Update app.py

app.py

@@ -7,6 +7,7 @@ from sklearn.metrics.pairwise import cosine_similarity
 import plotly.express as px
 import os
 import tempfile
+from scipy.signal import get_window as scipy_get_window  # ✅ Fix here
 
 # ----------------------------
 # Core Functions
@@ -22,8 +23,10 @@ def segment_audio(y, sr, frame_length_ms, hop_length_ms, window_type):
     if frame_length > len(y):
         raise ValueError("Frame length is longer than audio duration.")
     frames = librosa.util.frame(y, frame_length=frame_length, hop_length=hop_length)
+    
     if window_type != "rectangular":
-        window = librosa.get_window(window_type, frame_length)
+        # ✅ Use scipy.signal.get_window instead of librosa.get_window
+        window = scipy_get_window(window_type, frame_length)
         frames = frames * window[:, None]
     return frames, sr
 
@@ -94,7 +97,6 @@ def analyze_audio_pair(
     frame_length_ms,
     hop_length_ms,
     window_type,
-    features_list,
     n_clusters,
     comparison_metrics
 ):
@@ -116,20 +118,18 @@ def analyze_audio_pair(
     frames_near, _ = segment_audio(y_near, sr, frame_length_ms, hop_length_ms, window_type)
     frames_far, _ = segment_audio(y_far, sr, frame_length_ms, hop_length_ms, window_type)
 
-    # Extract features (always extract base set)
+    # Extract features
     near_features = extract_features(frames_near, sr)
     far_features = extract_features(frames_far, sr)
 
     # Compare
     comparison_df = compare_frames(near_features, far_features, comparison_metrics)
 
-    # Cluster near-field (you can choose which to cluster)
+    # Cluster near-field
     clustered_df = cluster_frames(near_features, n_clusters)
 
     # Plots
     plot_comparison = None
-    plot_scatter = None
-
     if "Euclidean Distance" in comparison_metrics:
         plot_comparison = px.line(
             comparison_df,
@@ -145,14 +145,12 @@ def analyze_audio_pair(
             title="Cosine Similarity Between Near & Far Frames"
         )
     else:
-        plot_comparison = px.line(
-            comparison_df,
-            x="frame_index",
-            y=comparison_df.columns[1] if len(comparison_df.columns) > 1 else [],
-            title="Frame Comparison"
-        )
+        # Fallback
+        col = comparison_df.columns[1] if len(comparison_df.columns) > 1 else "frame_index"
+        plot_comparison = px.line(comparison_df, x="frame_index", y=col, title="Frame Comparison")
 
-    # Scatter: RMS vs Spectral Centroid
+    # Scatter plot
+    plot_scatter = None
     if "rms" in clustered_df.columns and "spectral_centroid" in clustered_df.columns:
         plot_scatter = px.scatter(
             clustered_df,
@@ -165,25 +163,15 @@ def analyze_audio_pair(
     else:
         plot_scatter = px.scatter(title="Not enough features for scatter plot")
 
-    return (
-        plot_comparison,
-        comparison_df,
-        plot_scatter,
-        clustered_df
-    )
+    return plot_comparison, comparison_df, plot_scatter, clustered_df
 
 def export_results(plot_comparison, comparison_df, plot_scatter, clustered_df):
     temp_dir = tempfile.mkdtemp()
-    files = []
-
-    # Save DataFrames
     comp_path = os.path.join(temp_dir, "frame_comparisons.csv")
     cluster_path = os.path.join(temp_dir, "clustered_frames.csv")
     comparison_df.to_csv(comp_path, index=False)
     clustered_df.to_csv(cluster_path, index=False)
-    files.extend([comp_path, cluster_path])
-
-    return files
+    return [comp_path, cluster_path]
 
 # ----------------------------
 # Gradio Interface
@@ -230,7 +218,6 @@ with gr.Blocks(title="Near vs Far Field Audio Analyzer") as demo:
         inputs=[
             near_file, far_file,
             frame_length_ms, hop_length_ms, window_type,
-            gr.State([]),  # features_list (not used in UI but kept for extensibility)
             n_clusters,
             comparison_metrics
         ],