updated web app

base_app.py

@@ -21,47 +21,109 @@
 	https://docs.streamlit.io/en/latest/
 
 """
+# import images
+from PIL import Image
+
 # Streamlit dependencies
 import streamlit as st
 import joblib,os
 
 # Data dependencies
 import pandas as pd
 
-# Vectorizer
-news_vectorizer = open("resources/tfidfvect.pkl","rb")
+# Vectorizer 1
+news_vectorizer = open("resources/tf_vect.pkl","rb")
 tweet_cv = joblib.load(news_vectorizer) # loading your vectorizer from the pkl file
 
+# Vectorizer 2
+#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")
+raw = pd.read_csv("resources/train2.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.title("Climate Change Belief Analysis")
 	st.subheader("Climate change tweet classification")
 
 	# Creating sidebar with selection box -
 	# you can create multiple pages this way
-	options = ["Prediction", "Information"]
+	options = ["About Us", "Classification 1", "Classification 2", "Information"]
 	selection = st.sidebar.selectbox("Choose Option", options)
 
+	# Building out the "Home" page
+	if selection == "About Us":
+		image = Image.open('climate_UN.jpeg')
+		st.image(image, caption='Global warming')
+
+		st.info("Team Information - Experts and their Roles")
+		# You can read a markdown file from supporting resources folder
+		st.markdown("This Web app has been adapted and developed by the J.GAD AI - a group of       \
+			five students from the July 2022 cohort of the Explore AI Academy Data Science course.")
+
+		st.subheader("Meet the Team")
+		if st.button('Aniedi'): # information is hidden if button is clicked
+			st.markdown('Aniedi Oboho-Etuk is a J.GAD AI Developer')
+		if st.button('David'): # information is hidden if button is clicked
+			st.markdown('David Mugambi is the J.GAD AI Project Manager')
+		if st.button('Gavriel'): # information is hidden if button is clicked
+			st.markdown('Gavriel Leibovitz is a J.GAD Developer/Strategist')
+		if st.button('Josiah'): # information is hidden if button is clicked
+			st.markdown('Josiah Aramide is the J.GAD AI CEO')
+		if st.button('Joy'): # information is hidden if button is clicked
+			st.markdown('Joy Obukohwo is the J.GAD AI Product Owner')
+
 	# Building out the "Information" page
 	if selection == "Information":
-		st.info("General Information")
+		st.info("Project Overview")
 		# You can read a markdown file from supporting resources folder
-		st.markdown("Some information here")
+		st.markdown("Many companies are built around lessening one\’s environmental impact or carbon footprint. \
+		They offer products and services that are environmentally friendly and sustainable,             \
+		in line with their values and ideals. They would like to determine how people perceive           \
+		climate change and whether or not they believe it is a real threat. This would add to their       \
+		market research efforts in gauging how their product/service may be received.                     \n \
+		Providing an accurate and robust solution to this task gives companies access to a                \
+		broad base of consumer sentiment, spanning multiple demographic and geographic categories          \
+		- thus increasing their insights and informing future marketing strategies.")
 
 		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.info("Prediction with ML Models")
+	# Building out the prediction 1 page
+	if selection == "Classification 1":
+		st.info("Classification with Base ML Model")
+		# Creating a text box for user input
+		tweet_text = st.text_area("Enter Text","Type Here")
+
+		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/lgr_base.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
+			# more human interpretable.
+			if prediction == 0:
+				st.success(f"Text Categorized as: Neutral or Class {prediction}")
+			if prediction == 1:
+				st.success(f"Text Categorized as: Pro-climate change or Class {prediction}")
+			if prediction == 2:
+				st.success(f"Text Categorized as: News or Class {prediction}")
+			if prediction == -1:
+				st.success(f"Text Categorized as: Anti-climate change or Class {prediction}")
+
+	# Building out the prediction 2 page
+	if selection == "Classification 2":
+		st.info("Classification with Improved ML Model")
 		# Creating a text box for user input
 		tweet_text = st.text_area("Enter Text","Type Here")
 
@@ -70,13 +132,17 @@ def main():
 			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/lgr_bal.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
 			# more human interpretable.
-			st.success("Text Categorized as: {}".format(prediction))
+			dict_pred = {-1: 'Anti-climate change', 0: 'Neutral', 1: 'Pro-climate change', 2: 'News'}
+			for key in dict_pred.keys():
+				if prediction == key:
+					st.success(f"Text Categorized as: {dict_pred[key]}")
+				#st.success("Text Categorized as: {}".format(prediction))
 
 # Required to let Streamlit instantiate our web app.  
 if __name__ == '__main__':