Store .npz files in current dir

app.py

@@ -6,24 +6,23 @@ import numpy as np
 import gradio as gr
 import plotly.graph_objs as go
 
-# Path to the npz folder
-npz_folder = "npz"
-
 glob_a = -2
 glob_b = -2
 glob_c = -4
 glob_d = 7
 
-def clear_folder(folder_path=npz_folder):
-    for filename in os.listdir(folder_path):
-        file_path = os.path.join(folder_path, filename)
-        try:
-            if os.path.isfile(file_path) or os.path.islink(file_path):
-                os.unlink(file_path)  # Remove the file or symbolic link
-            elif os.path.isdir(file_path):
-                shutil.rmtree(file_path)  # Remove the directory and its contents
-        except Exception as e:
-            print(f"Failed to delete {file_path}. Reason: {e}")
+def clear_npz():
+    current_directory = os.getcwd()  # Get the current working directory
+    for filename in os.listdir(current_directory):
+        if filename.endswith(".npz"):  # Check if the file ends with .npz
+            file_path = os.path.join(current_directory, filename)
+            try:
+                if os.path.isfile(file_path) or os.path.islink(file_path):
+                    os.unlink(file_path)  # Remove the file or symbolic link
+                else:
+                    print(f"Skipping {file_path}, not a file or symbolic link.")
+            except Exception as e:
+                print(f"Failed to delete {file_path}. Reason: {e}")
 
 
 def complex_heat_eq_solution(x, t, a=glob_a, b=glob_b, c=glob_c, d=glob_d, k=0.5):
@@ -41,7 +40,7 @@ def plot_heat_equation(m, approx_type):
     n_t = 50
 
     try:
-        loaded_values = np.load(f"npz/{approx_type}_m{m}.npz")
+        loaded_values = np.load(f"{approx_type}_m{m}.npz")
     except:
         raise gr.Error(f"First train the coefficients for {approx_type} and m = {m}")
     alpha = loaded_values["alpha"]
@@ -138,7 +137,7 @@ def plot_errors(m, approx_type):
     n_t = 50
 
     try:
-        loaded_values = np.load(f"npz/{approx_type}_m{m}.npz")
+        loaded_values = np.load(f"{approx_type}_m{m}.npz")
     except:
         raise gr.Error(f"First train the coefficients for {approx_type} and m = {m}")
     alpha = loaded_values["alpha"]
@@ -216,14 +215,6 @@ def plot_errors(m, approx_type):
 
     fig.show(config=config)
 
-
-# Function to get the available .npz files in the npz folder
-def get_available_approx_files():
-    files = os.listdir(npz_folder)
-    npz_files = [f for f in files if f.endswith(".npz")]
-    return npz_files
-
-
 def generate_data(n_x=32, n_t=50):
     """Generate training data."""
     x = np.linspace(0, 1, n_x)  # spatial points
@@ -310,7 +301,7 @@ def train_coefficients(m, kernel):
 
     # Save values to the npz folder
     np.savez(
-        f"{npz_folder}/{kernel}_m{m}.npz",
+        f"{kernel}_m{m}.npz",
         alpha=alpha,
         kernel=kernel,
         Phi=Phi,
@@ -465,7 +456,7 @@ def create_gradio_ui():
                 error_button.click(
                     fn=plot_errors, inputs=[m_slider, kernel_dropdown], outputs=None
                 )
-        demo.load(fn=clear_folder, inputs=None, outputs=None)
+        demo.load(fn=clear_npz, inputs=None, outputs=None)
         demo.load(fn=plot_function, inputs=[a_slider, b_slider, c_slider, d_slider], outputs=[plot_output])
 
     return demo