app.py

import streamlit as st
import plotly as plt 
import numpy as np 
import pandas as pd
import webbrowser
import requests
import json
from streamlit_lottie import st_lottie 

st.set_page_config(page_title= "Welcome Page", page_icon ="👋")


st.sidebar.success("Select The Page You Want to Explore: ")

st.title("Welcome to my Sentiment Analysis App")

def load_lottiefile(filepath: str):
    with open(filepath, "r") as f:
        return json.load(f)

# initializaing my session state 
if 'lottie_hello' not in st.session_state:
    st.session_state.lottie_hello = load_lottiefile("./lottie_animations/main.json")

# creating a funciton to upload the file while implementing session state
def handle_uploaded_file(uploaded_file):
    if uploaded_file is not None:
        st.session_state.lottie_hello = load_lottiefile(uploaded_file.name)


# displaying the Lottie animation
st_lottie(st.session_state.lottie_hello, height=200)


st.markdown("""On this app, you will  be able to classify Covid-19 sentiments with the Roberta Base model
The objective of this challenge is to develop a machine learning model to assess if a twitter post that is related to vaccinations is positive, neutral, or negative.""")

st.subheader("""Variable Definition:""")

st.write("""

    **tweet_id**: Unique identifier of the tweet

**safe_tweet**: Text contained in the tweet. Some sensitive information has been removed like usernames and urls

**label**: Sentiment of the tweet (-1 for negative, 0 for neutral, 1 for positive)

**agreement**: The tweets were labeled by three people. Agreement indicates the percentage of the three reviewers that agreed on the given label. You may use this column in your training, but agreement data will not be shared for the test set.

**Train.csv**:  Labelled tweets on which to train your model
         
The Models I fine-tuned include: \n
- Roberta: Achieving an f1 score of 0.64 \n 
- Distilled Bert: Achieving an f1-score of 0.71
             
         """)

data= pd.read_csv("datasets/Train.csv")

st.subheader("A sample of the orginal Dataframe (Train.csv)")

st.write(data.head())

st.subheader("A sample of the preprocessed dataset")

data_clean= pd.read_csv("datasets/clean_copy.csv")

data_clean= data_clean.drop("Unnamed: 0", axis= 1)

st.write(data_clean.head())