feat: introduce PolyWeather application with an interactive map, detailed city weather, trend analysis, and a supporting API.

web/app.py

@@ -284,7 +284,7 @@ def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]:
     # ── 10. Shared analysis (probability, trend, AI) via trend_engine ──
     # This single call replaces the duplicate probability engine, dead market
     # detection, forecast bust grading, and AI context building.
-    from src.analysis.trend_engine import analyze_weather_trend as _trend_analyze
+    from src.analysis.trend_engine import analyze_weather_trend as _trend_analyze, calculate_prob_distribution
     from src.analysis.ai_analyzer import get_ai_analysis
 
     probabilities = []
@@ -434,19 +434,33 @@ def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]:
                 day_m["MGM"] = _sf(mgm_daily[d_str])
             
             d_val, d_winfo = None, ""
+            d_probs = []
             if day_m:
                 try:
                     blended, winfo = calculate_dynamic_weights(city, day_m)
                     if blended is not None:
                         d_val = blended
                         d_winfo = winfo
+                        
+                        # Calculate future probability based on model divergence
+                        m_vals = [v for v in day_m.values() if v is not None]
+                        if len(m_vals) > 1:
+                            # Use spread as a proxy for sigma. 
+                            # sigma = (max-min)/2 with a floor of 0.6
+                            d_sigma = max(0.6, (max(m_vals) - min(m_vals)) / 2.0)
+                        else:
+                            d_sigma = 1.0
+                        
+                        prob_obj = calculate_prob_distribution(d_val, d_sigma, None, sym)
+                        d_probs = prob_obj.get("probabilities", [])
                 except Exception:
                     pass
         
         if day_m:
             multi_model_daily[d_str] = {
                 "models": day_m,
-                "deb": {"prediction": d_val, "weights_info": d_winfo}
+                "deb": {"prediction": d_val, "weights_info": d_winfo},
+                "probabilities": d_probs if i > 0 else probabilities # Use today's real prob for today
             }
 
     # ── Assemble result ──

web/static/app.js

@@ -818,8 +818,18 @@ function renderChart(data) {
 
 function renderProbabilities(data) {
   const container = document.getElementById("probBars");
-  const probs = data.probabilities?.distribution || [];
-  const mu = data.probabilities?.mu;
+  const targetDate = selectedForecastDate || data.local_date;
+
+  let probs = [];
+  let mu = null;
+
+  if (targetDate === data.local_date) {
+    probs = data.probabilities?.distribution || [];
+    mu = data.probabilities?.mu;
+  } else if (data.multi_model_daily && data.multi_model_daily[targetDate]) {
+    probs = data.multi_model_daily[targetDate].probabilities || [];
+    mu = data.multi_model_daily[targetDate].deb?.prediction;
+  }
 
   if (probs.length === 0) {
     container.innerHTML =
@@ -834,7 +844,6 @@ function renderProbabilities(data) {
 
   probs.forEach((p, i) => {
     const pct = Math.round(p.probability * 100);
-    const width = Math.max(pct, 8);
     html += `
             <div class="prob-row">
                 <div class="prob-label">${p.value}${data.temp_symbol}</div>
@@ -962,6 +971,7 @@ function switchForecastDate(cityName, dateStr) {
   const data = cityDataCache[cityName];
   if (data) {
     renderModels(data);
+    renderProbabilities(data);
     renderForecast(data);
   }
 }