Added safety checks to the graph regression logic in utils.py. This stops those "illegal value" server warnings and makes the math more efficient for small datasets

utils.py

@@ -48,18 +48,22 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
 
     best_fit_points = []
     try:
-        if len(relative_positions) > 1:  # Ensure there are enough points for polyfit
-            # Fit a polynomial of the given degree
-            coeffs = np.polyfit(relative_positions, estimated_1rm_scaled, degree)
+        # Filter out NaNs if any (though scaled values shouldn't have them if ranges are correct)
+        mask = ~np.isnan(estimated_1rm_scaled)
+        x_fit = relative_positions[mask]
+        y_fit = estimated_1rm_scaled[mask]
+        
+        # Ensure we have enough unique X positions for the given degree
+        if len(np.unique(x_fit)) > degree: 
+            coeffs = np.polyfit(x_fit, y_fit, degree)
             poly_fit = np.poly1d(coeffs)
             y_best_fit = poly_fit(relative_positions)
             best_fit_points = list(zip(y_best_fit.tolist(), relative_positions.tolist()))
         else:
-            raise ValueError("Not enough data points for polyfit")
-    except (np.linalg.LinAlgError, ValueError) as e:
-        # Handle cases where polyfit fails
+            best_fit_points = []
+    except (np.linalg.LinAlgError, ValueError, TypeError) as e:
+        # Handle cases where polyfit fails or input is invalid
         best_fit_points = []
-        m, b = 0, 0
 
     # Prepare data for plots
     repetitions_data = {