feat: AI webpage sentiment and insight analyzer

requirements.txt

@@ -1,5 +1,4 @@
 streamlit>=1.32.0
-chromadb>=0.4.22
-sentence-transformers>=2.7.0
 requests>=2.31.0
-PyMuPDF>=1.24.0
+PyMuPDF>=1.24.0
+beautifulsoup4>=4.12.0

sentiment_analyzer_app.py

@@ -0,0 +1,311 @@
+import streamlit as st
+import requests
+import os
+import json
+import re
+from bs4 import BeautifulSoup
+from urllib.parse import urlparse
+
+st.set_page_config(page_title="AI Sentiment Analyzer", page_icon="🌐", layout="wide")
+
+st.markdown("""
+<style>
+@import url('https://fonts.googleapis.com/css2?family=Sora:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap');
+html, body, [class*="css"] { font-family: 'Sora', sans-serif; }
+.main { background: #0a0a0f; }
+
+.hero {
+    background: linear-gradient(135deg, #0d0d1a 0%, #0a0a0f 100%);
+    border: 1px solid #1e1e2e; border-top: 3px solid #a78bfa;
+    border-radius: 14px; padding: 28px 32px; margin-bottom: 24px;
+}
+.hero h1 { font-size: 1.8rem; font-weight: 700; color: #f1f5f9; margin: 0 0 6px 0; }
+.hero p { color: #4b5563; font-size: 0.88rem; margin: 0; }
+
+.insight-card {
+    background: #0d0d1a; border: 1px solid #1e1e2e;
+    border-radius: 12px; padding: 20px 24px; margin: 10px 0;
+}
+
+/* Sentiment meter */
+.sentiment-positive { color: #4ade80; }
+.sentiment-negative { color: #f87171; }
+.sentiment-neutral  { color: #94a3b8; }
+.sentiment-mixed    { color: #fbbf24; }
+
+.big-sentiment {
+    font-size: 3rem; font-weight: 700; text-align: center;
+    padding: 20px; letter-spacing: -0.02em;
+}
+.sentiment-score-label {
+    text-align: center; font-size: 0.82rem; color: #4b5563;
+    font-family: 'JetBrains Mono', monospace;
+}
+
+.entity-tag {
+    display: inline-block; border-radius: 6px;
+    padding: 4px 10px; font-size: 0.78rem; margin: 3px;
+    font-family: 'JetBrains Mono', monospace;
+}
+.entity-person   { background: rgba(167,139,250,0.12); color: #a78bfa; border: 1px solid rgba(167,139,250,0.25); }
+.entity-org      { background: rgba(59,130,246,0.1);  color: #60a5fa; border: 1px solid rgba(59,130,246,0.25); }
+.entity-location { background: rgba(34,197,94,0.1);   color: #4ade80; border: 1px solid rgba(34,197,94,0.25); }
+.entity-topic    { background: rgba(251,191,36,0.1);   color: #fbbf24; border: 1px solid rgba(251,191,36,0.25); }
+.entity-product  { background: rgba(248,113,113,0.1);  color: #f87171; border: 1px solid rgba(248,113,113,0.25); }
+
+.theme-pill {
+    display: inline-block; background: #1e1e2e; border: 1px solid #2d2d3e;
+    border-radius: 20px; padding: 5px 14px; margin: 4px;
+    font-size: 0.8rem; color: #94a3b8;
+}
+
+.section-label {
+    font-size: 0.68rem; text-transform: uppercase; letter-spacing: 0.1em;
+    color: #2d2d3e; font-weight: 600; margin: 18px 0 8px 0;
+}
+
+.stat-row { display: flex; gap: 10px; margin: 16px 0; }
+.stat-box {
+    flex: 1; background: #0d0d1a; border: 1px solid #1e1e2e;
+    border-radius: 10px; padding: 14px; text-align: center;
+}
+.stat-val { font-size: 1.3rem; font-weight: 700; color: #f1f5f9; }
+.stat-lbl { font-size: 0.68rem; color: #4b5563; margin-top: 2px; }
+
+.url-chip {
+    background: #0d0d1a; border: 1px solid #1e1e2e; border-radius: 8px;
+    padding: 10px 14px; font-family: 'JetBrains Mono', monospace;
+    font-size: 0.78rem; color: #4b5563; word-break: break-all;
+    margin-bottom: 16px;
+}
+
+section[data-testid="stSidebar"] { background: #060609; border-right: 1px solid #1e1e2e; }
+</style>
+""", unsafe_allow_html=True)
+
+
+def fetch_url_text(url: str) -> tuple[str, str]:
+    headers = {"User-Agent": "Mozilla/5.0 (compatible; InsightBot/1.0)"}
+    r = requests.get(url, headers=headers, timeout=15)
+    r.raise_for_status()
+    soup = BeautifulSoup(r.text, "html.parser")
+    title = soup.title.string.strip() if soup.title else urlparse(url).netloc
+    for tag in soup(["script", "style", "nav", "footer", "header", "aside", "form"]):
+        tag.decompose()
+    text = soup.get_text(separator=" ", strip=True)
+    text = re.sub(r'\s+', ' ', text).strip()
+    return text[:4000], title
+
+
+def analyze_content(text: str, url: str, title: str, api_key: str) -> dict:
+    prompt = f"""You are an expert content analyst. Analyze the following webpage content and extract deep insights.
+
+Source URL: {url}
+Page Title: {title}
+
+Content:
+{text}
+
+Respond ONLY with a valid JSON object in exactly this format:
+{{
+  "sentiment": "<one of: Positive | Negative | Neutral | Mixed>",
+  "sentiment_score": <float between -1.0 (very negative) and 1.0 (very positive)>,
+  "sentiment_explanation": "<1-2 sentences explaining the sentiment>",
+  "one_line_summary": "<single sentence capturing the entire content>",
+  "key_themes": ["<theme 1>", "<theme 2>", "<theme 3>", "<theme 4>", "<theme 5>"],
+  "named_entities": {{
+    "persons": ["<name>"],
+    "organizations": ["<org>"],
+    "locations": ["<location>"],
+    "products": ["<product>"]
+  }},
+  "content_type": "<one of: News Article | Product Page | Review | Blog Post | Research | Social Media | Other>",
+  "target_audience": "<who this content is written for>",
+  "key_insights": ["<insight 1>", "<insight 2>", "<insight 3>"],
+  "tone": "<one of: Informative | Promotional | Critical | Analytical | Emotional | Persuasive | Neutral>",
+  "credibility_signals": ["<signal 1>", "<signal 2>"],
+  "word_count_estimate": <integer>
+}}"""
+
+    headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
+    payload = {
+        "model": "llama-3.3-70b-versatile",
+        "messages": [{"role": "user", "content": prompt}],
+        "max_tokens": 1000,
+        "temperature": 0.1,
+    }
+    r = requests.post("https://api.groq.com/openai/v1/chat/completions",
+                      headers=headers, json=payload, timeout=30)
+    r.raise_for_status()
+    raw = r.json()["choices"][0]["message"]["content"]
+    raw = re.sub(r"```json|```", "", raw).strip()
+    return json.loads(raw)
+
+
+# ─── Sidebar ──────────────────────────────────────────────────────────────────
+with st.sidebar:
+    st.markdown("## 🌐 Sentiment Analyzer")
+    st.markdown("<div style='color:#2d2d3e;font-size:0.8rem'>AI-powered content intelligence</div>", unsafe_allow_html=True)
+    st.markdown("---")
+    env_key = os.environ.get("GROQ_API_KEY", "")
+    api_key = env_key if env_key else st.text_input("🔑 Groq API Key", type="password", placeholder="gsk_...")
+    if not env_key and not api_key:
+        st.caption("Free key → [console.groq.com](https://console.groq.com)")
+    st.markdown("---")
+    st.markdown("""
+<div style='font-size:0.78rem;color:#2d2d3e;line-height:2'>
+<b style='color:#4b5563'>Extracts</b><br>
+🎭 Sentiment & Score<br>
+🏷️ Key Themes<br>
+👤 Named Entities<br>
+💡 Key Insights<br>
+🎯 Target Audience<br>
+🗣️ Content Tone<br>
+📰 Content Type
+</div>""", unsafe_allow_html=True)
+    st.markdown("---")
+    st.markdown("""
+<div style='font-size:0.78rem;color:#2d2d3e;line-height:2'>
+<b style='color:#4b5563'>Try these URLs</b><br>
+• Any news article<br>
+• Amazon product page<br>
+• Wikipedia article<br>
+• Company blog post<br>
+• G2 / Trustpilot review
+</div>""", unsafe_allow_html=True)
+
+
+# ─── Main UI ──────────────────────────────────────────────────────────────────
+st.markdown("""
+<div class='hero'>
+  <h1>🌐 AI Webpage Sentiment & Insight Analyzer</h1>
+  <p>Paste any URL — AI extracts sentiment, themes, entities, tone, and key insights in seconds</p>
+</div>
+""", unsafe_allow_html=True)
+
+if not api_key:
+    st.warning("👈 Add your Groq API key in the sidebar.")
+    st.stop()
+
+st.markdown("<div class='section-label'>Paste a URL to analyze</div>", unsafe_allow_html=True)
+col_input, col_btn = st.columns([5, 1])
+with col_input:
+    url_input = st.text_input("URL", placeholder="https://...", label_visibility="collapsed")
+with col_btn:
+    analyze_btn = st.button("Analyze ➤", type="primary", use_container_width=True)
+
+# Example URLs
+st.markdown("<div class='section-label'>Quick examples</div>", unsafe_allow_html=True)
+examples = [
+    "https://en.wikipedia.org/wiki/Artificial_intelligence",
+    "https://techcrunch.com",
+    "https://www.bbc.com/news",
+]
+cols = st.columns(len(examples))
+clicked_url = None
+for i, ex in enumerate(examples):
+    parsed = urlparse(ex)
+    label = parsed.netloc
+    if cols[i].button(f"🔗 {label}", key=f"ex_{i}", use_container_width=True):
+        clicked_url = ex
+
+final_url = clicked_url or (url_input if analyze_btn else None)
+
+if final_url:
+    with st.spinner(f"Fetching and analyzing {final_url}..."):
+        try:
+            content_text, page_title = fetch_url_text(final_url)
+            result = analyze_content(content_text, final_url, page_title, api_key)
+
+            sentiment = result.get("sentiment", "Neutral")
+            score = result.get("sentiment_score", 0)
+            sentiment_color = (
+                "#4ade80" if sentiment == "Positive" else
+                "#f87171" if sentiment == "Negative" else
+                "#fbbf24" if sentiment == "Mixed" else
+                "#94a3b8"
+            )
+            sentiment_emoji = (
+                "😊" if sentiment == "Positive" else
+                "😟" if sentiment == "Negative" else
+                "😐" if sentiment == "Neutral" else "🤔"
+            )
+            score_pct = int((score + 1) / 2 * 100)
+
+            st.markdown(f"<div class='url-chip'>🔗 {final_url}</div>", unsafe_allow_html=True)
+            st.markdown(f"### 📄 {page_title}")
+
+            # Top row
+            col_sent, col_summary = st.columns([1, 2])
+
+            with col_sent:
+                st.markdown(f"""
+<div class='insight-card' style='text-align:center'>
+  <div style='font-size:0.72rem;text-transform:uppercase;letter-spacing:0.1em;color:#4b5563;margin-bottom:8px'>Sentiment</div>
+  <div style='font-size:3.5rem'>{sentiment_emoji}</div>
+  <div style='font-size:1.6rem;font-weight:700;color:{sentiment_color};margin:4px 0'>{sentiment}</div>
+  <div style='font-family:JetBrains Mono,monospace;font-size:0.8rem;color:#4b5563'>score: {score:+.2f}</div>
+  <div style='background:#1e1e2e;border-radius:4px;height:6px;margin:10px 0'>
+    <div style='height:6px;border-radius:4px;width:{score_pct}%;background:{sentiment_color}'></div>
+  </div>
+  <div style='font-size:0.78rem;color:#4b5563;margin-top:8px'>{result.get("sentiment_explanation","")}</div>
+</div>
+""", unsafe_allow_html=True)
+
+            with col_summary:
+                entities = result.get("named_entities", {})
+                persons_html   = "".join([f"<span class='entity-tag entity-person'>👤 {e}</span>"   for e in entities.get("persons", [])[:4]])
+                orgs_html      = "".join([f"<span class='entity-tag entity-org'>🏢 {e}</span>"      for e in entities.get("organizations", [])[:4]])
+                locations_html = "".join([f"<span class='entity-tag entity-location'>📍 {e}</span>" for e in entities.get("locations", [])[:3]])
+                products_html  = "".join([f"<span class='entity-tag entity-product'>📦 {e}</span>"  for e in entities.get("products", [])[:3]])
+                entities_html  = persons_html + orgs_html + locations_html + products_html or "<span style='color:#4b5563;font-size:0.82rem'>None detected</span>"
+
+                st.markdown(f"""
+<div class='insight-card'>
+  <div style='font-size:0.72rem;text-transform:uppercase;letter-spacing:0.1em;color:#4b5563;margin-bottom:10px'>One-Line Summary</div>
+  <div style='font-size:1rem;color:#f1f5f9;font-weight:500;line-height:1.6;margin-bottom:16px'>"{result.get("one_line_summary","")}"</div>
+  <div style='display:flex;gap:16px;margin-bottom:14px'>
+    <div><span style='font-size:0.72rem;color:#4b5563'>Content Type</span><br><span style='color:#a78bfa;font-weight:600;font-size:0.88rem'>{result.get("content_type","")}</span></div>
+    <div><span style='font-size:0.72rem;color:#4b5563'>Tone</span><br><span style='color:#60a5fa;font-weight:600;font-size:0.88rem'>{result.get("tone","")}</span></div>
+    <div><span style='font-size:0.72rem;color:#4b5563'>Audience</span><br><span style='color:#4ade80;font-weight:600;font-size:0.88rem'>{result.get("target_audience","")}</span></div>
+  </div>
+  <div style='font-size:0.72rem;text-transform:uppercase;letter-spacing:0.1em;color:#4b5563;margin-bottom:8px'>Named Entities</div>
+  {entities_html}
+</div>
+""", unsafe_allow_html=True)
+
+            # Themes + Insights
+            col_themes, col_insights = st.columns(2)
+
+            with col_themes:
+                themes_html = "".join([f"<div class='theme-pill'>#{t}</div>" for t in result.get("key_themes", [])])
+                st.markdown(f"""
+<div class='insight-card'>
+  <div style='font-size:0.72rem;text-transform:uppercase;letter-spacing:0.1em;color:#4b5563;margin-bottom:12px'>🏷️ Key Themes</div>
+  {themes_html}
+</div>""", unsafe_allow_html=True)
+
+            with col_insights:
+                insights_html = "".join([f"<div style='padding:8px 0;border-bottom:1px solid #1e1e2e;font-size:0.87rem;color:#94a3b8;line-height:1.6'>→ {ins}</div>" for ins in result.get("key_insights", [])])
+                st.markdown(f"""
+<div class='insight-card'>
+  <div style='font-size:0.72rem;text-transform:uppercase;letter-spacing:0.1em;color:#4b5563;margin-bottom:12px'>💡 Key Insights</div>
+  {insights_html}
+</div>""", unsafe_allow_html=True)
+
+            # Credibility
+            cred = result.get("credibility_signals", [])
+            if cred:
+                cred_html = "".join([f"<span style='background:rgba(74,222,128,0.08);border:1px solid rgba(74,222,128,0.2);border-radius:6px;padding:4px 12px;margin:3px;display:inline-block;font-size:0.8rem;color:#4ade80'>✓ {c}</span>" for c in cred])
+                st.markdown(f"""
+<div class='insight-card'>
+  <div style='font-size:0.72rem;text-transform:uppercase;letter-spacing:0.1em;color:#4b5563;margin-bottom:10px'>🛡️ Credibility Signals</div>
+  {cred_html}
+</div>""", unsafe_allow_html=True)
+
+        except requests.exceptions.ConnectionError:
+            st.error("❌ Could not reach that URL. Make sure it's publicly accessible.")
+        except json.JSONDecodeError:
+            st.error("❌ AI returned unexpected output. Try again.")
+        except Exception as e:
+            st.error(f"❌ Error: {str(e)}")

shared_requirements.txt

@@ -0,0 +1,4 @@
+streamlit>=1.32.0
+requests>=2.31.0
+PyMuPDF>=1.24.0
+beautifulsoup4>=4.12.0

src/streamlit_app.py

@@ -1,468 +1,311 @@
 import streamlit as st
-import chromadb
-from sentence_transformers import SentenceTransformer
-import fitz  # PyMuPDF
-import os
 import requests
+import os
+import json
 import re
-import hashlib
+from bs4 import BeautifulSoup
+from urllib.parse import urlparse
 
-# ─── Page Config ──────────────────────────────────────────────────────────────
-st.set_page_config(
-    page_title="PDF RAG · Upload & Ask",
-    page_icon="📂",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
+st.set_page_config(page_title="AI Sentiment Analyzer", page_icon="🌐", layout="wide")
 
-# ─── CSS ──────────────────────────────────────────────────────────────────────
 st.markdown("""
 <style>
-@import url('https://fonts.googleapis.com/css2?family=IBM+Plex+Sans:wght@300;400;500;600&family=IBM+Plex+Mono:wght@400;500&display=swap');
-
-html, body, [class*="css"] { font-family: 'IBM Plex Sans', sans-serif; }
-.main { background-color: #0b0f1a; }
+@import url('https://fonts.googleapis.com/css2?family=Sora:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap');
+html, body, [class*="css"] { font-family: 'Sora', sans-serif; }
+.main { background: #0a0a0f; }
 
 .hero {
-    background: linear-gradient(160deg, #0d1424 0%, #0b0f1a 100%);
-    border: 1px solid #1e2a3e;
-    border-top: 3px solid #22d3ee;
-    border-radius: 12px;
-    padding: 28px 32px;
-    margin-bottom: 24px;
+    background: linear-gradient(135deg, #0d0d1a 0%, #0a0a0f 100%);
+    border: 1px solid #1e1e2e; border-top: 3px solid #a78bfa;
+    border-radius: 14px; padding: 28px 32px; margin-bottom: 24px;
 }
-.hero h1 { font-size: 1.8rem; font-weight: 600; color: #e2e8f0; margin: 0 0 6px 0; }
-.hero p { color: #64748b; font-size: 0.95rem; margin: 0; }
+.hero h1 { font-size: 1.8rem; font-weight: 700; color: #f1f5f9; margin: 0 0 6px 0; }
+.hero p { color: #4b5563; font-size: 0.88rem; margin: 0; }
 
-.phase-bar {
-    display: flex; gap: 0; margin-bottom: 28px;
-    border: 1px solid #1e2a3e; border-radius: 10px; overflow: hidden;
-}
-.phase {
-    flex: 1; padding: 10px 6px; text-align: center;
-    font-size: 0.75rem; color: #4b5563; background: #0d1117;
-    border-right: 1px solid #1e2a3e; line-height: 1.5;
-}
-.phase:last-child { border-right: none; }
-.phase.done { color: #22d3ee; background: rgba(34,211,238,0.05); }
-.phase.active { color: #f8fafc; background: rgba(34,211,238,0.1); font-weight: 600; }
-.phase-icon { font-size: 1.1rem; display: block; margin-bottom: 2px; }
-
-.pdf-card {
-    background: #0d1424;
-    border: 1px solid #1e2a3e;
-    border-radius: 10px;
-    padding: 14px 16px;
-    margin: 8px 0;
-    display: flex;
-    align-items: center;
-    justify-content: space-between;
-}
-.pdf-name { font-size: 0.85rem; color: #e2e8f0; font-weight: 500; }
-.pdf-meta { font-family: 'IBM Plex Mono', monospace; font-size: 0.72rem; color: #475569; margin-top: 3px; }
-.pdf-badge {
-    font-size: 0.72rem; font-family: 'IBM Plex Mono', monospace;
-    background: rgba(34,211,238,0.1); color: #22d3ee;
-    border: 1px solid rgba(34,211,238,0.25); padding: 3px 10px; border-radius: 20px;
+.insight-card {
+    background: #0d0d1a; border: 1px solid #1e1e2e;
+    border-radius: 12px; padding: 20px 24px; margin: 10px 0;
 }
 
-.answer-box {
-    background: #0d1424;
-    border: 1px solid #1e3a4a;
-    border-left: 3px solid #22d3ee;
-    border-radius: 10px;
-    padding: 22px 24px;
-    color: #e2e8f0;
-    line-height: 1.75;
-    font-size: 0.96rem;
-    margin: 12px 0 20px 0;
-}
+/* Sentiment meter */
+.sentiment-positive { color: #4ade80; }
+.sentiment-negative { color: #f87171; }
+.sentiment-neutral  { color: #94a3b8; }
+.sentiment-mixed    { color: #fbbf24; }
 
-.chunk-card {
-    background: #0d1117;
-    border: 1px solid #1e2a3e;
-    border-radius: 9px;
-    padding: 14px 18px;
-    margin: 8px 0;
+.big-sentiment {
+    font-size: 3rem; font-weight: 700; text-align: center;
+    padding: 20px; letter-spacing: -0.02em;
 }
-.chunk-top {
-    display: flex; justify-content: space-between;
-    align-items: center; margin-bottom: 8px;
+.sentiment-score-label {
+    text-align: center; font-size: 0.82rem; color: #4b5563;
+    font-family: 'JetBrains Mono', monospace;
 }
-.chunk-source { font-size: 0.77rem; font-weight: 600; color: #22d3ee; text-transform: uppercase; letter-spacing: 0.05em; }
-.chunk-page { font-family: 'IBM Plex Mono', monospace; font-size: 0.72rem; color: #475569; }
-.score-bar-wrap { display: flex; align-items: center; gap: 8px; }
-.score-bar {
-    height: 4px; border-radius: 2px; background: #1e2a3e; width: 80px; overflow: hidden;
-}
-.score-fill { height: 100%; border-radius: 2px; background: #22d3ee; }
-.score-num { font-family: 'IBM Plex Mono', monospace; font-size: 0.72rem; color: #22d3ee; }
-.chunk-text { font-size: 0.86rem; color: #94a3b8; line-height: 1.65; }
 
-.stat-row { display: flex; gap: 10px; margin: 16px 0; }
-.stat-box {
-    flex: 1; background: #0d1424; border: 1px solid #1e2a3e;
-    border-radius: 8px; padding: 12px; text-align: center;
+.entity-tag {
+    display: inline-block; border-radius: 6px;
+    padding: 4px 10px; font-size: 0.78rem; margin: 3px;
+    font-family: 'JetBrains Mono', monospace;
+}
+.entity-person   { background: rgba(167,139,250,0.12); color: #a78bfa; border: 1px solid rgba(167,139,250,0.25); }
+.entity-org      { background: rgba(59,130,246,0.1);  color: #60a5fa; border: 1px solid rgba(59,130,246,0.25); }
+.entity-location { background: rgba(34,197,94,0.1);   color: #4ade80; border: 1px solid rgba(34,197,94,0.25); }
+.entity-topic    { background: rgba(251,191,36,0.1);   color: #fbbf24; border: 1px solid rgba(251,191,36,0.25); }
+.entity-product  { background: rgba(248,113,113,0.1);  color: #f87171; border: 1px solid rgba(248,113,113,0.25); }
+
+.theme-pill {
+    display: inline-block; background: #1e1e2e; border: 1px solid #2d2d3e;
+    border-radius: 20px; padding: 5px 14px; margin: 4px;
+    font-size: 0.8rem; color: #94a3b8;
 }
-.stat-val { font-size: 1.35rem; font-weight: 600; color: #22d3ee; }
-.stat-lbl { font-size: 0.7rem; color: #475569; margin-top: 2px; }
 
 .section-label {
-    font-size: 0.7rem; text-transform: uppercase; letter-spacing: 0.1em;
-    color: #374151; font-weight: 600; margin: 18px 0 8px 0;
+    font-size: 0.68rem; text-transform: uppercase; letter-spacing: 0.1em;
+    color: #2d2d3e; font-weight: 600; margin: 18px 0 8px 0;
 }
 
-section[data-testid="stSidebar"] {
-    background-color: #080c14; border-right: 1px solid #131c2e;
+.stat-row { display: flex; gap: 10px; margin: 16px 0; }
+.stat-box {
+    flex: 1; background: #0d0d1a; border: 1px solid #1e1e2e;
+    border-radius: 10px; padding: 14px; text-align: center;
 }
-
-.empty-state {
-    text-align: center; padding: 48px 24px;
-    border: 2px dashed #1e2a3e; border-radius: 12px; color: #374151;
+.stat-val { font-size: 1.3rem; font-weight: 700; color: #f1f5f9; }
+.stat-lbl { font-size: 0.68rem; color: #4b5563; margin-top: 2px; }
+
+.url-chip {
+    background: #0d0d1a; border: 1px solid #1e1e2e; border-radius: 8px;
+    padding: 10px 14px; font-family: 'JetBrains Mono', monospace;
+    font-size: 0.78rem; color: #4b5563; word-break: break-all;
+    margin-bottom: 16px;
 }
-.empty-state .icon { font-size: 2.5rem; margin-bottom: 12px; }
-.empty-state p { font-size: 0.9rem; line-height: 1.6; }
+
+section[data-testid="stSidebar"] { background: #060609; border-right: 1px solid #1e1e2e; }
 </style>
 """, unsafe_allow_html=True)
 
 
-# ─── Session State ────────────────────────────────────────────────────────────
-if "indexed_files" not in st.session_state:
-    st.session_state.indexed_files = {}   # filename → {chunks, pages, size}
-if "chroma_collection" not in st.session_state:
-    st.session_state.chroma_collection = None
-if "chroma_client" not in st.session_state:
-    st.session_state.chroma_client = None
-if "total_chunks" not in st.session_state:
-    st.session_state.total_chunks = 0
-
-
-# ─── Load embedding model (cached globally) ───────────────────────────────────
-@st.cache_resource(show_spinner=False)
-def load_embed_model():
-    return SentenceTransformer('all-MiniLM-L6-v2')
-
-
-# ─── PDF Extraction ───────────────────────────────────────────────────────────
-def extract_text_from_pdf(pdf_bytes: bytes) -> list[dict]:
-    """Returns list of {page, text} dicts."""
-    doc = fitz.open(stream=pdf_bytes, filetype="pdf")
-    pages = []
-    for page_num, page in enumerate(doc, start=1):
-        text = page.get_text("text").strip()
-        if text:
-            pages.append({"page": page_num, "text": text})
-    doc.close()
-    return pages
-
-
-# ─── Chunking ─────────────────────────────────────────────────────────────────
-def chunk_text(pages: list[dict], chunk_size: int = 400, overlap: int = 60) -> list[dict]:
-    """Splits page text into overlapping word-based chunks."""
-    chunks = []
-    for p in pages:
-        words = p["text"].split()
-        start = 0
-        while start < len(words):
-            end = start + chunk_size
-            chunk_words = words[start:end]
-            chunk_text_str = " ".join(chunk_words).strip()
-            if len(chunk_text_str) > 60:
-                chunks.append({"text": chunk_text_str, "page": p["page"]})
-            start += chunk_size - overlap
-    return chunks
-
-
-# ─── Index PDF into ChromaDB ──────────────────────────────────��───────────────
-def index_pdf(filename: str, pdf_bytes: bytes, embed_model):
-    # Init or reuse ChromaDB
-    if st.session_state.chroma_client is None:
-        st.session_state.chroma_client = chromadb.Client()
-        st.session_state.chroma_collection = st.session_state.chroma_client.get_or_create_collection(
-            name="pdf_rag", metadata={"hnsw:space": "cosine"}
-        )
-
-    collection = st.session_state.chroma_collection
-
-    # Extract & chunk
-    pages = extract_text_from_pdf(pdf_bytes)
-    chunks = chunk_text(pages)
-
-    if not chunks:
-        return 0, 0
-
-    # Embed & add
-    texts = [c["text"] for c in chunks]
-    embeddings = embed_model.encode(texts, batch_size=32, show_progress_bar=False).tolist()
-
-    ids, docs, metas, embeds = [], [], [], []
-    for i, (chunk, emb) in enumerate(zip(chunks, embeddings)):
-        chunk_id = f"{hashlib.md5(filename.encode()).hexdigest()[:8]}_chunk_{i}"
-        ids.append(chunk_id)
-        docs.append(chunk["text"])
-        metas.append({"filename": filename, "page": chunk["page"]})
-        embeds.append(emb)
-
-    collection.add(ids=ids, embeddings=embeds, documents=docs, metadatas=metas)
-
-    st.session_state.indexed_files[filename] = {
-        "chunks": len(chunks),
-        "pages": len(pages),
-        "size_kb": round(len(pdf_bytes) / 1024, 1)
-    }
-    st.session_state.total_chunks += len(chunks)
-    return len(chunks), len(pages)
-
-
-# ─── RAG Query ────────────────────────────────────────────────────────────────
-def rag_query(question: str, embed_model, top_k: int, api_key: str):
-    collection = st.session_state.chroma_collection
-    q_emb = embed_model.encode(question).tolist()
-    results = collection.query(query_embeddings=[q_emb], n_results=top_k)
-
-    chunks = []
-    for i in range(len(results["documents"][0])):
-        distance = results["distances"][0][i]
-        chunks.append({
-            "text": results["documents"][0][i],
-            "filename": results["metadatas"][0][i]["filename"],
-            "page": results["metadatas"][0][i]["page"],
-            "relevance": round((1 - distance) * 100, 1),
-        })
-
-    context = "\n\n".join([
-        f"[Source: {c['filename']}, Page {c['page']}]\n{c['text']}" for c in chunks
-    ])
-
-    prompt = f"""You are a helpful assistant. Answer the user's question using ONLY the document context provided below. Be concise and clear. Always mention the source filename and page number when referencing specific information. If the answer cannot be found in the provided context, say "I couldn't find that information in the uploaded documents."
-
-Document Context:
-{context}
-
-Question: {question}
-
-Answer:"""
+def fetch_url_text(url: str) -> tuple[str, str]:
+    headers = {"User-Agent": "Mozilla/5.0 (compatible; InsightBot/1.0)"}
+    r = requests.get(url, headers=headers, timeout=15)
+    r.raise_for_status()
+    soup = BeautifulSoup(r.text, "html.parser")
+    title = soup.title.string.strip() if soup.title else urlparse(url).netloc
+    for tag in soup(["script", "style", "nav", "footer", "header", "aside", "form"]):
+        tag.decompose()
+    text = soup.get_text(separator=" ", strip=True)
+    text = re.sub(r'\s+', ' ', text).strip()
+    return text[:4000], title
+
+
+def analyze_content(text: str, url: str, title: str, api_key: str) -> dict:
+    prompt = f"""You are an expert content analyst. Analyze the following webpage content and extract deep insights.
+
+Source URL: {url}
+Page Title: {title}
+
+Content:
+{text}
+
+Respond ONLY with a valid JSON object in exactly this format:
+{{
+  "sentiment": "<one of: Positive | Negative | Neutral | Mixed>",
+  "sentiment_score": <float between -1.0 (very negative) and 1.0 (very positive)>,
+  "sentiment_explanation": "<1-2 sentences explaining the sentiment>",
+  "one_line_summary": "<single sentence capturing the entire content>",
+  "key_themes": ["<theme 1>", "<theme 2>", "<theme 3>", "<theme 4>", "<theme 5>"],
+  "named_entities": {{
+    "persons": ["<name>"],
+    "organizations": ["<org>"],
+    "locations": ["<location>"],
+    "products": ["<product>"]
+  }},
+  "content_type": "<one of: News Article | Product Page | Review | Blog Post | Research | Social Media | Other>",
+  "target_audience": "<who this content is written for>",
+  "key_insights": ["<insight 1>", "<insight 2>", "<insight 3>"],
+  "tone": "<one of: Informative | Promotional | Critical | Analytical | Emotional | Persuasive | Neutral>",
+  "credibility_signals": ["<signal 1>", "<signal 2>"],
+  "word_count_estimate": <integer>
+}}"""
 
     headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
     payload = {
         "model": "llama-3.3-70b-versatile",
         "messages": [{"role": "user", "content": prompt}],
-        "max_tokens": 600,
-        "temperature": 0.2,
+        "max_tokens": 1000,
+        "temperature": 0.1,
     }
-    r = requests.post("https://api.groq.com/openai/v1/chat/completions", headers=headers, json=payload, timeout=30)
+    r = requests.post("https://api.groq.com/openai/v1/chat/completions",
+                      headers=headers, json=payload, timeout=30)
     r.raise_for_status()
-    answer = r.json()["choices"][0]["message"]["content"]
-    return answer, chunks
-
-
-# ─── Determine current phase ──────────────────────────────────────────────────
-has_docs = len(st.session_state.indexed_files) > 0
-phase = 1 if not has_docs else 2
+    raw = r.json()["choices"][0]["message"]["content"]
+    raw = re.sub(r"```json|```", "", raw).strip()
+    return json.loads(raw)
 
 
 # ─── Sidebar ──────────────────────────────────────────────────────────────────
 with st.sidebar:
-    st.markdown("## 📂 PDF RAG Demo")
-    st.markdown("<div style='color:#374151;font-size:0.8rem'>Upload → Extract → Index → Ask</div>", unsafe_allow_html=True)
+    st.markdown("## 🌐 Sentiment Analyzer")
+    st.markdown("<div style='color:#2d2d3e;font-size:0.8rem'>AI-powered content intelligence</div>", unsafe_allow_html=True)
     st.markdown("---")
-
     env_key = os.environ.get("GROQ_API_KEY", "")
-    if env_key:
-        api_key = env_key
-        st.success("✅ Groq key loaded from secrets")
-    else:
-        api_key = st.text_input("🔑 Groq API Key", type="password", placeholder="gsk_...", help="Free at console.groq.com")
-        if not api_key:
-            st.caption("Get free key → [console.groq.com](https://console.groq.com)")
-
+    api_key = env_key if env_key else st.text_input("🔑 Groq API Key", type="password", placeholder="gsk_...")
+    if not env_key and not api_key:
+        st.caption("Free key → [console.groq.com](https://console.groq.com)")
     st.markdown("---")
-    st.markdown("<div class='section-label'>Indexed Documents</div>", unsafe_allow_html=True)
-
-    if st.session_state.indexed_files:
-        for fname, info in st.session_state.indexed_files.items():
-            st.markdown(f"""
-<div style='padding:6px 0;border-bottom:1px solid #131c2e'>
-  <div style='font-size:0.8rem;color:#e2e8f0'>📄 {fname}</div>
-  <div style='font-size:0.72rem;color:#475569;font-family:IBM Plex Mono,monospace'>
-    {info["pages"]}p · {info["chunks"]} chunks · {info["size_kb"]}KB
-  </div>
+    st.markdown("""
+<div style='font-size:0.78rem;color:#2d2d3e;line-height:2'>
+<b style='color:#4b5563'>Extracts</b><br>
+🎭 Sentiment & Score<br>
+🏷️ Key Themes<br>
+👤 Named Entities<br>
+💡 Key Insights<br>
+🎯 Target Audience<br>
+🗣️ Content Tone<br>
+📰 Content Type
 </div>""", unsafe_allow_html=True)
-
-        st.markdown("---")
-        if st.button("🗑️ Clear all & reset", use_container_width=True):
-            for key in ["indexed_files", "chroma_collection", "chroma_client", "total_chunks"]:
-                del st.session_state[key]
-            st.rerun()
-    else:
-        st.markdown("<div style='color:#374151;font-size:0.82rem'>No documents indexed yet.</div>", unsafe_allow_html=True)
-
     st.markdown("---")
     st.markdown("""
-<div style='font-size:0.77rem;color:#374151;line-height:1.9'>
-<b style='color:#4b5563'>Stack</b><br>
-📄 PDF parsing: PyMuPDF<br>
-✂️ Chunking: word-overlap (400w)<br>
-🔢 Embeddings: all-MiniLM-L6-v2<br>
-🗄️ Vector DB: ChromaDB in-memory<br>
-🧠 LLM: Groq · Llama 3.3 70B<br>
-🌐 Hosting: HuggingFace Spaces
-</div>
-""", unsafe_allow_html=True)
+<div style='font-size:0.78rem;color:#2d2d3e;line-height:2'>
+<b style='color:#4b5563'>Try these URLs</b><br>
+• Any news article<br>
+• Amazon product page<br>
+• Wikipedia article<br>
+• Company blog post<br>
+• G2 / Trustpilot review
+</div>""", unsafe_allow_html=True)
 
 
-# ─── Hero ─────────────────────────────────────────────────────────────────────
+# ─── Main UI ──────────────────────────────────────────────────────────────────
 st.markdown("""
 <div class='hero'>
-  <h1>📂 PDF RAG — Upload & Ask</h1>
-  <p>Upload any PDF documents · They get extracted, chunked, embedded, and indexed · Then ask questions across all of them</p>
+  <h1>🌐 AI Webpage Sentiment & Insight Analyzer</h1>
+  <p>Paste any URL — AI extracts sentiment, themes, entities, tone, and key insights in seconds</p>
 </div>
 """, unsafe_allow_html=True)
 
-# Phase bar
-st.markdown(f"""
-<div class='phase-bar'>
-  <div class='phase {"done" if phase > 1 else "active"}'>
-    <span class='phase-icon'>📤</span>Upload PDFs
-  </div>
-  <div class='phase {"active" if phase == 1 else "done"}'>
-    <span class='phase-icon'>📑</span>Extract Text
-  </div>
-  <div class='phase {"active" if phase == 1 else "done"}'>
-    <span class='phase-icon'>✂️</span>Chunk
-  </div>
-  <div class='phase {"active" if phase == 1 else "done"}'>
-    <span class='phase-icon'>🔢</span>Embed
-  </div>
-  <div class='phase {"active" if phase == 1 else "done"}'>
-    <span class='phase-icon'>🗄️</span>Index
-  </div>
-  <div class='phase {"active" if phase == 2 else ""}'>
-    <span class='phase-icon'>💬</span>Ask Questions
+if not api_key:
+    st.warning("👈 Add your Groq API key in the sidebar.")
+    st.stop()
+
+st.markdown("<div class='section-label'>Paste a URL to analyze</div>", unsafe_allow_html=True)
+col_input, col_btn = st.columns([5, 1])
+with col_input:
+    url_input = st.text_input("URL", placeholder="https://...", label_visibility="collapsed")
+with col_btn:
+    analyze_btn = st.button("Analyze ➤", type="primary", use_container_width=True)
+
+# Example URLs
+st.markdown("<div class='section-label'>Quick examples</div>", unsafe_allow_html=True)
+examples = [
+    "https://en.wikipedia.org/wiki/Artificial_intelligence",
+    "https://techcrunch.com",
+    "https://www.bbc.com/news",
+]
+cols = st.columns(len(examples))
+clicked_url = None
+for i, ex in enumerate(examples):
+    parsed = urlparse(ex)
+    label = parsed.netloc
+    if cols[i].button(f"🔗 {label}", key=f"ex_{i}", use_container_width=True):
+        clicked_url = ex
+
+final_url = clicked_url or (url_input if analyze_btn else None)
+
+if final_url:
+    with st.spinner(f"Fetching and analyzing {final_url}..."):
+        try:
+            content_text, page_title = fetch_url_text(final_url)
+            result = analyze_content(content_text, final_url, page_title, api_key)
+
+            sentiment = result.get("sentiment", "Neutral")
+            score = result.get("sentiment_score", 0)
+            sentiment_color = (
+                "#4ade80" if sentiment == "Positive" else
+                "#f87171" if sentiment == "Negative" else
+                "#fbbf24" if sentiment == "Mixed" else
+                "#94a3b8"
+            )
+            sentiment_emoji = (
+                "😊" if sentiment == "Positive" else
+                "😟" if sentiment == "Negative" else
+                "😐" if sentiment == "Neutral" else "🤔"
+            )
+            score_pct = int((score + 1) / 2 * 100)
+
+            st.markdown(f"<div class='url-chip'>🔗 {final_url}</div>", unsafe_allow_html=True)
+            st.markdown(f"### 📄 {page_title}")
+
+            # Top row
+            col_sent, col_summary = st.columns([1, 2])
+
+            with col_sent:
+                st.markdown(f"""
+<div class='insight-card' style='text-align:center'>
+  <div style='font-size:0.72rem;text-transform:uppercase;letter-spacing:0.1em;color:#4b5563;margin-bottom:8px'>Sentiment</div>
+  <div style='font-size:3.5rem'>{sentiment_emoji}</div>
+  <div style='font-size:1.6rem;font-weight:700;color:{sentiment_color};margin:4px 0'>{sentiment}</div>
+  <div style='font-family:JetBrains Mono,monospace;font-size:0.8rem;color:#4b5563'>score: {score:+.2f}</div>
+  <div style='background:#1e1e2e;border-radius:4px;height:6px;margin:10px 0'>
+    <div style='height:6px;border-radius:4px;width:{score_pct}%;background:{sentiment_color}'></div>
   </div>
+  <div style='font-size:0.78rem;color:#4b5563;margin-top:8px'>{result.get("sentiment_explanation","")}</div>
 </div>
 """, unsafe_allow_html=True)
 
-# ─── Load model ───────────────────────────────────────────────────────────────
-with st.spinner("⚙️ Loading embedding model..."):
-    embed_model = load_embed_model()
-
-
-# ════════════════════════════════════════════════════════════
-# PHASE 1 — Upload & Index
-# ════════════════════════════════════════════════════════════
-st.markdown("<div class='section-label'>Step 1 — Upload PDF Documents</div>", unsafe_allow_html=True)
-
-uploaded_files = st.file_uploader(
-    "Drop your PDF files here",
-    type=["pdf"],
-    accept_multiple_files=True,
-    label_visibility="collapsed"
-)
-
-if uploaded_files:
-    new_files = [f for f in uploaded_files if f.name not in st.session_state.indexed_files]
-
-    if new_files:
-        st.markdown(f"**{len(new_files)} new file(s) ready to index:**")
-        for f in new_files:
-            st.markdown(f"<div class='pdf-card'><div><div class='pdf-name'>📄 {f.name}</div><div class='pdf-meta'>{round(f.size/1024,1)} KB</div></div><div class='pdf-badge'>ready</div></div>", unsafe_allow_html=True)
-
-        if st.button(f"⚡ Extract & Index {len(new_files)} PDF(s)", type="primary", use_container_width=True):
-            progress = st.progress(0, text="Starting...")
-            for idx, f in enumerate(new_files):
-                progress.progress((idx) / len(new_files), text=f"Processing: {f.name}")
-                pdf_bytes = f.read()
-
-                with st.spinner(f"Extracting & indexing **{f.name}**..."):
-                    n_chunks, n_pages = index_pdf(f.name, pdf_bytes, embed_model)
-
-                st.success(f"✅ **{f.name}** → {n_pages} pages · {n_chunks} chunks indexed")
-
-            progress.progress(1.0, text="Done!")
-            st.balloons()
-            st.rerun()
-
-    else:
-        st.info("All uploaded files are already indexed. Upload new files or ask questions below.")
-
-elif not has_docs:
-    st.markdown("""
-<div class='empty-state'>
-  <div class='icon'>📂</div>
-  <p><b style='color:#94a3b8'>No documents uploaded yet</b><br>
-  Upload one or more PDF files above to get started.<br>
-  Any topic works — reports, manuals, research papers, policies.</p>
+            with col_summary:
+                entities = result.get("named_entities", {})
+                persons_html   = "".join([f"<span class='entity-tag entity-person'>👤 {e}</span>"   for e in entities.get("persons", [])[:4]])
+                orgs_html      = "".join([f"<span class='entity-tag entity-org'>🏢 {e}</span>"      for e in entities.get("organizations", [])[:4]])
+                locations_html = "".join([f"<span class='entity-tag entity-location'>📍 {e}</span>" for e in entities.get("locations", [])[:3]])
+                products_html  = "".join([f"<span class='entity-tag entity-product'>📦 {e}</span>"  for e in entities.get("products", [])[:3]])
+                entities_html  = persons_html + orgs_html + locations_html + products_html or "<span style='color:#4b5563;font-size:0.82rem'>None detected</span>"
+
+                st.markdown(f"""
+<div class='insight-card'>
+  <div style='font-size:0.72rem;text-transform:uppercase;letter-spacing:0.1em;color:#4b5563;margin-bottom:10px'>One-Line Summary</div>
+  <div style='font-size:1rem;color:#f1f5f9;font-weight:500;line-height:1.6;margin-bottom:16px'>"{result.get("one_line_summary","")}"</div>
+  <div style='display:flex;gap:16px;margin-bottom:14px'>
+    <div><span style='font-size:0.72rem;color:#4b5563'>Content Type</span><br><span style='color:#a78bfa;font-weight:600;font-size:0.88rem'>{result.get("content_type","")}</span></div>
+    <div><span style='font-size:0.72rem;color:#4b5563'>Tone</span><br><span style='color:#60a5fa;font-weight:600;font-size:0.88rem'>{result.get("tone","")}</span></div>
+    <div><span style='font-size:0.72rem;color:#4b5563'>Audience</span><br><span style='color:#4ade80;font-weight:600;font-size:0.88rem'>{result.get("target_audience","")}</span></div>
+  </div>
+  <div style='font-size:0.72rem;text-transform:uppercase;letter-spacing:0.1em;color:#4b5563;margin-bottom:8px'>Named Entities</div>
+  {entities_html}
 </div>
 """, unsafe_allow_html=True)
 
+            # Themes + Insights
+            col_themes, col_insights = st.columns(2)
 
-# ════════════════════════════════════════════════════════════
-# PHASE 2 — Stats & Query
-# ════════════════════════════════════════════════════════════
-if has_docs:
-    total_pages = sum(v["pages"] for v in st.session_state.indexed_files.values())
-
-    st.markdown("<div class='section-label' style='margin-top:24px'>Index Summary</div>", unsafe_allow_html=True)
-    st.markdown(f"""
-<div class='stat-row'>
-  <div class='stat-box'><div class='stat-val'>{len(st.session_state.indexed_files)}</div><div class='stat-lbl'>Documents</div></div>
-  <div class='stat-box'><div class='stat-val'>{total_pages}</div><div class='stat-lbl'>Pages Parsed</div></div>
-  <div class='stat-box'><div class='stat-val'>{st.session_state.total_chunks}</div><div class='stat-lbl'>Chunks Indexed</div></div>
-  <div class='stat-box'><div class='stat-val'>384</div><div class='stat-lbl'>Embedding Dims</div></div>
-</div>
-""", unsafe_allow_html=True)
-
-    if not api_key:
-        st.warning("👈 Enter your Groq API key in the sidebar to start asking questions.")
-        st.stop()
+            with col_themes:
+                themes_html = "".join([f"<div class='theme-pill'>#{t}</div>" for t in result.get("key_themes", [])])
+                st.markdown(f"""
+<div class='insight-card'>
+  <div style='font-size:0.72rem;text-transform:uppercase;letter-spacing:0.1em;color:#4b5563;margin-bottom:12px'>🏷️ Key Themes</div>
+  {themes_html}
+</div>""", unsafe_allow_html=True)
 
-    st.markdown("---")
-    st.markdown("<div class='section-label'>Step 2 — Ask a Question</div>", unsafe_allow_html=True)
-
-    col1, col2 = st.columns([5, 1])
-    with col1:
-        question = st.text_input("", placeholder="What does the document say about...?", label_visibility="collapsed")
-    with col2:
-        top_k = st.selectbox("Top K", [2, 3, 4, 5], index=1, help="Number of chunks to retrieve")
-
-    ask_btn = st.button("🔍 Search & Answer", type="primary", use_container_width=True)
-
-    if ask_btn and question:
-        with st.spinner("🔍 Searching index and generating answer..."):
-            try:
-                answer, chunks = rag_query(question, embed_model, top_k, api_key)
-
-                st.markdown(f"<div class='section-label'>Answer</div>", unsafe_allow_html=True)
-                st.markdown(f"<div class='answer-box'>{answer}</div>", unsafe_allow_html=True)
-
-                st.markdown("<div class='section-label'>Retrieved Chunks (context sent to LLM)</div>", unsafe_allow_html=True)
-
-                for i, chunk in enumerate(chunks):
-                    bar_width = int(chunk['relevance'])
-                    st.markdown(f"""
-<div class='chunk-card'>
-  <div class='chunk-top'>
-    <div>
-      <div class='chunk-source'>📄 {chunk['filename']}</div>
-      <div class='chunk-page'>Page {chunk['page']}</div>
-    </div>
-    <div class='score-bar-wrap'>
-      <div class='score-bar'><div class='score-fill' style='width:{bar_width}%'></div></div>
-      <div class='score-num'>{chunk['relevance']}%</div>
-    </div>
-  </div>
-  <div class='chunk-text'>{chunk['text']}</div>
-</div>
-""", unsafe_allow_html=True)
+            with col_insights:
+                insights_html = "".join([f"<div style='padding:8px 0;border-bottom:1px solid #1e1e2e;font-size:0.87rem;color:#94a3b8;line-height:1.6'>→ {ins}</div>" for ins in result.get("key_insights", [])])
+                st.markdown(f"""
+<div class='insight-card'>
+  <div style='font-size:0.72rem;text-transform:uppercase;letter-spacing:0.1em;color:#4b5563;margin-bottom:12px'>💡 Key Insights</div>
+  {insights_html}
+</div>""", unsafe_allow_html=True)
 
-            except requests.HTTPError as e:
-                if e.response.status_code == 401:
-                    st.error("❌ Invalid Groq API key.")
-                else:
-                    st.error(f"❌ API error: {str(e)}")
-            except Exception as e:
-                st.error(f"❌ Error: {str(e)}")
+            # Credibility
+            cred = result.get("credibility_signals", [])
+            if cred:
+                cred_html = "".join([f"<span style='background:rgba(74,222,128,0.08);border:1px solid rgba(74,222,128,0.2);border-radius:6px;padding:4px 12px;margin:3px;display:inline-block;font-size:0.8rem;color:#4ade80'>✓ {c}</span>" for c in cred])
+                st.markdown(f"""
+<div class='insight-card'>
+  <div style='font-size:0.72rem;text-transform:uppercase;letter-spacing:0.1em;color:#4b5563;margin-bottom:10px'>🛡️ Credibility Signals</div>
+  {cred_html}
+</div>""", unsafe_allow_html=True)
 
-    elif ask_btn and not question:
-        st.warning("Please enter a question.")
+        except requests.exceptions.ConnectionError:
+            st.error("❌ Could not reach that URL. Make sure it's publicly accessible.")
+        except json.JSONDecodeError:
+            st.error("❌ AI returned unexpected output. Try again.")
+        except Exception as e:
+            st.error(f"❌ Error: {str(e)}")