update to support gradio_client

app.py

@@ -1,9 +1,13 @@
+import json
+import os
+from pathlib import Path
+
 import gradio as gr
-import tensorflow as tf
 import numpy as np
-import os
 import obspy
 import pandas as pd
+import tensorflow as tf
+
 from phasenet.model import ModelConfig, UNet
 from phasenet.postprocess import extract_picks
 
@@ -43,11 +47,17 @@ def reshape_input(vec):
 
 
 # inference fn
-def predict(inputs):
-
+def predict(mseeds, waveforms, stations):
+    if len(stations) > 0:
+        stations = json.loads(stations)
+        print(f"{len(stations)}: {stations = }")
+    if len(waveforms) > 0:
+        waveforms = json.loads(waveforms)
+        waveforms = np.array(waveforms)
+        print(f"{waveforms.shape = }")
     picks = []
-    for input in inputs:
-        file = input.name  # if not isinstance(input, str) else input
+    for mseed in mseeds:
+        file = mseed.name  # if not isinstance(input, str) else input
         mseed = obspy.read(file)
         begin_time = min([tr.stats.starttime for tr in mseed])
         end_time = max([tr.stats.endtime for tr in mseed])
@@ -73,16 +83,19 @@ def predict(inputs):
 
         picks.extend(tmp_picks)
 
-    picks = pd.DataFrame(picks)
-    picks.to_csv("picks.csv", index=False)
+    picks_df = pd.DataFrame(picks)
+    picks_df.to_csv("picks.csv", index=False)
+
+    return picks_df, "picks.csv", json.dumps(picks)
 
-    return picks, "picks.csv"
 
+inputs = [
+    gr.File(label="mseed file", file_count="multiple"),
+    gr.Textbox(label="waveform", visible=False),
+    gr.Textbox(label="stations", visible=False),
+]
 
-# gradio components
-inputs = gr.File(file_count="multiple")
-# outputs = gr.outputs.JSON()
-outputs = [gr.Dataframe(headers=["phase_time", "phase_score", "phase_type"]), gr.File()]
+outputs = [gr.Dataframe(headers=["phase_time", "phase_score", "phase_type"]), gr.File(), gr.Textbox(visible=False)]
 gr.Interface(
     predict,
     inputs=inputs,
@@ -91,7 +104,7 @@ gr.Interface(
     description="PhaseNet",
     examples=[[[os.path.join(os.path.dirname(__file__), "tests/test.mseed")]]],
     allow_flagging="never",
-).launch()
+).queue().launch()
 
 # if __name__ == "__main__":
 #    picks = predict(["tests/test.mseed"])