TSLASentimentAnalyzer/.ipynb_checkpoints/app-checkpoint.py

import streamlit as st
import pandas as pd

import matplotlib.pyplot as plt
from scraper import RedditScraper
import pandas as pd
from classifier import predict
from config import settings
from transformers import pipeline
from loguru import logger

reddit = RedditScraper()
st.title("$TSLA Market Sentiment Analyzer using r/TSLA Subreddit")


def load_data(number, scraping_option):
    st.write("loading new data")
    # st.write(scraping_option)
    comments = []
    for submission in scraping_option(number):
        comments.extend(reddit.get_comment_forest(submission.comments))
        logger.debug(
            submission.title,
            submission.num_comments,
            len(reddit.get_comment_forest(submission.comments)),
        )
    df = pd.DataFrame(comments)

    return df


def select_scrap_type(option):
    if option == "Hot":
        st.write("Selected Hot submissions")
        return reddit.get_hot
    if option == "Rising":
        st.write("Selected rising submissions")
        return reddit.get_rising
    if option == "New":
        st.write("Selected new submissions")
        return reddit.get_new


st.info(
    "Option has been deactivated as the same submissions were scraped because the subreddit is not too active"
)
select = st.selectbox("choose option", ["Hot", "Rising", "New"], disabled=True)


number = st.number_input("Insert a number", step=1, max_value=30, min_value=3)


sentiment_pipeline = pipeline("sentiment-analysis", settings.model_path)

data = load_data(number, select_scrap_type("Hot"))


if st.button("Analyze"):
    results = sentiment_pipeline(list(data["comment"]))
    data["label"] = [res["label"] for res in results]
    data["sentiment_score"] = [res["score"] for res in results]
    st.write(data.groupby("label").count())
    sizes = list(data.groupby("label").count()["comment"])
    labels = "Negative", "Positive"
    fig1, ax1 = plt.subplots()
    ax1.pie(sizes, labels=labels, autopct="%1.1f%%", shadow=True, startangle=90)
    ax1.axis("equal")
    st.pyplot(fig1)


st.write(data)