Code formatting

base_app.py

@@ -22,62 +22,201 @@
 
 """
 # Streamlit dependencies
+# Streamlit dependencies
 import streamlit as st
-import joblib,os
+from streamlit_option_menu import option_menu
+import joblib
+import os
+from PIL import Image
 
 # Data dependencies
 import pandas as pd
+import time
 
 # 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/Linear_SVC_vect.pkl", "rb")
+tweet_cv = joblib.load(news_vectorizer)  # loading your vectorizer from the pkl file
+news_vectorizer_1 = open("resources/Linear_SVC_vect.pkl", "rb")
+tweet_cv_1 = joblib.load(news_vectorizer_1)
 
 # Load your raw data
 raw = pd.read_csv("resources/train.csv")
+raw2 = pd.read_csv("resources/training_data.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")
-
-	# Creating sidebar with selection box -
-	# you can create multiple pages this way
-	options = ["Prediction", "Information"]
-	selection = st.sidebar.selectbox("Choose Option", options)
-
-	# 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.info("Prediction with ML Models")
-		# 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/Logistic_regression.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))
-
-# Required to let Streamlit instantiate our web app.  
-if __name__ == '__main__':
-	main()
+    """Tweet Classifier App with Streamlit"""
+
+    # Creates a main title and subheader on your page -
+    # these are static across all pages
+    st.title("Elites  \n _____________________________________________________")
+    st.subheader("Climate change tweet classification")
+
+    # Creating sidebar using streamlit-option-menu
+    with st.sidebar:
+        selected = option_menu(
+            "Menu",
+            ["Home", "Raw Data", "Predictions", "About Us", "Contact Us"],
+            icons=["house", "table", "graph-up-arrow", "info-circle", "telephone"],
+            menu_icon="menu-button",
+            default_index=0,
+        )
+
+    # Building out the "Home" page
+    if selected == "Home":
+        image = Image.open("resources/imgs/KB.png")
+        st.image(image)
+
+        st.subheader("Tweet Classifier")
+        st.markdown("Consumers gravitate toward companies that are built around lessening one’s environmental impact. Elites provides an accurate and robust solution that 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.markdown("Choose Elites and walk a greener path.")
+
+    # Building out the raw data page
+    if selected == "Raw Data":
+        tab1, tab2 = st.tabs(["Data description", "Data Visualizations"])
+        with tab1:
+            st.markdown(
+                "The collection of the raw data was funded by a Canada Foundation for Innovation JELF Grant to Chris Bauch, University of Waterloo."
+            )
+            st.write(
+                """
+                This dataset aggregates tweets pertaining to climate change collected between Apr 27, 2015 and Feb 21, 2018. In total, 43943 tweets were annotated. Each tweet is labelled independently by 3 reviewers. This dataset only contains tweets that all 3 reviewers agreed on (the rest were discarded). \n
+                Each tweet is labelled as one of the following classes: \n
+                * 2(News): the tweet links to factual news about climate change \n
+                * 1(Pro): the tweet supports the belief of man-made climate change \n
+                * 0(Neutral): the tweet neither supports nor refutes the belief of man-made climate change \n
+                * -1(Anti): the tweet does not believe in man-made climate change
+                """
+            )
+            st.write("")
+
+        with tab2:
+            if st.checkbox("Show sentiment value count"):
+                st.bar_chart(data=raw2["sentiment"].value_counts(), x=None, y=None, width=220, height=320, use_container_width=True)
+
+            if st.checkbox("Show raw data"):
+                job_filter = st.selectbox("Select sentiment", pd.unique(raw['sentiment']))
+
+
+                # creating a single-element container.
+                placeholder = st.empty()
+
+                # dataframe filter 
+
+                df = raw[raw['sentiment']==job_filter]
+
+                for seconds in range(100):
+                #while True: 
+
+                   with placeholder.container():       
+
+
+                       st.markdown("### Raw data")
+                       st.dataframe(df)
+                       time.sleep(1)
+
+            if st.checkbox("Show raw data word cloud"):
+                image5 = Image.open("resources/50commonWords.png")
+                st.image(image5)
+
+    # Building out the predications page
+    if selected == "Predictions":
+        st.subheader("How It Works")
+        st.markdown("Click on a tab to choose your desired classifier then enter a tweet relating to climate change and it will be classified according to its sentiment. \n See below on how to interpret results.")
+        #using tabs for different predictors
+        tab1 = st.tabs(["Linear SVC"])
+        with tab1:
+
+            st.markdown("To test classifier accuracy, copy and past one of the tweets in the list below into the classifier and check the corresponding sentiment that the model outputs.")
+
+            with st.expander("🐤 Tweets", expanded=False):
+                            st.write(
+                            """
+                            * The biggest threat to mankind is NOT global warming but liberal idiocy👊🏻🖕🏻\n
+                            Expected output = -1 \n
+                            * Polar bears for global warming. Fish for water pollution.\n
+                            Expected output = 0 \n
+                            * RT Leading the charge in the climate change fight - Portland Tribune  https://t.co/DZPzRkcVi2 \n
+                            Expected output = 1 \n
+                            * G20 to focus on climate change despite Trump’s resistance \n
+                            Expected output = 2
+
+                            """
+                        )
+            st.write("")
+
+            # Creating a text box for user input
+            tweet_text = st.text_area("Enter Text Below")            
+
+            if st.button("Predict Tweet Sentiment"):
+            # 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/LinearSVC.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("Sentiment: {}".format(prediction))
+
+                with st.expander("ℹ️ How to interpret the results", expanded=False):
+                    st.write(
+                 """
+                 Sentiment is categorized into 4 classes:\n
+                 [-1] = **Anti**: the tweet does not believe in man-made climate change \n
+                 [0] = **Neutral**: the tweet neither supports nor refutes the belief of man-made climate change \n
+                 [1] = **Pro**: the tweet supports the belief of man-made climate change \n
+                 [2] = **News**: the tweet links to factual news about climate change \n
+
+                 """
+             )
+                st.write("")
+
+            with tab2:
+
+                st.markdown("To test classifier accuracy, copy and past one of the tweets in the list below into the classifier and check the corresponding sentiment that the model outputs.")
+
+                with st.expander("🐤 Tweets", expanded=False):
+                    st.write(
+                    """
+                    * The biggest threat to mankind is NOT global warming but liberal idiocy👊🏻🖕🏻\n
+                    Expected output = -1 \n
+                    * Polar bears for global warming. Fish for water pollution.\n
+                    Expected output = 0 \n
+                    * RT Leading the charge in the climate change fight - Portland Tribune  https://t.co/DZPzRkcVi2 \n
+                    Expected output = 1 \n
+                    * G20 to focus on climate change despite Trump’s resistance \n
+                    Expected output = 2
+
+                    """
+                )
+                st.write("")
+
+            # Building out the contact page
+            if selected == "Contact Us":
+                with st.form("form1", clear_on_submit=True):
+                    st.subheader("Get in touch with us")
+                    name = st.text_input("Enter full name")
+                    email = st.text_input("Enter email")
+                    message = st.text_area("Message")
+
+                    submit = st.form_submit_button("Submit Form")
+                    if submit:
+                        st.write("Your form has been successfully submitted and we will be in touch")
+
+                job_filter = st.selectbox(
+                    "Select sentiment", pd.unique(raw['sentiment'])
+                )
+
+                # creating a single-element data frame with the selected sentiment
+                filtered_data = raw[raw["sentiment"] == job_filter]
+
+                # displaying the filtered data
+                st.write(filtered_data)
+
+# Run the application
+if __name__ == "__main__":
+    main()