More changes

app.py

@@ -87,18 +87,22 @@ if uploaded_file is not None:
                          "Punk","Bebop", "Pop", "R&B", "Country", "Rap & Hip-Hop", "Soul"]
     class_indices = {i: class_name for i, class_name in enumerate(class_names)}
 
-    features_list = audio_splitting.split_audio(uploaded_file)
+    features_list,val_list = audio_splitting.split_audio(uploaded_file)
     features = feature_extraction.scale(features_list)
 
     # st.write(features)
     # Features Dataframe
     df = pd.DataFrame({
-        "fname": ["Feature -1", "Feature - 2"]
+        "fname": ["Chroma_STFT"],
+        "Values": val_list
     })
     st.dataframe(
         df,
         column_config={
-            "name": "Features"
+            "name": "Features",
+            "Values": st.column_config.LineChartColumn(
+                "Graph Values",y_min=0,y_max = 10000
+            )
         }
     )
 

feature_extraction.py

@@ -31,16 +31,19 @@ short_field = Fields[2:]
 
 def all_feature_extraction(audio_path, sample_rate=22050):
     data_list = []
+    val_field = []
+
     audio_df, sr = librosa.load(audio_path, sr=22050)
-    print("\n",audio_df)
     data_list.append(audio_path)
-    print(audio_path)
     data_list.append(len(audio_df))
-    print(data_list)
+
+
     # 1. Chroma STFT
     chroma_stft = librosa.feature.chroma_stft(y=audio_df, hop_length=512)
     chroma_stft_mean = np.mean(chroma_stft)
     chroma_stft_var = np.var(chroma_stft)
+
+    val_field.append(chroma_stft)
     data_list.append(chroma_stft_mean)
     data_list.append(chroma_stft_var)
 
@@ -103,8 +106,8 @@ def all_feature_extraction(audio_path, sample_rate=22050):
     for mean, var in mfcc_list:
         data_list.append(mean)
         data_list.append(var)
-    print(data_list)
-    return data_list
+
+    return [data_list,val_field]
 
 def scale(initial_features):
     final_features = initial_features[2:]