Update app.py

app.py

@@ -4,6 +4,7 @@ import numpy as np
 import librosa
 from torchmetrics.functional.audio.nisqa import non_intrusive_speech_quality_assessment as tm_nisqa
 import spaces
+import pandas as pd
 
 SR = 16000
 
@@ -52,15 +53,18 @@ def predict_nisqa(audio):
         ("Loudness", loudness, label_dim(loudness), explain_dim("Loudness")),
     ]
 
-    table = {
-        "Metric":   [m[0] for m in metrics],
-        "Score":    [round(m[1], 3) for m in metrics],
-        "Label":    [m[2] for m in metrics],
-        "Notes":    [m[3] for m in metrics],
-    }
-    bars = {m[0]: float(m[1]) for m in metrics}
-
-    return table, bars
+    df_table = pd.DataFrame(
+        {
+            "Metric": [m[0] for m in metrics],
+            "Score":  [round(float(m[1]), 3) for m in metrics],
+            "Label":  [m[2] for m in metrics],
+            "Notes":  [m[3] for m in metrics],
+        }
+    )
+    df_bars = pd.DataFrame(
+        {"Metric": [m[0] for m in metrics], "Score": [float(m[1]) for m in metrics]}
+    )
+    return df_table, df_bars
 
 with gr.Blocks(title="NISQA Speech Quality (MOS) Demo") as demo:
     gr.Markdown(
@@ -71,27 +75,19 @@ with gr.Blocks(title="NISQA Speech Quality (MOS) Demo") as demo:
         **Dimensions:** higher = fewer issues in that aspect.
         """
     )
-    audio = gr.Audio(sources=["microphone", "upload"], type="filepath", label="Input audio")
+    audio = gr.Audio(sources=["upload", "microphone"], type="filepath", label="Input audio")
     btn = gr.Button("Predict")
 
-    out_table = gr.Dataframe(headers=["Metric", "Score", "Label", "Notes"], interactive=False)
+    out_table = gr.Dataframe(interactive=False, label="Results")
 
     bars = gr.BarPlot(
         x="Metric", y="Score",
         y_lim=(0, 5),
-        tooltip=["Score"],
-        width=0.6,
+        label="Scores (0–5, higher = better)",
         interactive=False,
-        label="Scores (0–5, higher = better)"
     )
 
-    def _bars_to_df(table_dict, bars_dict):
-        import pandas as pd
-        df = pd.DataFrame({"Metric": list(bars_dict.keys()), "Score": list(bars_dict.values())})
-        return table_dict, df
-
-    btn.click(fn=predict_nisqa, inputs=audio, outputs=[out_table, bars], postprocess=False)\
-       .then(fn=_bars_to_df, inputs=[out_table, bars], outputs=[out_table, bars])
+    btn.click(fn=predict_nisqa, inputs=audio, outputs=[out_table, bars])
 
 if __name__ == "__main__":
     demo.launch()