changed model path

app.py

@@ -1,42 +1,42 @@
-import gradio as gr
-import torch
-from functions import *
-from unet import UNet
-from custom_scaler import min_max_scaler
-
-model = UNet()
-model_state_dict = torch.load("huggingface/model.pth", map_location="cpu")
-model.load_state_dict(model_state_dict["model_state_dict"])
-
-scaler = min_max_scaler()
-scaler.fit()
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        gr.Markdown(
-    """
-    # Speech enhancement demonstration
-    
-    Hello!
-    
-    This is a demo for a speech enhancement model trained to reduce background noice to ensure inteligibility of a single speaker.
-    
-    Feel free to upload your own audio file or try one of our example files to see how it works!
-    
-    """
-        )
-    with gr.Row():
-        with gr.Column():
-            audio_path = gr.Audio(sources=["upload", "microphone"], type="filepath", label="Upload your song here", format="wav")
-        with gr.Column():
-            enhanced_audio = gr.Audio(sources=None, label="Enhanced audio will be found here", format="wav")
-    with gr.Row():
-        files = gr.FileExplorer(label="Example files", file_count="single", root_dir=r"examples", interactive=True)
-        files.change(fn=return_input, inputs=files, outputs=audio_path)
-        files.change(fn=return_input, inputs=None, outputs=enhanced_audio)
-    with gr.Row():
-        submit_audio = gr.Button(value="Submit audio for enhancement")
-        submit_audio.click(fn=lambda x: predict(x, model, scaler), inputs=audio_path, outputs=enhanced_audio, trigger_mode="once")
-
-demo.launch(share=True)
-
+import gradio as gr
+import torch
+from functions import *
+from unet import UNet
+from custom_scaler import min_max_scaler
+
+model = UNet()
+model_state_dict = torch.load(r"model.pth", map_location="cpu")
+model.load_state_dict(model_state_dict["model_state_dict"])
+
+scaler = min_max_scaler()
+scaler.fit()
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        gr.Markdown(
+    """
+    # Speech enhancement demonstration
+    
+    Hello!
+    
+    This is a demo for a speech enhancement model trained to reduce background noice to ensure inteligibility of a single speaker.
+    
+    Feel free to upload your own audio file or try one of our example files to see how it works!
+    
+    """
+        )
+    with gr.Row():
+        with gr.Column():
+            audio_path = gr.Audio(sources=["upload", "microphone"], type="filepath", label="Upload your song here", format="wav")
+        with gr.Column():
+            enhanced_audio = gr.Audio(sources=None, label="Enhanced audio will be found here", format="wav")
+    with gr.Row():
+        files = gr.FileExplorer(label="Example files", file_count="single", root_dir=r"examples", interactive=True)
+        files.change(fn=return_input, inputs=files, outputs=audio_path)
+        files.change(fn=return_input, inputs=None, outputs=enhanced_audio)
+    with gr.Row():
+        submit_audio = gr.Button(value="Submit audio for enhancement")
+        submit_audio.click(fn=lambda x: predict(x, model, scaler), inputs=audio_path, outputs=enhanced_audio, trigger_mode="once")
+
+demo.launch(share=True)
+