fix app"

app.py

@@ -1,141 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[3]:
-
-
-import gradio
-
-from fastai.vision.all import *
-from fastai.data.all import *
-from pathlib import Path
-import pandas as pd
-from matplotlib.pyplot import specgram
-import librosa
-import librosa.display
-from huggingface_hub import hf_hub_download
-from fastai.learner import load_learner
-
-
-# In[10]:
-
-
-ref_file = hf_hub_download("gputrain/UrbanSound8K", "UrbanSound8K.csv")
-
-model_file = hf_hub_download(
-    hf_hub_download("gputrain/UrbanSound8K", "export.pkl")
-)
-
-
-# In[3]:
-
-
-# Local test
-# ref_file = "UrbanSound8K.csv"
-# model_file = "export.pkl"
-
-
-# In[4]:
-
-
-df = pd.read_csv(ref_file) 
-df['fname'] = df[['slice_file_name','fold']].apply (lambda x: str(x['slice_file_name'][:-4])+'.png'.strip(),axis=1 )
-my_dict = dict(zip(df.fname,df['class']))
-def label_func(f_name):
-    f_name = str(f_name).split('/')[-1:][0]
-    return my_dict[f_name]
-model = load_learner (model_file)
-labels = model.dls.vocab
-
-
-# In[5]:
-
-
-with open("article.md") as f:
-    article = f.read()
-
-
-# In[6]:
-
-
-interface_options = {
-    "title": "Urban Sound 8K Classification",
-    "description": "A Fast AI example with ResNet34 image classification of a sound wav file transformed to a Mel Spectrogram ",
-    #"article": article,
-    "interpretation": "default",
-    "layout": "horizontal",
-    # Audio from validation file
-    "examples": ["dog_bark.wav", "children_playing.wav", "air_conditioner.wav", "street_music.wav", "engine_idling.wav",
-                "jackhammer.wav", "drilling.wav", "siren.wav","car_horn.wav","gun_shot.wav"],
-    "allow_flagging": "never"
-}
-
-
-# In[7]:
-
-
-def convert_sounds_melspectogram (audio_file):
-
-    samples, sample_rate = librosa.load(audio_file)  #create onces with librosa
-
-    fig = plt.figure(figsize=[0.72,0.72])
-    ax = fig.add_subplot(111)
-    ax.axes.get_xaxis().set_visible(False)
-    ax.axes.get_yaxis().set_visible(False)
-    ax.set_frame_on(False)
-    melS = librosa.feature.melspectrogram(y=samples, sr=sample_rate)
-    librosa.display.specshow(librosa.power_to_db(melS, ref=np.max))
-    filename  = 'temp.png'
-    plt.savefig(filename, dpi=400, bbox_inches='tight',pad_inches=0)
-    plt.close('all')
-    
-    return None
-
-
-# In[8]:
-
-
-def predict():
-    img = PILImage.create('temp.png')
-    pred,pred_idx,probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
-    return labels_probs
-
-
-# In[9]:
-
-
-def end2endpipeline(filename):
-    create_image(filename)
-    return predict("temp.png")
-
-
-# In[10]:
-
-
-demo = gradio.Interface(
-    fn=end2endpipeline,
-    inputs=gradio.inputs.Audio(source="upload", type="filepath"),
-    outputs=gradio.outputs.Label(num_top_classes=10),
-    **interface_options,
-)
-
-
-# In[ ]:
-
-
-launch_options = {
-    "enable_queue": True,
-    "share": False,
-    # thanks Alex for pointing this option to cache examples
-    "cache_examples": True,
-}
-
-demo.launch(**launch_options)
-
-
-# In[ ]:
-
-
-
-