app.py

import transformers
from transformers import pipeline
import gradio as gr
import pandas
import matplotlib.pyplot as plt
import sklearn
#import scikit-learn
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC


whisper_esc50 = pipeline(model="mskov/whisper_esc50")  
whisper_miso= pipeline(model="mskov/whisper_miso")  
whisper_tiny = whisper.load_model("tiny")
whisper_base = whisper.load_model("base")

dataset = load_dataset("mskov/miso_test")

names = ['path', 'file_name', 'category']
dataframe = pandas.read_csv(url, names=names)
array = dataframe.values
X = array[:,0:2]
Y = array[:,2]
# prepare configuration for cross validation test harness
seed = 7
# prepare models
models = [whisper_esc50, whisper_miso, whisper_tiny, whisper_base]
models.append(('LR', LogisticRegression()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC()))
# evaluate each model in turn
results = []
names = []
scoring = 'accuracy'
for name, model in models:
 kfold = model_selection.KFold(n_splits=10, random_state=seed)
 cv_results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)
 results.append(cv_results)
 names.append(name)
 msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
 print(msg)
# boxplot algorithm comparison
fig = plt.figure()
fig.suptitle('Algorithm Comparison')
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()