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import os
from inference import Evaluator
import argparse
from utils.YParams import YParams
import torch
import gradio as gr
def read_markdown_file(path):
with open(path, 'r', encoding='utf-8') as file:
return file.read()
if __name__ == '__main__':
#parse args
parser = argparse.ArgumentParser()
parser.add_argument("--yaml_config", default='config.yaml', type=str)
parser.add_argument("--config", default='resnet_0.7', type=str)
args = parser.parse_args()
params = YParams(os.path.abspath(args.yaml_config), args.config)
#GPU stuff
try:
params.device = torch.device(torch.cuda.current_device())
except:
params.device = "cpu"
#checkpoint stuff
expDir = "ckpts/resnet_0.7/150classes_alldata_cliplength30"
params['checkpoint_path'] = os.path.join(expDir, 'training_checkpoints/ckpt.tar')
params['best_checkpoint_path'] = os.path.join(expDir, 'training_checkpoints/best_ckpt.tar')
evaluator = Evaluator(params)
with gr.Blocks() as demo:
with gr.Tab("Classifier"):
gr.Interface(
title="Carnatic Raga Classifier",
description="**Welcome!** This app uses AI to recognize Carnatic ragas. Upload or record an audio clip to test it out. <br> Provide at least **30 seconds** of audio for best results (**the more audio you provide, the higher the accuracy**). <br> Wait for the audio waves to appear and remain before clicking Submit.",
article = "**Get in Touch:** Feel free to reach out to [me](https://sanjeevraja.com/) via email (sanjeevr AT berkeley DOT edu) with any questions or feedback, or start a discussion in the Community tab! ",
inputs=[
gr.Slider(minimum = 1, maximum = 150, value = 5, label = "Number of displayed ragas", info = "Choose number of top predictions to display"),
gr.Audio()
],
fn=evaluator.inference,
outputs="label",
allow_flagging = False
)
with gr.Tab("About"):
gr.Markdown(read_markdown_file('about.md'))
gr.Image('site/tsne.jpeg', height = 800, width=800)
demo.launch()
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