Fix forecast chart continuity and hover

utils.py

@@ -84,10 +84,14 @@ def build_forecast(values, dates, horizon_weeks=8, metric='E1RM'):
     horizon_days = horizon_weeks * 7
     future_x = np.arange(0, horizon_days + 1, 7, dtype=float) + x[-1]
     future_dates = [last_date + timedelta(days=int(day - x[-1])) for day in future_x]
-    future_values = intercept + slope * future_x
+    # Anchor the projection to the most recent observed value.  A pure
+    # regression line can sit noticeably above or below the latest workout,
+    # which makes the forecast appear to jump when it starts.
+    future_values = observed_values[-1] + slope * (future_x - x[-1])
 
-    # A 95% prediction band, widened for points further from the observations.
-    prediction_error = 1.96 * residual_std * np.sqrt(
+    # A restrained 90% prediction band, widened for points further from the
+    # observations so the uncertainty remains readable on the small chart.
+    prediction_error = 1.645 * residual_std * np.sqrt(
         1 + (1 / len(observed_values)) + ((future_x - mean_x) ** 2 / sum_squared_x)
     )
     lower_values = np.maximum(future_values - prediction_error, 0)
@@ -212,6 +216,7 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
     forecast_points = []
     forecast_band_lower = []
     forecast_band_upper = []
+    forecast_labels = []
     forecast_start_position = None
     if forecast.get('available'):
         forecast_start_position = round((observed_max_date - observed_min_date).days / total_span, 3)
@@ -220,6 +225,11 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
             forecast_points.append((round(((point['value'] - min_e1rm) / e1rm_range) * vb_height, 1), position))
             forecast_band_lower.append((round(((point['lower'] - min_e1rm) / e1rm_range) * vb_height, 1), position))
             forecast_band_upper.append((round(((point['upper'] - min_e1rm) / e1rm_range) * vb_height, 1), position))
+            forecast_labels.append((
+                position,
+                f"{point['date'].strftime('%d %b %y')} · Projected E1RM: {point['value']}kg "
+                f"(range {point['lower']}–{point['upper']}kg)"
+            ))
 
     # Return exercise data with SVG dimensions and data points
     return {
@@ -243,6 +253,7 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
         'forecast_points': forecast_points,
         'forecast_band_lower': forecast_band_lower,
         'forecast_band_upper': forecast_band_upper,
+        'forecast_labels': forecast_labels,
         'forecast_start_position': forecast_start_position
     }