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app.py

@@ -7,7 +7,7 @@ import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
 
-BASE_PATH = 'Models' 
+BASE_PATH = 'Models'
 
 st.set_page_config(layout="wide", page_title="Audio Source Separation Inspector")
 
@@ -21,7 +21,7 @@ def load_spectrogram_interactive(pt_path, title="Spectrogram"):
   """Loads a .pt spectrogram and returns a Plotly figure."""
   try:
     spec_tensor = torch.load(pt_path, map_location='cpu')
-    
+
     # Handle dimensions: [Channels, Freq, Time]
     if spec_tensor.dim() == 4: # [Batch, C, F, T]
       spec_tensor = spec_tensor[0]
@@ -33,13 +33,13 @@ def load_spectrogram_interactive(pt_path, title="Spectrogram"):
     # Log scaling for better visibility
     if spec_data.min() >= 0:
       spec_data = np.log1p(spec_data)
-    
+
     # Create interactive heatmap
     fig = px.imshow(
-        spec_data, 
-        origin='lower', 
-        aspect='auto', 
-        color_continuous_scale='Magma',
+        spec_data,
+        origin='lower',
+        aspect='auto',
+        color_continuous_scale='Viridis',
         labels=dict(x="Time Frame", y="Frequency Bin", color="Log Magnitude"),
         title=title
     )
@@ -53,21 +53,21 @@ def load_feature_map_interactive(pt_path):
   """Loads an internal feature map and visualizes its mean activation interactively."""
   try:
     feat_tensor = torch.load(pt_path, map_location='cpu')
-    
+
     # Squeeze batch if present
-    if feat_tensor.dim() == 4: 
+    if feat_tensor.dim() == 4:
       feat_tensor = feat_tensor[0]
-    
+
     # feat_tensor is likely [Channels, Freq, Time]
     mean_activation = feat_tensor.mean(dim=0).numpy()
-    
+
     fig = px.imshow(
-        mean_activation, 
-        origin='lower', 
-        aspect='auto', 
-        color_continuous_scale='Viridis',
-        labels=dict(x="Time", y="Freq/Feature", color="Activation"),
-        title=f"Mean Activation (Shape: {list(feat_tensor.shape)})"
+      mean_activation,
+      origin='lower',
+      aspect='auto',
+      color_continuous_scale='Viridis',
+      labels=dict(x="Time", y="Freq/Feature", color="Activation"),
+      title=f"Mean Activation (Shape: {list(feat_tensor.shape)})"
     )
     fig.update_layout(margin=dict(l=0, r=0, t=40, b=0))
     return fig
@@ -88,26 +88,26 @@ selected_model = st.sidebar.selectbox("Select Model", models)
 if selected_model:
   model_path = os.path.join(BASE_PATH, selected_model)
   artifacts_path = os.path.join(model_path, "test_artifacts")
-  
+
   # 2. Select Sample
   if os.path.exists(artifacts_path):
     samples = get_subdirs(artifacts_path)
     # Sort samples numerically
     samples.sort(key=lambda x: int(x.split('_')[-1]) if '_' in x else 0)
-    
+
     selected_sample = st.sidebar.selectbox("Select Sample ID", samples)
-    
+
     if selected_sample:
       sample_path = os.path.join(artifacts_path, selected_sample)
       audio_dir = os.path.join(sample_path, "audio")
       specs_dir = os.path.join(sample_path, "specs")
       feats_dir = os.path.join(sample_path, "feats")
-      
+
       # 3. Detect Classes
       all_files = os.listdir(audio_dir)
       target_files = [f for f in all_files if f.startswith("target_") and f.endswith(".wav")]
       classes = [f.replace("target_", "").replace(".wav", "") for f in target_files]
-      
+
       # Sidebar Class Filter
       selected_class = st.sidebar.selectbox("Focus Class", classes)
 
@@ -116,12 +116,12 @@ if selected_model:
 
       with tab1:
         st.header(f"Sample {selected_sample} | Focus: {selected_class.capitalize()}")
-        
+
         # --- Mixture (Input) ---
         st.subheader("1. Mixture (Input)")
         mix_audio = os.path.join(audio_dir, "mixture.wav")
         mix_spec = os.path.join(specs_dir, "mixture.pt")
-        
+
         c1, c2 = st.columns([1, 3]) # Audio on left, Graph on right (wider)
         with c1:
             if os.path.exists(mix_audio):
@@ -129,16 +129,16 @@ if selected_model:
                 st.audio(mix_audio)
         with c2:
             if os.path.exists(mix_spec):
-                fig = load_spectrogram_interactive(mix_spec, title="Mixture Spectrogram")
+                fig = load_spectrogram_interactive(mix_spec, title="Mixture Mel-Spectrogram")
                 if fig: st.plotly_chart(fig, width='stretch')
-        
+
         st.divider()
 
         # --- Target (Ground Truth) ---
         st.subheader(f"2. Target: {selected_class}")
         tgt_audio = os.path.join(audio_dir, f"target_{selected_class}.wav")
         tgt_spec = os.path.join(specs_dir, f"target_{selected_class}.pt")
-        
+
         c1, c2 = st.columns([1, 3])
         with c1:
             if os.path.exists(tgt_audio):
@@ -146,16 +146,16 @@ if selected_model:
                 st.audio(tgt_audio)
         with c2:
             if os.path.exists(tgt_spec):
-                fig = load_spectrogram_interactive(tgt_spec, title=f"Target Spectrogram ({selected_class})")
+                fig = load_spectrogram_interactive(tgt_spec, title=f"Target Mel-Spectrogram ({selected_class})")
                 if fig: st.plotly_chart(fig, width='stretch')
-        
+
         st.divider()
 
         # --- Prediction (Output) ---
         st.subheader(f"3. Prediction: {selected_class}")
         pred_audio = os.path.join(audio_dir, f"pred_{selected_class}.wav")
         pred_spec = os.path.join(specs_dir, f"pred_{selected_class}.pt")
-        
+
         c1, c2 = st.columns([1, 3])
         with c1:
             if os.path.exists(pred_audio):
@@ -163,15 +163,15 @@ if selected_model:
                 st.audio(pred_audio)
         with c2:
             if os.path.exists(pred_spec):
-                fig = load_spectrogram_interactive(pred_spec, title=f"Predicted Spectrogram ({selected_class})")
+                fig = load_spectrogram_interactive(pred_spec, title=f"Predicted Mel-Spectrogram ({selected_class})")
                 if fig: st.plotly_chart(fig, width='stretch')
-      
+
       with tab2:
         st.header("Internal Feature Maps")
-        
+
         if os.path.exists(feats_dir):
           feat_files = sorted(os.listdir(feats_dir))
-          
+
           if feat_files:
             selected_layer = st.selectbox("Select Probed Layer", feat_files)
             if selected_layer:
@@ -186,7 +186,7 @@ if selected_model:
 
       with tab3:
         st.header("Training and Testing Logs")
-        
+
         c1, c2 = st.columns(2)
         with c1:
           results_csv = os.path.join(model_path, "test_results.csv")
@@ -199,7 +199,7 @@ if selected_model:
             st.dataframe(df, width='stretch')
           else:
             st.info("No `test_results.csv` found.")
-                
+
         with c2:
           loss_csv = os.path.join(model_path, "loss.csv")
           if os.path.exists(loss_csv):
@@ -208,7 +208,7 @@ if selected_model:
               df_loss = pd.read_csv(loss_csv)
               # Try to find an epoch column, otherwise use index
               x_axis = 'epoch' if 'epoch' in df_loss.columns else df_loss.index
-              
+
               # Melt if multiple loss columns exist for better visualization
               numeric_cols = df_loss.select_dtypes(include=np.number).columns
               fig = px.line(df_loss, x=x_axis, y=numeric_cols, title="Loss Curves")