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base_app_latest.py
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"""
Simple Streamlit webserver application for serving developed classification
models.
Author: Explore Data Science Academy.
Note:
---------------------------------------------------------------------
Please follow the instructions provided within the README.md file
located within this directory for guidance on how to use this script
correctly.
---------------------------------------------------------------------
Description: This file is used to launch a minimal streamlit web
application. You are expected to extend the functionality of this script
as part of your predict project.
For further help with the Streamlit framework, see:
https://docs.streamlit.io/en/latest/
"""
# Streamlit dependencies
import streamlit as st
import joblib,os
# Data dependencies
import pandas as pd
#import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
# Vectorizer
news_vectorizer = open("resources/tfidfvect.pkl","rb")
tweet_cv = joblib.load(news_vectorizer) # loading your vectorizer from the pkl file
# Load your raw data
raw = pd.read_csv("resources/train.csv")
# defining a data Processing and cleaning function
# The main function where we will build the actual app
def main():
"""Tweet Classifier App with Streamlit """
# Creates a main title and subheader on your page -
# these are static across all pages
#st.title("Tweet Classifer")
#image= Image.open('resources/imgs/global_warming_img.jpg')
# Creating sidebar with selection box -
# you can create multiple pages this way
options = ["Background","Prediction","Data visualization", "Information","Project Team"]
selection = st.sidebar.radio("Choose page:",options )
#selection = st.sidebar.selectbox("Choose Option", options)
# Builing out the "Background" page
if selection == "Background":
st.info('This works');
st.write('Explorers !!!')
image= Image.open('resources/imgs/global_warming_img.jpg')
st.image(image, caption='https://www.azocleantech.com/article.aspx?ArticleID=898', use_column_width=True)
if selection == "Project Team" :
#First row of pictures
col1, col2,col3 = st.beta_columns(3)
Ric_Pic =Image.open('resources/imgs/Rickie_pic.png')
col1.image(Ric_Pic,caption="Rickie Mogale Mohale", width=150)
Cot_Pic =Image.open('resources/imgs/courtney_pic.png')
col2.image(Cot_Pic,caption="Courtney Murugan", width=150)
Cot_Pic =Image.open('resources/imgs/jacques_pic.png')
col3.image(Cot_Pic,caption="Jacques Stander", width=150)
#Second row of pictures
col4, col5,col6 = st.beta_columns(3)
vas_Pic =Image.open('resources/imgs/Rickie_pic.png')
col4.image(Ric_Pic,caption="Veshen Naidoo", width=150)
Phiw_Pic =Image.open('resources/imgs/Rickie_pic.png')
col5.image(Phiw_Pic,caption="Phiweka Mthini", width=150)
nor_Pic =Image.open('resources/imgs/Rickie_pic.png')
col6.image(nor_Pic,caption="Nourhan ALfalous", width=150)
#Third row of picture
col7, col8,col9 = st.beta_columns(3)
zin_Pic =Image.open('resources/imgs/zintle_pic.png')
col8.image(zin_Pic,caption='Zintle Faltein-Maqubela', width=150)
col8.header("Role : Team Supervisor")
# Building out the "Information" page
if selection == "Information":
st.info("General Information");
# You can read a markdown file from supporting resources folder
st.markdown("Some information here");
st.subheader("Raw Twitter data and label");
if st.checkbox('Show raw data'): # data is hidden if box is unchecked
st.write(raw[['sentiment', 'message']]) # will write the df to the page
# Building out the predication page
if selection == "Prediction":
st.markdown("")
st.info('This page will help you predict an individual\'s position on global warming base on their tweet')
st.subheader('To make predictions, please follow the three steps below')
#selecting input text
text_type_selection = ['Single tweet','multiple tweets']
text_selection = st.selectbox('Step 1 ) : Text input', text_type_selection)
#C
def get_keys(val,my_dict):
for key,value in my_dict.items():
if val == value:
return key
#C
# User selecting prediction model
#Models = ["Logistic regression","Decision tree","Random Forest Classifier","Naive Bayes","XGboost","Linear SVC"]
#selected_model =st.selectbox("Step 3 ) : Choose prediction model ",Models )
if text_selection== 'Single tweet':
### SINGLE TWEET CLASSIFICATION ###
# Creating a text box for user input
input_text = st.text_area("Step 2 ) : Enter Your Single Text Below :") ##user entering a single text to classify and predict
Models = ["Logistic regression","Decision tree","Random Forest","Naive Bayes","XGboost","Linear SVC" ]
selected_model = st.selectbox("Step 3 ) : Choose prediction model ",Models)
def load_prediction_models(model_file):
loaded_models = joblib.load(open(os.path.join(model_file),"rb"))
return loaded_models
prediction_labels = {'Negative':-1,'Neutral':0,'Positive':1}
if st.button("Classify"):
## showing the user original text
st.text("Input tweet is :\n{}".format(input_text))
## Calling a function to process the text
#tweet_text = cleaner(input_text)
# Transforming user input with vectorizer
vect_text = tweet_cv.transform([input_text]).toarray()
# Load your .pkl file with the model of your choice + make predictions
# Try loading in multiple models to give the user a choice
predictor = joblib.load(open(os.path.join("resources/Logistic_regression.pkl"),"rb"))
prediction = predictor.predict(vect_text)
#M Model_ Selection
if selected_model == "Logistic regression":
predictor = load_prediction_models("resources/Logistic_regression.pkl")
prediction = predictor.predict(vect_text)
# st.write(prediction)
elif selected_model == "Decision tree":
predictor = load_prediction_models("resources/Logistic_regression.pkl")
prediction = predictor.predict(vect_text)
# st.write(prediction)
elif selected_model == "Random Forest Classifier":
predictor = load_prediction_models("resources/Logistic_regression.pkl")
prediction = predictor.predict(vect_text)
# st.write(prediction)
elif selected_model == "Naive Bayes":
predictor = load_prediction_models("resources/Logistic_regression.pkl")
prediction = predictor.predict(vect_text)
elif selected_model =="XGboost" :
predictor = load_prediction_models("resources/Logistic_regression.pkl")
prediction = predictor.predict(vect_text)
elif selected_model == "Linear SVC" :
predictor = load_prediction_models("resources/Logistic_regression.pkl")
prediction = predictor.predict(vect_text)
# st.write(prediction)
# When model has successfully run, will print prediction
# You can use a dictionary or similar structure to make this output
# more human interpretable.
# st.write(prediction)
final_result = get_keys(prediction,prediction_labels)
st.success("Tweet Categorized as : {}".format(final_result))
# Required to let Streamlit instantiate our web app.
if __name__ == '__main__':
main()