import streamlit as st
import praw
import googleapiclient.discovery
import pandas as pd
import joblib
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
from transformers import pipeline

# Load pre-trained regression model (if available)
try:
    model = joblib.load("sentiment_regression.pkl")
except:
    model = None

# Reddit Authentication
def authenticate_reddit():
    return praw.Reddit(
        client_id=st.secrets["REDDIT_CLIENT_ID"],
        client_secret=st.secrets["REDDIT_CLIENT_SECRET"],
        user_agent=st.secrets["REDDIT_USER_AGENT"]
    )

# YouTube Authentication
def authenticate_youtube():
    return googleapiclient.discovery.build("youtube", "v3", developerKey=st.secrets["YOUTUBE_API_KEY"])

# Sentiment Analysis Functions
def vader_sentiment(text):
    analyzer = SentimentIntensityAnalyzer()
    return analyzer.polarity_scores(text)["compound"]

def bert_sentiment(text):
    sentiment_pipeline = pipeline("sentiment-analysis")
    return sentiment_pipeline(text)[0]['label']

def regression_sentiment(text):
    if model:
        return model.predict([text])[0]
    return "Model not trained yet"

# Fetch Reddit Data
def get_reddit_data(keyword):
    reddit = authenticate_reddit()
    posts = []
    for submission in reddit.subreddit("all").search(keyword, limit=10):
        posts.append(submission.title)
    return posts

# Fetch YouTube Data
def get_youtube_data(keyword):
    youtube = authenticate_youtube()
    request = youtube.search().list(q=keyword, part="snippet", maxResults=10)
    response = request.execute()
    return [item["snippet"]["title"] for item in response.get("items", [])]

# Streamlit UI
st.title("Sentiment Analysis App")
keyword = st.text_input("Enter a keyword to analyze:")

if keyword:
    reddit_data = get_reddit_data(keyword)
    youtube_data = get_youtube_data(keyword)

    st.subheader("Reddit Sentiment")
    for post in reddit_data:
        st.write(f"🔹 {post}")
        st.write(f"VADER Sentiment: {vader_sentiment(post)}")
        st.write(f"BERT Sentiment: {bert_sentiment(post)}")
        st.write(f"Regression Sentiment: {regression_sentiment(post)}")
        st.write("---")

    st.subheader("YouTube Sentiment")
    for title in youtube_data:
        st.write(f"📺 {title}")
        st.write(f"VADER Sentiment: {vader_sentiment(title)}")
        st.write(f"BERT Sentiment: {bert_sentiment(title)}")
        st.write(f"Regression Sentiment: {regression_sentiment(title)}")
        st.write("---")