add app.py with gradio

app.py

@@ -0,0 +1,103 @@
+import gradio as gr
+from test import predict_uri
+import sys
+import warnings
+from fastapi import FastAPI
+
+# ignore UserWarning
+warnings.simplefilter("ignore", UserWarning)
+
+# examples = [
+#     ['res/miaow_16k.wav'],
+#     ['res/snore/pro_snore 6bee45643b45af9b_a7a3bbe6ba79af5b25b19ad10a8d9421d0d5679b.wav'],
+#     ['res/snore/Snoring vs Sleep Apnea  - What the difference sounds like.mp4']
+# ]
+title = "yamnet test"
+description = "An audio event classifier trained on the AudioSet dataset to predict audio events from the AudioSet ontology."
+
+# # https://github.com/gradio-app/gradio/issues/2362
+# class Logger:
+#     def __init__(self, filename):
+#         self.terminal = sys.stdout
+#         self.log = open(filename, "w")
+#
+#     def write(self, message):
+#         self.terminal.write(message)
+#         self.log.write(message)
+#
+#     def flush(self):
+#         self.terminal.flush()
+#         self.log.flush()
+#
+#     def isatty(self):
+#         return False
+#
+#
+# sys.stdout = Logger("output.log")
+#
+#
+# def test(x):
+#     print("This is a test")
+#     print(f"Your function is running with input {x}...")
+#     return x
+#
+#
+# def read_logs():
+#     sys.stdout.flush()
+#     with open("output.log", "r") as f:
+#         return f.read()
+#
+#
+# with gr.Interface(predict_uri, inputs=gr.inputs.Audio(type="filepath"), outputs=["text", 'plot']) as demo:
+#     examples = examples,
+#     title = title,
+#     description = description,
+#     allow_flagging = 'never'
+#
+#     logs = gr.Textbox()
+#     demo.load(read_logs, None, logs, every=1)
+#
+# demo.launch(enable_queue=True, show_error=True)
+
+
+# with gr.Blocks() as demo:
+#     with gr.Row():
+#         inputs = gr.inputs.Audio(type="filepath")
+#         outputs = ["text", 'plot']
+#     btn = gr.Button("Run")
+#     btn.click(predict_uri, inputs, outputs)
+#
+#     logs = gr.Textbox()
+#     demo.load(read_logs, None, logs, every=1)
+#
+# demo.queue().launch()
+
+
+demo = gr.Interface(
+    predict_uri,
+    inputs=[
+        gr.inputs.Audio(type="filepath"),
+        gr.inputs.Audio(source="microphone", type="filepath"),
+        gr.Slider(minimum=7, maximum=21, step=1)
+    ],
+    outputs=['image', 'image', 'image', 'text', 'text', 'text', 'text'],
+    # examples=examples,
+    title=title,
+    description=description,
+    allow_flagging='never'
+)
+demo.launch(enable_queue=True, show_error=True, share=False)
+
+# # FastAPI
+# CUSTOM_PATH = "/gradio"
+#
+# app = FastAPI()
+#
+#
+# @app.get("/")
+# def read_main():
+#     return {"message": "This is your main app"}
+#
+#
+# io = gr.Interface(lambda x: "Hello, " + x + "!", "textbox", "textbox")
+# app = gr.mount_gradio_app(app, io, path=CUSTOM_PATH)

requirements.txt

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+Pillow
+librosa
+soundfile
+matplotlib==3.7.2
+gradio
+tensorflow
+tensorflow_hub
+numpy
+scipy
+pydub
+seaborn