Updated gradio app to new api

app.py

@@ -3,21 +3,10 @@ import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 
-import batdetect2.utils.audio_utils as au
+from batdetect2 import api, plot
-import batdetect2.utils.detector_utils as du
-import batdetect2.utils.plot_utils as viz
-
-# setup the arguments
-args = {}
-args = du.get_default_run_config()
-args["detection_threshold"] = 0.3
-args["time_expansion_factor"] = 1
-args["model_path"] = "models/Net2DFast_UK_same.pth.tar"
-max_duration = 2.0
-
-# load the model
-model, params = du.load_model(args["model_path"])
 
+MAX_DURATION = 2
+DETECTION_THRESHOLD = 0.3
 
 df = gr.Dataframe(
     headers=["species", "time", "detection_prob", "species_prob"],
@@ -28,97 +17,71 @@ df = gr.Dataframe(
 )
 
 examples = [
-    ["example_data/audio/20170701_213954-MYOMYS-LR_0_0.5.wav", 0.3],
+    [
-    ["example_data/audio/20180530_213516-EPTSER-LR_0_0.5.wav", 0.3],
+        "example_data/audio/20170701_213954-MYOMYS-LR_0_0.5.wav",
-    ["example_data/audio/20180627_215323-RHIFER-LR_0_0.5.wav", 0.3],
+        DETECTION_THRESHOLD,
+    ],
+    [
+        "example_data/audio/20180530_213516-EPTSER-LR_0_0.5.wav",
+        DETECTION_THRESHOLD,
+    ],
+    [
+        "example_data/audio/20180627_215323-RHIFER-LR_0_0.5.wav",
+        DETECTION_THRESHOLD,
+    ],
 ]
 
 
-def make_prediction(file_name=None, detection_threshold=0.3):
+def make_prediction(file_name, detection_threshold=DETECTION_THRESHOLD):
-    if file_name is not None:
+    # configure the model run
-        audio_file = file_name
+    run_config = api.get_config(
-    else:
+        detection_threshold=detection_threshold,
-        return "You must provide an input audio file."
+        max_duration=MAX_DURATION,
-
-    if detection_threshold is not None and detection_threshold != "":
-        args["detection_threshold"] = float(detection_threshold)
-
-    run_config = {
-        **params,
-        **args,
-        "max_duration": max_duration,
-    }
-
-    # process the file to generate predictions
-    results = du.process_file(
-        audio_file,
-        model,
-        run_config,
     )
 
-    anns = [ann for ann in results["pred_dict"]["annotation"]]
+    # process the file to generate predictions
-    clss = [aa["class"] for aa in anns]
+    results = api.process_file(file_name, config=run_config)
-    st_time = [aa["start_time"] for aa in anns]
-    cls_prob = [aa["class_prob"] for aa in anns]
-    det_prob = [aa["det_prob"] for aa in anns]
-    data = {
-        "species": clss,
-        "time": st_time,
-        "detection_prob": det_prob,
-        "species_prob": cls_prob,
-    }
 
-    df = pd.DataFrame(data=data)
+    # extract the detections
-    im = generate_results_image(audio_file, anns)
+    detections = results["pred_dict"]["annotation"]
+
+    # create a dataframe of the predictions
+    df = pd.DataFrame(
+        [
+            {
+                "species": pred["class"],
+                "time": pred["start_time"],
+                "detection_prob": pred["class_prob"],
+                "species_prob": pred["class_prob"],
+            }
+            for pred in detections
+        ]
+    )
+    im = generate_results_image(file_name, detections, run_config)
 
     return [df, im]
 
 
-def generate_results_image(audio_file, anns):
+def generate_results_image(file_name, detections, config):
-
+    audio = api.load_audio(
-    # load audio
+        file_name,
-    sampling_rate, audio = au.load_audio(
+        max_duration=config["max_duration"],
-        audio_file,
+        time_exp_fact=config["time_expansion"],
-        args["time_expansion_factor"],
+        target_samp_rate=config["target_samp_rate"],
-        params["target_samp_rate"],
-        params["scale_raw_audio"],
-        max_duration=max_duration,
     )
-    duration = audio.shape[0] / sampling_rate
 
-    # generate spec
+    spec = api.generate_spectrogram(audio, config=config)
-    spec, spec_viz = au.generate_spectrogram(
-        audio, sampling_rate, params, True, False
-    )
 
     # create fig
     plt.close("all")
     fig = plt.figure(
         1,
-        figsize=(spec.shape[1] / 100, spec.shape[0] / 100),
+        figsize=(15, 4),
         dpi=100,
         frameon=False,
     )
-    spec_duration = au.x_coords_to_time(
+    ax = fig.add_subplot(111)
-        spec.shape[1],
+    plot.spectrogram_with_detections(spec, detections, ax=ax)
-        sampling_rate,
-        params["fft_win_length"],
-        params["fft_overlap"],
-    )
-    viz.create_box_image(
-        spec,
-        fig,
-        anns,
-        0,
-        spec_duration,
-        spec_duration,
-        params,
-        spec.max() * 1.1,
-        False,
-        True,
-    )
-    plt.ylabel("Freq - kHz")
-    plt.xlabel("Time - secs")
     plt.tight_layout()
 
     # convert fig to image
@@ -126,7 +89,6 @@ def generate_results_image(audio_file, anns):
     data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
     w, h = fig.canvas.get_width_height()
     im = data.reshape((int(h), int(w), -1))
-
     return im
 
 
@@ -140,7 +102,7 @@ descr_txt = (
 gr.Interface(
     fn=make_prediction,
     inputs=[
-        gr.Audio(source="upload", type="filepath", optional=True),
+        gr.Audio(source="upload", type="filepath"),
         gr.Dropdown([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]),
     ],
     outputs=[df, gr.Image(label="Visualisation")],