app.py

import streamlit as st
import snscrape.modules.twitter as sntwitter
from transformers import pipeline



#Load the model
model = pipeline(task="sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")

def analyze_tweet(tweet_text):
    results = model(tweet_text)
    return f"Toxicity Score: {results[0]['score']}\nLabel: {results[0]['label']}"

def fetch_tweets(username):
    results = []
    for tweet in sntwitter.TwitterUserScraper(username).get_items():
        tweet_text = tweet.content
        analysis_result = analyze_tweet(tweet_text)
        results.append(f"Tweet: {tweet_text}\n{analysis_result}\n")
        if len(results) >= 10:
            break
    return "\n".join(results)

st.title("Toxicity Analyzer")

task = st.sidebar.selectbox("Select Task", ("Analyze Tweet", "Fetch Tweets"))

if task == "Analyze Tweet":
    tweet_text = st.text_input("Enter the tweet text:")
    if st.button("Analyze"):
        if tweet_text:
            result = analyze_tweet(tweet_text)
            st.text(result)

if task == "Fetch Tweets":
    username = st.text_input("Enter the Twitter username:")
    if st.button("Fetch"):
        if username:
            results = fetch_tweets(username)
            st.text(results)