Update app.py

app.py

@@ -72,8 +72,16 @@ def newton_backward_interpolation(x, y, x_interp):
     
     return np.array([newton_backward(xi) for xi in x_interp])
 
-def create_and_edit_plot(x, y, x_interp, y_interp, method, plot_title, x_label, y_label, legend_position, label_size, x_predict=None, y_predict=None):
+def create_and_edit_plot(x, y, x_interp, y_interp, method, plot_title, x_label, y_label, legend_position, label_size, log_x, x_predict=None, y_predict=None):
     fig, ax = plt.subplots(figsize=(10, 6))
+    
+    if log_x:
+        # Ensure all x-values are positive before setting log scale
+        if np.any(np.array(x) <= 0):
+            return create_error_plot("Error: All x values must be positive for logarithmic scale."), \
+                   '<p style="color: red;">Error: All x values must be positive for logarithmic scale.</p>'
+        ax.set_xscale('log')
+
     ax.scatter(x, y, color='red', label='Input points')
     ax.plot(x_interp, y_interp, label=f'{method} interpolant')
     ax.set_xlabel(x_label, fontsize=label_size)
@@ -89,7 +97,7 @@ def create_and_edit_plot(x, y, x_interp, y_interp, method, plot_title, x_label,
 
     return fig
 
-def interpolate_and_plot(x_input, y_input, x_predict, method, plot_title, x_label, y_label, legend_position, label_size):
+def interpolate_and_plot(x_input, y_input, x_predict, method, plot_title, x_label, y_label, legend_position, label_size, log_x):
     try:
         x = np.array([float(val.strip()) for val in x_input.split(',')])
         y = np.array([float(val.strip()) for val in y_input.split(',')])
@@ -144,34 +152,55 @@ def interpolate_and_plot(x_input, y_input, x_predict, method, plot_title, x_labe
             
             y_predict = np.interp(x_predict, x_interp, y_interp)
             
-            fig = create_and_edit_plot(x, y, x_interp, y_interp, method, plot_title, x_label, y_label, legend_position, label_size, x_predict, y_predict)
+            fig = create_and_edit_plot(x, y, x_interp, y_interp, method, plot_title, x_label, y_label, legend_position, label_size, log_x, x_predict, y_predict)
             return fig, f"Predicted y value for x = {x_predict}: {y_predict:.4f}"
         except ValueError:
             error_msg = "Error: Invalid input for x prediction. Please enter a number."
             return create_error_plot(error_msg), f'<p style="color: red;">{error_msg}</p>'
     
-    fig = create_and_edit_plot(x, y, x_interp, y_interp, method, plot_title, x_label, y_label, legend_position, label_size)
+    fig = create_and_edit_plot(x, y, x_interp, y_interp, method, plot_title, x_label, y_label, legend_position, label_size, log_x)
     return fig, None
 
-iface = gr.Interface(
-    fn=interpolate_and_plot,
-    inputs=[
-        gr.Textbox(label="X values (comma-separated)"),
-        gr.Textbox(label="Y values (comma-separated)"),
-        gr.Number(label="X value to predict (optional)", value=lambda: None),
-        gr.Radio(["Linear", "Quadratic", "Lagrange", "Newton Forward", "Newton Backward"], label="Interpolation Method", value="Linear"),
-        gr.Textbox(label="Plot Title", value="Interpolation Plot"),
-        gr.Textbox(label="X-axis Label", value="x"),
-        gr.Textbox(label="Y-axis Label", value="y"),
-        gr.Dropdown(["best", "upper right", "upper left", "lower left", "lower right", "right", "center left", "center right", "lower center", "upper center", "center"], label="Legend Position", value="best"),
-        gr.Slider(minimum=8, maximum=24, step=1, label="Label Size", value=12)
-    ],
-    outputs=[
-        gr.Plot(label="Interpolation Plot"),
-        gr.HTML(label="Result or Error Message")
-    ],
-    title="Interpolation App",
-    description="Enter x and y values to see the interpolation graph. Choose the interpolation method using the radio buttons. Optionally, enter an x value (between min and max of input x values) to predict its corresponding y value. Note: Newton Forward and Backward methods require uniform x spacing. You can also customize the plot labels, legend position, and label size."
-)
+def toggle_plot_options(show_options):
+    return not show_options, gr.update(visible=not show_options)
+
+with gr.Blocks() as iface:
+    gr.Markdown("# Interpolation App")
+    gr.Markdown("Enter x and y values to see the interpolation graph. Choose the interpolation method using the radio buttons. Optionally, enter an x value (between min and max of input x values) to predict its corresponding y value. Note: Newton Forward and Backward methods require uniform x spacing.")
+    
+    show_options = gr.State(False)
+    
+    with gr.Row():
+        with gr.Column():
+            x_input = gr.Textbox(label="X values (comma-separated)")
+            y_input = gr.Textbox(label="Y values (comma-separated)")
+            x_predict = gr.Number(label="X value to predict (optional)", value=lambda: None)
+            method = gr.Radio(["Linear", "Quadratic", "Lagrange", "Newton Forward", "Newton Backward"], label="Interpolation Method", value="Linear")
+            submit_btn = gr.Button("Generate Plot", variant="primary", elem_id="submit-btn")
+            edit_plot_btn = gr.Button("Edit Plot", variant="secondary")
+        
+        with gr.Column():
+            plot_output = gr.Plot(label="Interpolation Plot")
+            result_output = gr.HTML(label="Result or Error Message")
+    
+    plot_options = gr.Column(visible=False)
+    with plot_options:
+        plot_title = gr.Textbox(label="Plot Title", value="Interpolation Plot")
+        x_label = gr.Textbox(label="X-axis Label", value="x")
+        y_label = gr.Textbox(label="Y-axis Label", value="y")
+        legend_position = gr.Dropdown(["best", "upper right", "upper left", "lower left", "lower right", "right", "center left", "center right", "lower center", "upper center", "center"], label="Legend Position", value="best")
+        label_size = gr.Slider(minimum=8, maximum=24, step=1, label="Label Size", value=14)
+        log_x = gr.Checkbox(label="Log scale for X-axis", value=False)
+    
+    edit_plot_btn.click(
+        toggle_plot_options,
+        inputs=[show_options],
+        outputs=[show_options, plot_options]
+    )
+    
+    inputs = [x_input, y_input, x_predict, method, plot_title, x_label, y_label, legend_position, label_size, log_x]
+    outputs = [plot_output, result_output]
+    
+    submit_btn.click(interpolate_and_plot, inputs=inputs, outputs=outputs)
 
-iface.launch()
+iface.launch()