Last active
July 10, 2019 14:52
-
-
Save Saminu/f93a55e1fa4d00c3e2dd7be5ef0aee8c to your computer and use it in GitHub Desktop.
app.py - Visualizing Uncertainty in Models using Pandas, Scikit Learn, Jinja, Flask, and D3.js
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| from flask import Flask, render_template, url_for, request | |
| import pandas as pd | |
| import pickle | |
| from sklearn.feature_extraction.text import CountVectorizer | |
| from sklearn.naive_bayes import MultinomialNB | |
| from sklearn.metrics import classification_report | |
| from sklearn.externals import joblib | |
| app = Flask(__name__) | |
| @app.route('/') | |
| def home(): | |
| return render_template('index.html') | |
| @app.route('/predict', methods=['POST']) | |
| def predict(): | |
| df = pd.read_csv("data/ieee.csv", encoding="latin-1") | |
| df = df[['Conference','Abstract']] | |
| df.drop(df[df.isnull().any(axis=1)].index,inplace=True) | |
| # Features and Labels | |
| df['label'] = df['Conference'].map({'InfoVis': 0, 'SciVis': 1, 'VAST':2, 'Vis':3}) | |
| X = df['Abstract'] | |
| y = df['label'] | |
| # Extract Feature With CountVectorizer | |
| cv = CountVectorizer() | |
| X = cv.fit_transform(X) | |
| # Fit the Data | |
| from sklearn.model_selection import train_test_split | |
| X_train, X_test, y_train, y_test = train_test_split( | |
| X, y, test_size=0.33, random_state=42) | |
| # Naive Bayes Classifier | |
| from sklearn.naive_bayes import MultinomialNB | |
| clf = MultinomialNB() | |
| clf.fit(X_train, y_train) | |
| clf.score(X_test, y_test) | |
| # Alternative Usage of Saved Model | |
| # joblib.dump(clf, 'NB_spam_model.pkl') | |
| # NB_spam_model = open('NB_spam_model.pkl','rb') | |
| # clf = joblib.load(NB_spam_model) | |
| if request.method == 'POST': | |
| message = request.form['message'] | |
| data = [message] | |
| vect = cv.transform(data).toarray() | |
| y_pred = clf.predict(X_test) | |
| my_prediction = clf.predict(vect) | |
| my_report = classification_report(y_test, y_pred, output_dict=True) | |
| # print(my_report) | |
| return render_template('prediction.html',prediction = my_prediction, report = my_report) | |
| if __name__ == '__main__': | |
| app.run(debug=True) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment