diff --git a/db.py b/db.py
index acbbf8d..4227c40 100644
--- a/db.py
+++ b/db.py
@@ -7,7 +7,7 @@ from urllib.parse import urlparse
 from flask import g
 
 
-from utils import get_all_exercises_from_topsets, get_stats_from_topsets, get_workouts
+from utils import get_all_exercises_from_topsets, get_exercise_graph_model, get_stats_from_topsets, get_workouts
 
 
 class DataBase():
@@ -476,69 +476,8 @@ class DataBase():
         repetitions = [t['repetitions'] for t in topsets]
         weight = [t['weight'] for t in topsets]
         start_dates = [t['start_date'] for t in topsets]
-        min_date, max_date = min(start_dates), max(start_dates)
-        min_e1rm, max_e1rm = min(estimated_1rm), max(estimated_1rm)
-        min_reps, max_reps = min(repetitions), max(repetitions)
-        min_weight, max_weight = min(weight), max(weight)
-        
-        # Calculate viewBox dimensions
-        date_range = max_date - min_date
-        total_span = date_range.days or 1
-        e1rm_range = (max_e1rm - min_e1rm) or 1
-        reps_range = (max_reps - min_reps) or 1
-        weight_range = (max_weight - min_weight) or 1
-        vb_width, vb_height = total_span, e1rm_range
-        vb_width *= 200 / vb_width  # Scale to 200px width
-        vb_height *= 75 / vb_height  # Scale to 75px height
-
-        # Scale estimated_1rm values for SVG plotting
-        estimated_1rm_scaled = [((value - min_e1rm) / e1rm_range) * vb_height for value in estimated_1rm]
-        repetitions_scaled = [((value - min_reps) / reps_range) * vb_height for value in repetitions]
-        weight_scaled = [((value - min_weight) / weight_range) * vb_height for value in weight]
-        
-        relative_positions = [(date - min_date).days / total_span for date in start_dates]
-
-        # Convert relative positions and scaled estimated 1RM values to numpy arrays
-        x = np.array(relative_positions)
-        y = np.array(estimated_1rm_scaled)
-
-        # Calculate the slope (m) and y-intercept (b) of the line of best fit
-        m, b = np.polyfit(x, y, 1)
-
-        # Generate points along the line of best fit
-        y_best_fit = [m * xi + b for xi in x]
-        best_fit_points = zip(y_best_fit, relative_positions)
-
-        # Create messages and zip data for SVG plotting
         messages = [f'{t["repetitions"]} x {t["weight"]}kg ({t["estimated_1rm"]}kg E1RM) on {t["start_date"].strftime("%d %b %y")}' for t in topsets]
-        estimated_1rm_points = zip(estimated_1rm_scaled, relative_positions, messages)
-        repetitions_points = zip(repetitions_scaled, relative_positions, messages)
-        weight_points = zip(weight_scaled, relative_positions, messages)
 
-        repetitions = {
-            'label': 'Reps',
-            'color': '#388fed',
-            'points': list(repetitions_points)
-        }
-        weight = {
-            'label': 'Weight',
-            'color': '#bd3178',
-            'points': list(weight_points)
-        }
-        estimated_1rm = {
-            'label': 'E1RM',
-            'color': '#2ca02c',
-            'points': list(estimated_1rm_points)
-        }
+        exercise_progress = get_exercise_graph_model(topsets[0]['exercise_name'], estimated_1rm, repetitions, weight, start_dates, messages)
 
-        plot_labels = zip(relative_positions, messages)
-
-        # Return exercise data with SVG dimensions and data points
-        return {
-            'title': topsets[0]['exercise_name'],
-            'vb_width': vb_width,
-            'vb_height': vb_height,
-            'plots': [repetitions, weight, estimated_1rm],
-            'best_fit_points': list(best_fit_points),
-            'plot_labels': plot_labels
-        }
\ No newline at end of file
+        return exercise_progress
\ No newline at end of file
diff --git a/templates/dashboard.html b/templates/dashboard.html
index fc34bdf..0c2f0b0 100644
--- a/templates/dashboard.html
+++ b/templates/dashboard.html
@@ -133,10 +133,7 @@
                 <div class="align-middle inline-block min-w-full">
                     <div class="shadow overflow-hidden sm:rounded-lg">
                         <div class="w-full mt-2 pb-2 aspect-video">
-                            <div class="hidden"
-                                hx-get="{{ url_for('get_exercise_progress_for_user', person_id=p['PersonId'], exercise_id=e['ExerciseId'], min_date=min_date, max_date=max_date) }}"
-                                hx-trigger="load" hx-target="this" hx-swap="outerHTML">
-                            </div>
+                            {{ render_partial('partials/sparkline.html', **e['ExerciseProgressGraph']) }}
                         </div>
 
                         <table class="min-w-full divide-y divide-gray-200">
diff --git a/templates/partials/sparkline.html b/templates/partials/sparkline.html
index eb3fe50..f07a61a 100644
--- a/templates/partials/sparkline.html
+++ b/templates/partials/sparkline.html
@@ -2,7 +2,7 @@
 {% set margin = 2 %}
 
 {% macro path(data_points, vb_height) %}
-    {% for value, position, message in data_points %}
+    {% for value, position in data_points %}
         {% set x = (position * vb_width)+margin %}
         {% set y = (vb_height - value)+margin %}
         {% if loop.first %}M{{ x | int }} {{ y | int }}{% else %} L{{ x | int }} {{ y | int }}{% endif %}
@@ -18,7 +18,7 @@
 {% endmacro %}
 
 {% macro circles(data_points, color) %}
-    {% for value, position, message in data_points %}
+    {% for value, position in data_points %}
         {% set x = (position * vb_width)+margin %}
         {% set y = (vb_height - value)+margin %}
         <circle cx="{{ x | int }}" cy="{{ y | int }}" r="1"></circle>
diff --git a/utils.py b/utils.py
index 1f37da7..a2cfd0b 100644
--- a/utils.py
+++ b/utils.py
@@ -1,4 +1,5 @@
 from datetime import datetime, date, timedelta
+import numpy as np
 import json
 
 
@@ -44,10 +45,20 @@ def get_topsets_for_person(person_topsets):
         # Sort topsets by StartDate in descending order
         sorted_topsets = sorted(exercise_topsets, key=lambda x: x['StartDate'], reverse=True)
 
+        # Extracting values and calculating value ranges for SVG dimensions
+        estimated_1rm = [t['Estimated1RM'] for t in exercise_topsets]
+        repetitions = [t['Repetitions'] for t in exercise_topsets]
+        weight = [t['Weight'] for t in exercise_topsets]
+        start_dates = [t['StartDate'] for t in exercise_topsets]
+        messages = [f'{t["Repetitions"]} x {t["Weight"]}kg ({t["Estimated1RM"]}kg E1RM) on {t["StartDate"].strftime("%d %b %y")}' for t in exercise_topsets]
+
+        exercise_progress = get_exercise_graph_model(exercise_topsets[0]['ExerciseName'], estimated_1rm, repetitions, weight, start_dates, messages)
+
         exercises_topsets.append({
             'ExerciseId': e['ExerciseId'],
             'ExerciseName': e['ExerciseName'],
-            'Topsets': sorted_topsets
+            'Topsets': sorted_topsets,
+            'ExerciseProgressGraph': exercise_progress
         })
 
     return exercises_topsets
@@ -223,3 +234,75 @@ def get_date_info(input_date, selected_view):
             'start_date': first_day_of_year,
             'end_date': last_day_of_year,
         }
+
+def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_dates, messages):
+    min_date, max_date = min(start_dates), max(start_dates)
+    min_e1rm, max_e1rm = min(estimated_1rm), max(estimated_1rm)
+    min_reps, max_reps = min(repetitions), max(repetitions)
+    min_weight, max_weight = min(weight), max(weight)
+    
+    # Calculate viewBox dimensions
+    date_range = max_date - min_date
+    total_span = date_range.days or 1
+    e1rm_range = (max_e1rm - min_e1rm) or 1
+    reps_range = (max_reps - min_reps) or 1
+    weight_range = (max_weight - min_weight) or 1
+    vb_width, vb_height = total_span, e1rm_range
+    vb_width *= 200 / vb_width  # Scale to 200px width
+    vb_height *= 75 / vb_height  # Scale to 75px height
+
+    # Scale estimated_1rm values for SVG plotting
+    estimated_1rm_scaled = [((value - min_e1rm) / e1rm_range) * vb_height for value in estimated_1rm]
+    repetitions_scaled = [((value - min_reps) / reps_range) * vb_height for value in repetitions]
+    weight_scaled = [((value - min_weight) / weight_range) * vb_height for value in weight]
+    
+    relative_positions = [(date - min_date).days / total_span for date in start_dates]
+
+    best_fit_points = []
+    # trry catch LinAlgError
+    try:
+        # Convert relative positions and scaled estimated 1RM values to numpy arrays
+        x = np.array(relative_positions)
+        y = np.array(estimated_1rm_scaled)
+
+        # Calculate the slope (m) and y-intercept (b) of the line of best fit
+        m, b = np.polyfit(x, y, 1)
+
+        # Generate points along the line of best fit
+        y_best_fit = [m * xi + b for xi in x]
+        best_fit_points = list(zip(y_best_fit, relative_positions))
+    except np.linalg.LinAlgError:
+        pass
+
+    # Create messages and zip data for SVG plotting
+    estimated_1rm_points = zip(estimated_1rm_scaled, relative_positions)
+    repetitions_points = zip(repetitions_scaled, relative_positions)
+    weight_points = zip(weight_scaled, relative_positions)
+
+    repetitions = {
+        'label': 'Reps',
+        'color': '#388fed',
+        'points': list(repetitions_points)
+    }
+    weight = {
+        'label': 'Weight',
+        'color': '#bd3178',
+        'points': list(weight_points)
+    }
+    estimated_1rm = {
+        'label': 'E1RM',
+        'color': '#2ca02c',
+        'points': list(estimated_1rm_points)
+    }
+
+    plot_labels = zip(relative_positions, messages)
+
+    # Return exercise data with SVG dimensions and data points
+    return {
+        'title': title,
+        'vb_width': vb_width,
+        'vb_height': vb_height,
+        'plots': [repetitions, weight, estimated_1rm],
+        'best_fit_points': best_fit_points,
+        'plot_labels': plot_labels
+    }
\ No newline at end of file