Update app.py

app.py

@@ -7,14 +7,6 @@ import os
 import sys
 os.system('python -m pip install --upgrade pip')
 os.system('pip install -U scikit-learn scipy matplotlib')
-#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
 os.system("pip install git+https://github.com/openai/whisper.git")
 import whisper
 # os.system("pip install numpy==1.20.0")
@@ -28,37 +20,3 @@ 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()