Create App.py

App.py

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+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("---")