feat: upgrade NLP engine with deep search, fallbacks, and neutral signal detection

api/main.py

@@ -119,6 +119,7 @@ async def scout_player(player: str, club: str = "", interested_club: str = ""):
         "agent": nlp["agent"],
         "logs": nlp.get("_logs", []),
         "from_cache": nlp.get("_from_cache", False),
+        "nlp_found": nlp.get("_found_any", False)
     }
 
 
@@ -189,20 +190,32 @@ def _fetch_nlp_intelligence(
     scores = {'durability': 0.0, 'recency': 0.0, 'agent': 0.0}
     logs = []
 
+    found_any = False
     for axis, query in axes.items():
         try:
-            snippets = list(ddgs.text(query.strip(), max_results=3))
-            sentiments = [
-                TextBlob(r.get('body', '') + ' ' + r.get('title', '')).sentiment.polarity
-                for r in snippets
-            ]
-            avg_pol = sum(sentiments) / len(sentiments) if sentiments else 0.0
-            scores[axis] = float(avg_pol)
-            logs.append(f"Scraped {axis}: Polarity {avg_pol:.2f}")
+            # Increase results to 10 for better sentiment spread
+            snippets = list(ddgs.text(query.strip(), max_results=10))
+            
+            # Fallback: if no results, try a broader search without the clubs
+            if not snippets:
+                fallback_query = f"{player_name} {axis} news"
+                snippets = list(ddgs.text(fallback_query, max_results=5))
+            
+            if snippets:
+                found_any = True
+                sentiments = [
+                    TextBlob(r.get('body', '') + ' ' + r.get('title', '')).sentiment.polarity
+                    for r in snippets
+                ]
+                avg_pol = sum(sentiments) / len(sentiments) if sentiments else 0.0
+                scores[axis] = float(avg_pol)
+                logs.append(f"Scraped {axis}: Polarity {avg_pol:.2f} ({len(snippets)} results)")
+            else:
+                logs.append(f"No results for {axis} (Primary & Fallback)")
         except Exception as e:
             logs.append(f"Failed {axis}: {str(e)}")
 
-    result = {**scores, '_ts': time.time(), '_logs': logs, '_from_cache': False}
+    result = {**scores, '_ts': time.time(), '_logs': logs, '_from_cache': False, '_found_any': found_any}
     _nlp_cache[cache_key] = result
     return result
 
@@ -332,6 +345,7 @@ async def evaluate_player(req: PlayerEvaluateRequest):
         },
         "nlp_results": {"durability": dur, "recency": rec, "agent": agnt},
         "nlp_cached": nlp.get('_from_cache', False),
+        "nlp_found": nlp.get('_found_any', False),
         "logs": logs,
         "shap_data": shap_data,
     }

fairvalue-webapp/src/components/ReportTemplate.tsx

@@ -25,6 +25,7 @@ export interface ReportProps {
       recency: number
       agent: number
     }
+    nlp_found?: boolean
   }
 }
 
@@ -164,20 +165,20 @@ export const ReportTemplate = forwardRef<HTMLDivElement, ReportProps>(({ form, r
           <div style={{ display: 'grid', gridTemplateColumns: '1fr 1fr 1fr', gap: '16px', background: '#f8fafc', padding: '24px', borderRadius: '12px', border: '1px solid #e2e8f0' }}>
             <div>
               <div style={{ fontSize: '11px', color: '#64748b', textTransform: 'uppercase' }}>Recent Form &amp; Impact</div>
-              <div style={{ fontSize: '18px', fontWeight: 700, color: (result.nlp_results?.recency ?? 0) === 0 ? '#94a3b8' : (result.nlp_results?.recency ?? 0) < 0 ? '#ef4444' : '#22c55e' }}>
-                {(result.nlp_results?.recency ?? 0) === 0 ? 'No signals detected' : `${(result.nlp_results?.recency ?? 0) > 0 ? '+' : ''}${(result.nlp_results?.recency ?? 0).toFixed(2)}`}
+              <div style={{ fontSize: '18px', fontWeight: 700, color: (result.nlp_results?.recency ?? 0) === 0 ? (result.nlp_found ? '#475569' : '#94a3b8') : (result.nlp_results?.recency ?? 0) < 0 ? '#ef4444' : '#22c55e' }}>
+                {(result.nlp_results?.recency ?? 0) === 0 ? (result.nlp_found ? 'Neutral / Factual' : 'No signals detected') : `${(result.nlp_results?.recency ?? 0) > 0 ? '+' : ''}${(result.nlp_results?.recency ?? 0).toFixed(2)}`}
               </div>
             </div>
             <div>
               <div style={{ fontSize: '11px', color: '#64748b', textTransform: 'uppercase' }}>Injury / Availability</div>
-              <div style={{ fontSize: '18px', fontWeight: 700, color: (result.nlp_results?.durability ?? 0) === 0 ? '#94a3b8' : (result.nlp_results?.durability ?? 0) < 0 ? '#ef4444' : '#0f172a' }}>
-                {(result.nlp_results?.durability ?? 0) === 0 ? 'No signals detected' : `${(result.nlp_results?.durability ?? 0) > 0 ? '+' : ''}${(result.nlp_results?.durability ?? 0).toFixed(2)}`}
+              <div style={{ fontSize: '18px', fontWeight: 700, color: (result.nlp_results?.durability ?? 0) === 0 ? (result.nlp_found ? '#475569' : '#94a3b8') : (result.nlp_results?.durability ?? 0) < 0 ? '#ef4444' : '#0f172a' }}>
+                {(result.nlp_results?.durability ?? 0) === 0 ? (result.nlp_found ? 'Neutral / Factual' : 'No signals detected') : `${(result.nlp_results?.durability ?? 0) > 0 ? '+' : ''}${(result.nlp_results?.durability ?? 0).toFixed(2)}`}
               </div>
             </div>
             <div>
               <div style={{ fontSize: '11px', color: '#64748b', textTransform: 'uppercase' }}>Transfer Speculation</div>
-              <div style={{ fontSize: '18px', fontWeight: 700, color: (result.nlp_results?.agent ?? 0) === 0 ? '#94a3b8' : (result.nlp_results?.agent ?? 0) < 0 ? '#ef4444' : '#22c55e' }}>
-                {(result.nlp_results?.agent ?? 0) === 0 ? 'No signals detected' : `${(result.nlp_results?.agent ?? 0) > 0 ? '+' : ''}${(result.nlp_results?.agent ?? 0).toFixed(2)}`}
+              <div style={{ fontSize: '18px', fontWeight: 700, color: (result.nlp_results?.agent ?? 0) === 0 ? (result.nlp_found ? '#475569' : '#94a3b8') : (result.nlp_results?.agent ?? 0) < 0 ? '#ef4444' : '#22c55e' }}>
+                {(result.nlp_results?.agent ?? 0) === 0 ? (result.nlp_found ? 'Neutral / Factual' : 'No signals detected') : `${(result.nlp_results?.agent ?? 0) > 0 ? '+' : ''}${(result.nlp_results?.agent ?? 0).toFixed(2)}`}
               </div>
             </div>
           </div>

fairvalue-webapp/src/pages/Estimator.tsx

@@ -25,6 +25,7 @@ interface EvalResult {
   ledger: Ledger
   nlp_results: { durability: number; recency: number; agent: number }
   nlp_cached: boolean
+  nlp_found?: boolean
   logs: string[]
   shap_data: { feature: string; impact: number }[]
 }
@@ -125,16 +126,20 @@ function ShapChart({ data }: { data: { feature: string; impact: number }[] }) {
 }
 
 // ── NLP Sentiment Score ───────────────────────────────────────────────────────
-function SentimentScore({ label, value, icon }: { label: string; value: number; icon: string }) {
+function SentimentScore({ label, value, icon, found }: { label: string; value: number; icon: string, found?: boolean }) {
   const pct = Math.round(((value + 1) / 2) * 100)
-  const color = value > 0.1 ? 'var(--profit-color)' : value < -0.1 ? 'var(--loss-color)' : 'var(--accent-blue)'
+  const isNeutral = value === 0
+  const color = value > 0.1 ? 'var(--profit-color)' : value < -0.1 ? 'var(--loss-color)' : (isNeutral && found ? 'var(--text-3)' : 'var(--accent-blue)')
+  
   return (
     <div>
       <div style={{ display:'flex', justifyContent:'space-between', marginBottom:6 }}>
         <span style={{ fontSize:'0.8rem', color:'var(--text-2)', display:'flex', gap:6, alignItems:'center' }}>
           {icon} {label}
         </span>
-        <span style={{ fontSize:'0.8rem', fontWeight:700, color }}>{value > 0 ? '+' : ''}{value.toFixed(2)}</span>
+        <span style={{ fontSize:'0.8rem', fontWeight:700, color }}>
+          {isNeutral ? (found ? 'Neutral' : 'No Data') : (value > 0 ? '+' : '') + value.toFixed(2)}
+        </span>
       </div>
       <div className="sentiment-bar-track">
         <div className="sentiment-bar-fill" style={{ width:`${pct}%`, background:color }}/>
@@ -488,9 +493,9 @@ export default function Estimator() {
                     {result.nlp_cached && <span className="badge badge-blue">Cached</span>}
                   </div>
                   <div style={{ display:'flex', flexDirection:'column', gap:14 }}>
-                    <SentimentScore label="Injury / Availability"    icon="🏥" value={result.nlp_results.durability}/>
-                    <SentimentScore label="Recent Form & Impact"     icon="📈" value={result.nlp_results.recency}/>
-                    <SentimentScore label="Transfer Speculation"     icon="🗞️" value={result.nlp_results.agent}/>
+                    <SentimentScore label="Injury / Availability"    icon="🏥" value={result.nlp_results.durability} found={result.nlp_found}/>
+                    <SentimentScore label="Recent Form & Impact"     icon="📈" value={result.nlp_results.recency} found={result.nlp_found}/>
+                    <SentimentScore label="Transfer Speculation"     icon="🗞️" value={result.nlp_results.agent} found={result.nlp_found}/>
                   </div>
                   <div style={{ marginTop:16 }}>
                     <button className="btn btn-ghost" style={{ fontSize:'0.78rem', padding:'6px 14px' }}

fairvalue-webapp/src/pages/Intel.tsx

@@ -12,11 +12,12 @@ interface ScoutResult {
   agent: number
   logs: string[]
   from_cache: boolean
+  nlp_found?: boolean
 }
 
 function ScoreCard({
-  label, value, icon, desc,
-}: { label: string; value: number; icon: string; desc: string }) {
+  label, value, icon, desc, found
+}: { label: string; value: number; icon: string; desc: string; found?: boolean }) {
   const norm  = (value + 1) / 2             // –1..+1 → 0..1
   const pct   = Math.round(norm * 100)
   const color = value > 0.1 ? 'var(--profit-color)' : value < -0.1 ? 'var(--loss-color)' : 'var(--accent-blue)'
@@ -33,9 +34,11 @@ function ScoreCard({
         </div>
         <div style={{ textAlign:'right' }}>
           <div style={{ fontSize:'2rem', fontWeight:900, color, letterSpacing:'-0.03em', lineHeight:1 }}>
-            {value > 0 ? '+' : ''}{value.toFixed(2)}
+            {value === 0 ? (found ? '0.00' : 'N/A') : (value > 0 ? '+' : '') + value.toFixed(2)}
           </div>
-          <span className={`badge ${badge}`} style={{ marginTop:6 }}>{label2}</span>
+          <span className={`badge ${badge}`} style={{ marginTop:6 }}>
+            {value === 0 ? (found ? 'Neutral' : 'No Data') : label2}
+          </span>
         </div>
       </div>
       {/* Bar */}
@@ -198,16 +201,19 @@ export default function Intel() {
               <ScoreCard
                 icon="🏥" label="Durability"
                 value={result.durability}
+                found={result.nlp_found}
                 desc="Based on injury reports, games missed, and fitness news. Negative = active concerns."
               />
               <ScoreCard
                 icon="📈" label="Recent Form"
                 value={result.recency}
+                found={result.nlp_found}
                 desc="Based on match ratings, goal/assist data, and performance commentary."
               />
               <ScoreCard
                 icon="🗞️" label="Transfer Heat"
                 value={result.agent}
+                found={result.nlp_found}
                 desc="Based on rumour intensity, agent activity, and bid speculation volume."
               />
             </div>