Update app.py

app.py

@@ -11,6 +11,30 @@ st.title('PantoScanner')
 st.header('Example')
 st.subheader('Thickness measurement of sliding element')
 
+import numpy as np
+
+def generate_data(slope, intercept, num_points):
+  """
+  Generates data points with a linear degression and a +/- 5% tolerance.
+
+  Args:
+      slope: The slope of the linear degression.
+      intercept: The y-intercept of the linear degression.
+      num_points: The number of data points to generate.
+
+  Returns:
+      A numpy array of size (num_points, 1) containing the data points.
+  """
+  x = np.linspace(0, 1, num_points)  # Creates evenly spaced x-values
+  y = slope * x + intercept        # Generates linear function values
+
+  # Add random noise with +/- 5% tolerance
+  noise = np.random.uniform(low=-0.05, high=0.05, size=num_points)
+  y += noise * y  # Scale noise by original y value for percentage variation
+
+  return y.reshape(-1, 1)  # Reshape to column vector
+
+
 tab1, tab2, tab3 = st.tabs([f' {image_emoji}  Image', f' {model_emoji}  Mask', f' {profile_emoji}  Measurement'])
 
 with tab1:
@@ -24,7 +48,15 @@ with tab2:
 
 with tab3:
    st.header(f'Profile Height')
-   data = np.random.randn(10, 1)
+   # data = np.random.randn(10, 1)
+   # Example usage
+   # Use 'data' for your chart with linear degression and +/- 5% tolerance
+
+   slope = -2  # Example slope for linear degression
+   intercept = 10
+   num_points = 20
+    
+   data = generate_data(slope, intercept, num_points)
    st.line_chart(data)