feat: add download CSV button

.gitignore

@@ -0,0 +1 @@
+data/predictions/

app.py

@@ -34,7 +34,7 @@ def interface_fn(
     model: YOLO,
     audio_filepath: str,
     config_model: dict[str, float | int],
-) -> Tuple[list[Image.Image], pd.DataFrame, str]:
+) -> Tuple[list[Image.Image], pd.DataFrame, str, Path]:
     """
     Main interface function that runs the model on the provided audio_filepath and
     returns the exepected tuple to populate the gradio interface.
@@ -99,6 +99,11 @@ def interface_fn(
         for waveform in waveforms
     ]
 
+    dir_predictions_csv = Path("./data/predictions/csv/")
+    dir_predictions_csv.mkdir(parents=True, exist_ok=True)
+    filepath_predictions_csv = dir_predictions_csv / f"{Path(audio_filepath).stem}.csv"
+    df.to_csv(filepath_predictions_csv, index=False)
+
     spectrograms_pil_images = [Image.fromarray(a) for a in spectrograms_array_images]
 
     predictions = model.predict(spectrograms_pil_images)
@@ -106,7 +111,12 @@ def interface_fn(
         Image.fromarray(bgr_to_rgb(p.plot())) for p in predictions
     ]
 
-    return (pil_image_spectrogram_with_predictions, df[CSV_COLUMNS], prediction_to_str(df=df))
+    return (
+        pil_image_spectrogram_with_predictions,
+        df[CSV_COLUMNS],
+        prediction_to_str(df=df),
+        filepath_predictions_csv,
+    )
 
 
 def examples(dir_examples: Path) -> list[Path]:
@@ -150,6 +160,7 @@ with gr.Blocks() as demo:
         headers=CSV_COLUMNS,
         label="prediction as CSV",
     )
+    output_download_button = gr.DownloadButton(label="Download CSV")
 
     fn = lambda audio_filepath: interface_fn(
         model=model,
@@ -160,7 +171,7 @@ with gr.Blocks() as demo:
         title="ML model for forest elephant rumble detection 🐘",
         fn=fn,
         inputs=input,
-        outputs=[output_gallery, output_dataframe, output_raw],
+        outputs=[output_gallery, output_dataframe, output_raw, output_download_button],
         examples=sound_filepaths,
         flagging_mode="never",
     )