Update app.py

app.py

@@ -1,50 +1,9 @@
-import gradio as gr
-from demo import * 
+import zipfile
 
+# Specify the path to the ZIP file
+zip_path = 'app.zip'
 
-
-article = """
-<p style='text-align: center'>
-    <a href='https://github.com/deep-matter/Nano-AutoGrad' target='_blank'>Github Repo Nano-AutoGrad</a>
-</p>
-"""
-
-iface_webcam = gr.Interface(
-    Optimization_training_progress_realtime,
-    inputs=[
-        gr.Radio(["Sparsity"], label="Task"),
-        gr.Slider(minimum=1, maximum=100, label="Number of Epochs"),
-        gr.Slider(minimum=0.01, maximum=1.0, label="Learning Rate"),
-        gr.Number(label="Number of Layers"),
-        gr.Number(label="Values for Weights")
-        # gr.inputs.Slider(minimum=6, maximum=18, step=6, default=12),  # Leaving manual fps out for now
-    ],
-    outputs=[gr.Plot(),gr.Video(),gr.Video()],
-    title="Optimization Training Progress",
-    description="Real-time visualization of training progress",
-    article=article,
-    allow_flagging=False,
-)
-
-iface_file = gr.Interface(
-    Optimization_training_progress_realtime,
-    inputs=[
-        gr.Radio(["Classification"], label="Task"),
-        gr.Slider(minimum=1, maximum=100, label="Number of Epochs"),
-        gr.Slider(minimum=0.01, maximum=1.0, label="Learning Rate"),
-        gr.Number(label="Number of Layers"),
-        gr.Number(label="Values for Weights")
-    ],
-    outputs=[gr.Plot(),gr.Video(),gr.Video()],
-    title="Optimization Training Progress",
-    description="Real-time visualization of training progress",
-    article=article,
-    allow_flagging=False,
-)
-
-if __name__ == '__main__':
-    gr.TabbedInterface(
-        interface_list=[iface_file, iface_webcam],
-        tab_names=["Classification Task", "Sparsity Task"]
-        
-    ).launch(enable_queue=True,share=False)
+# Open the ZIP file
+with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+    # Extract all files and directories in the ZIP file
+    zip_ref.extractall('.')