness", f"{scores.get('freshness', 0):.0%} (Case {fresh_case})"), | |
| ] | |
| df_rep = pd.DataFrame(rows, columns=["Field", "Value"]) | |
| st.dataframe(df_rep, use_container_width=True, hide_index=True, height=240) | |
| with st.expander("🔧 Raw JSON (for developers)"): | |
| st.code(json.dumps(res, indent=2, default=str), language="json") | |
| # ── Analyze another ── | |
| st.markdown("---") | |
| if st.button("← Analyze another article", use_container_width=True): | |
| st.session_state["analyzed"] = False | |
| st.session_state["last_result"] = None | |
| st.rerun() | |