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README.md

@@ -3,7 +3,7 @@ title: AnomalyMachine-50K Demo
 colorFrom: red
 colorTo: gray
 sdk: gradio
-sdk_version: 4.38.0
+sdk_version: 4.35.0
 python_version: "3.10"
 app_file: app.py
 pinned: true

app.py

@@ -55,7 +55,7 @@ def create_spectrogram(audio_path, title="Spectrogram"):
 def predict_anomaly(audio_file, machine_type):
     """Predict anomaly in audio."""
     if audio_file is None:
-        return "Please upload an audio file.", 0.0, None, None
+        return "Please upload an audio file.", None, None
     
     import random
     random.seed(hash(str(audio_file)) % 1000)
@@ -81,7 +81,7 @@ def predict_anomaly(audio_file, machine_type):
         if ref_files:
             ref_spec = create_spectrogram(str(ref_files[0]), f"Reference - {machine_type}")
     
-    return result, confidence, input_spec, ref_spec
+    return result, input_spec, ref_spec
 
 
 theme = gr.themes.Monochrome(primary_hue="red", secondary_hue="gray")
@@ -105,7 +105,6 @@ with gr.Blocks(theme=theme, title="AnomalyMachine-50K Demo") as app:
                 
                 with gr.Column():
                     result_html = gr.HTML()
-                    confidence_bar = gr.Slider(minimum=0, maximum=1, value=0, label="Confidence", interactive=False)
             
             with gr.Row():
                 input_spec = gr.Image(label="Input Spectrogram")
@@ -114,7 +113,7 @@ with gr.Blocks(theme=theme, title="AnomalyMachine-50K Demo") as app:
             predict_btn.click(
                 fn=predict_anomaly,
                 inputs=[audio_input, machine_dropdown],
-                outputs=[result_html, confidence_bar, input_spec, ref_spec]
+                outputs=[result_html, input_spec, ref_spec]
             )
         
         with gr.Tab("📊 Explore Dataset"):

requirements.txt

@@ -1,4 +1,4 @@
-gradio==4.38.0
+gradio==4.35.0
 librosa==0.10.2
 matplotlib==3.8.2
 numpy==1.26.4