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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)
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