Update base_app.py

base_app.py

@@ -22,61 +22,152 @@
 
 """
 # Streamlit dependencies
+
 import streamlit as st
-import joblib,os
+import joblib
+import os
+import pickle as pkl
+import numpy as np
+from PIL import Image
+from sklearn.metrics import mean_absolute_error
+
 
 # Data dependencies
 import pandas as pd
 
+# Load your raw data
+raw = pd.read_csv("resources/train.csv")
+
+
 # Vectorizer
-news_vectorizer = open("resources/tfidfvect.pkl","rb")
-tweet_cv = joblib.load(news_vectorizer) # loading your vectorizer from the pkl file
+news_vectorizer = open("resources/vectorizer.pkl", "rb")
+# loading your vectorizer from the pkl file
+tweet_cv = joblib.load(news_vectorizer)
 
 # Load your raw data
 raw = pd.read_csv("resources/train.csv")
 
 # 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")
-	st.subheader("Climate change tweet classification")
+	#  get random tweet sample
 
 	# Creating sidebar with selection box -
 	# you can create multiple pages this way
-	options = ["Prediction", "Information"]
+	options = ["Prediction", "Data Insights",
+	    'About the project', "Information", 'The team']
 	selection = st.sidebar.selectbox("Choose Option", options)
 
+	# Information on the project
+	if selection == "About the project":
+		st.title("Climate change tweet classification")
+		st.write("Climate action, is the goal 13 of the sustainable development goals. It calls for urgent actions to combact climate change and its impact. To address climate change, countries adopt the paris agreement to limit global temperature rise to well below 2 degree celsius. People are experiencing the significant impacts of climate change, which include changing weather patterns, rising sea level, and more extreme weather events. The greenhouse gas emissions from human activities are driving climate change and continue to rise. ")
+		image = Image.open('climate action.jpeg')
+		st.image(image, caption='SDG Goal 13: Climate action')
+		st.write("South Africa’s National Climate Change Response Policy (NCCRP) (DEA 2011) commits the Department of Environmental Affairs (DEA) in Section 12 to publish annual progress reports on monitoring climate change responses.The South African government has also pledged to continue contributing positively to adresssing the climate emergency and is planning on long term efforts to  change the attitude of people towards climate change. But in order to do that, there is a need to know  what people's opinion are regarding climate change")
+		st.write("In this project, various machine learning models were utilised to predict people's sentiment regarding climate change. The machines were trained using messages and known sentiments from twitter. Through that, we can predict,  what sentiment an individaul has, based on their tweet. This would enable  the govermnent ascertain people's current opionion regarding climate change and how much effort is required to positively influence that")
+
+	# Exploratory data analysis
+	if selection == "Data Insights":
+		st.title("Exploratory Data Analysis")
+		st.write('In this section, we will be discussing insights from the Tweet classification dataset')
+		st.subheader('Tweet distribution')
+		st.write('From the chart below, It is obvious that pro-climate change tweets, account for more than half of the overall number of  tweets. This is more than the combine  tweets from news,neutral and anti- climate change.')
+		image = Image.open('tweet.png')
+		st.image(image, caption='Tweet distribution')
+		st.subheader('Climate change buzzwords')
+		st.write("The word cloud below  displays the 25 most popular words found in the tweets for each classes.The top buzzwords accross all classes are climate, change, global and retweet. The frequency of retweet means that many opinions are being shared and viewed by large audiences. This is true for all four classes.'Trump' is a also a frequently occuring word in all four classes. This is unsurprising given his controversial view on the topic. Words like denier, believe, think, fight, etc. occur frequently in pro climate change tweets. In contrast, anti climate change tweets contain words such as 'hoax', 'scam', 'tax' and 'liberal'. There is a stark difference in tone and use of emotive language in these two classes of tweets. From this data we could deduce that people who are anti climate change believe that global warming is a 'hoax' and feel negatively towards a tax–based approach to slowing global climate change. Words like 'science' and 'scientist' occur frequently as well which could imply that people are tweeting about scientific studies that support their views on climate change.")
+		image = Image.open('word_cloud.png')
+		st.image(image, caption='Word cloud showing most popular buzz words')
+
+		st.subheader('Hashtag analysis')
+		st.write('People use the hashtag symbol (#) before a relevant keyword or phrase in their Tweet to categorize those Tweets and help them show more easily in Twitter search. Clicking or tapping on a hashtagged word in any message shows you other Tweets that include that hashtag. Hashtags can be included anywhere in a Tweet. Hashtagged words that become very popular are often trending topics.')
+		image = Image.open('hashtag analysis.png')
+		st.image(image, caption='Hashtag analysis of Tweets')
+
+
+	if selection == "The team":
+		st.title("About Dynamic Data Developers ")
+
+		st.write("* Data Dynamic Developers is a company founded in 2020.")
+		st.write("*  Our team is made up of highly qualified and reputable individuals in the field of data science. ")
+
+
+		st.subheader("Meet the team")
+		image = Image.open('The team.png')
+		st.image(image, caption='Data Dynamic Developers')
+
 	# 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.markdown("")
+
+
 		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
+		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.title("Dynamic Data Developers Inc")
+		st.subheader("Climate change tweet classification")
+		image = Image.open('Twitter.png')
+		st.image(image)
 		st.info("Prediction with ML Models")
+
+
+
 		# Creating a text box for user input
 		tweet_text = st.text_area("Enter Text","Type Here")
+		option = st.selectbox(
+		'Select a model to use',
+		#['Logistic Regression'])
+		['Linear SVC','Naive Bayes', 'Decision Tree', 'Logistic Regression', 'Random Forest'])
 
 		if st.button("Classify"):
-			# Transforming user input with vectorizer
+
+
 			vect_text = tweet_cv.transform([tweet_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"))
+
+			predictor = joblib.load(open(os.path.join("resources/model.pkl"),"rb"))
 			prediction = predictor.predict(vect_text)
+
+			#elif(option == "Naive Bayes"):
+				#predictor = joblib.load(open(os.path.join("resources/NaiveB.pkl"),"rb"))
+				#prediction = predictor.predict(vect_text)
+
+			#elif(option == "Decision Tree"):
+				#predictor = joblib.load(open(os.path.join("resources/dt.pkl"),"rb"))
+				#prediction = predictor.predict(vect_text)
+
+			#elif(option == "Linear SVC"):
+				#predictor = joblib.load(open(os.path.join("resources/lsvc.pkl"),"rb"))
+				#prediction = predictor.predict(vect_text)
+
+			#elif(option == "Random Forest"):
+				#predictor = joblib.load(open(os.path.join("resources/rf.pkl"),"rb"))
+				#prediction = predictor.predict(vect_text)
 
 			# When model has successfully run, will print prediction
 			# You can use a dictionary or similar structure to make this output
+			# select option to choose 
+			output_text = {
+			'0': 'Neutral', 
+			'-1': 'Anti climate change', 
+			'1': 'Pro Climate change', 
+			'2': 'News'
+			}
 			# more human interpretable.
-			st.success("Text Categorized as: {}".format(prediction))
+			st.success("Tweet categorised by {} model as : {}".format(option, output_text[str(prediction[0])]))
 
 # Required to let Streamlit instantiate our web app.  
 if __name__ == '__main__':