diff --git a/cloud-functions/python/functions/performance/main.py b/cloud-functions/python/functions/performance/main.py
index 00ec514..e1cbfd7 100644
--- a/cloud-functions/python/functions/performance/main.py
+++ b/cloud-functions/python/functions/performance/main.py
@@ -15,6 +15,7 @@ import re
 import xlsxwriter
 import math
 import matplotlib.pyplot as plt
+from scipy.stats import linregress
 
 initialize_app()
 
@@ -59,27 +60,27 @@ def performance(request):
     
     if 'file' not in request.files:
         print('No file part')
+    else:
+        file = request.files['file']
 
-    file = request.files['file']
+        if file.filename == '':
+            print('No selected file')
+        
+        # Save the file to a temporary location
+        temp_filepath = '/tmp/temp_file.csv'
+        file.save(temp_filepath)
 
-    if file.filename == '':
-        print('No selected file')
-    
-    # Save the file to a temporary location
-    temp_filepath = '/tmp/temp_file.csv'
-    file.save(temp_filepath)
-
-    # Load the file into a Pandas DataFrame
-    try:
-        df_reference_device_raw = pd.read_csv(temp_filepath)  # Adjust the read method based on your file type (e.g., read_excel for Excel files)
-        # Now you can work with the DataFrame (e.g., perform analysis or display it)
-        print(df_reference_device_raw.head())
-        print('File uploaded and loaded into DataFrame successfully')
-    except Exception as e:
-        print(f'Error loading the file: {str(e)}')
-    finally:
-        # Remove the temporary file
-        os.remove(temp_filepath)
+        # Load the file into a Pandas DataFrame
+        try:
+            df_reference_device_raw = pd.read_csv(temp_filepath)  # Adjust the read method based on your file type (e.g., read_excel for Excel files)
+            # Now you can work with the DataFrame (e.g., perform analysis or display it)
+            print(df_reference_device_raw.head())
+            print('File uploaded and loaded into DataFrame successfully')
+        except Exception as e:
+            print(f'Error loading the file: {str(e)}')
+        finally:
+            # Remove the temporary file
+            os.remove(temp_filepath)
 
 
     path_delim = "/"
@@ -283,6 +284,7 @@ def performance(request):
         wks1.write(0, 0, 'Solution')
         wks1.write(0, 1, 'Slope')
         wks1.write(0, 2, 'Intercept')
+        wks1.write(0, 3, 'R^2')
         row = 1 
 
         fig, ax = plt.subplots()
@@ -295,33 +297,38 @@ def performance(request):
                 
                 concentration_means = solution_data.groupby('concentration')['absorbance'].mean()
                 
+                # Fit a linear regression model
                 coefficients = np.polyfit(concentration_means.index, concentration_means.values, 1)
+
+                # Use linregress to get additional statistics including R-squared
+                slope, intercept, r_value, p_value, std_err = linregress(concentration_means.index, concentration_means.values)
                 
-                print(f'Coefficients for {solution}: Slope={coefficients[0]}, Intercept={coefficients[1]}')
+                print(f'For {solution}: Slope={slope:.4f}, Intercept={intercept:.4f}, R^2={r_value**2:.4f}')
 
                 ax.plot(concentration_means.index, concentration_means.values, marker='o', linestyle='-', label=f'Mean Absorbance for {solution}')
 
                 ax.plot(concentration_means.index, np.polyval(coefficients, concentration_means.index), linestyle='--', label=f'Linear Fit for {solution}')
                 
-                annotation_text = f'Slope: {coefficients[0]:.4f}\nIntercept: {coefficients[1]:.4f}'
+                annotation_text = f'Slope: {slope:.4f}\nIntercept: {intercept:.4f}\nR^2: {r_value**2:.4f}'
                 ax.text(concentration_means.index[-1] + 5, np.polyval(coefficients, concentration_means.index[-1]), annotation_text, fontsize=10, verticalalignment='center')
 
                 wks1.write(row, 0, solution)
-                wks1.write(row, 1, str(coefficients[0]))
-                wks1.write(row, 2, str(coefficients[1]))
+                wks1.write(row, 1, str(slope))
+                wks1.write(row, 2, str(intercept))
+                wks1.write(row, 3, str(r_value**2))
                 
                 row += 1
 
-        # writer.save()
-
         ax.set_title('Mean Absorbance for Each Solution')
         ax.set_xlabel('Concentration')
         ax.set_ylabel('Mean Absorbance')
         ax.legend()
+        # plt.show()
 
         imgdata=io.BytesIO()
         fig.savefig(imgdata, format='png')
-        wks1.insert_image(2,2, '', {'image_data': imgdata})
+        wks1.insert_image(6,0, '', {'image_data': imgdata})
+
 
 
         ### debug each row