Update App.py

App.py

@@ -1,78 +1,79 @@
+import os
 import streamlit as st
 import praw
 import googleapiclient.discovery
-import pandas as pd
 import joblib
+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LinearRegression
 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
+# Load environment variables
+REDDIT_CLIENT_ID = os.getenv("REDDIT_CLIENT_ID")
+REDDIT_CLIENT_SECRET = os.getenv("REDDIT_CLIENT_SECRET")
+REDDIT_USER_AGENT = os.getenv("REDDIT_USER_AGENT")
+YOUTUBE_API_KEY = os.getenv("YOUTUBE_API_KEY")
 
-# Reddit Authentication
+# Authenticate Reddit
 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"]
+        client_id=REDDIT_CLIENT_ID,
+        client_secret=REDDIT_CLIENT_SECRET,
+        user_agent=REDDIT_USER_AGENT
     )
 
-# YouTube Authentication
+# Authenticate YouTube
 def authenticate_youtube():
-    return googleapiclient.discovery.build("youtube", "v3", developerKey=st.secrets["YOUTUBE_API_KEY"])
+    return googleapiclient.discovery.build("youtube", "v3", developerKey=YOUTUBE_API_KEY)
 
-# Sentiment Analysis Functions
-def vader_sentiment(text):
-    analyzer = SentimentIntensityAnalyzer()
-    return analyzer.polarity_scores(text)["compound"]
+# VADER Sentiment Analysis
+vader = SentimentIntensityAnalyzer()
 
-def bert_sentiment(text):
-    sentiment_pipeline = pipeline("sentiment-analysis")
-    return sentiment_pipeline(text)[0]['label']
+def get_vader_sentiment(text):
+    scores = vader.polarity_scores(text)
+    return scores['compound']  # Ranges from -1 (negative) to +1 (positive)
 
-def regression_sentiment(text):
-    if model:
-        return model.predict([text])[0]
-    return "Model not trained yet"
+# BERT Sentiment Analysis
+bert_sentiment = pipeline("sentiment-analysis")
 
-# 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
+def get_bert_sentiment(text):
+    result = bert_sentiment(text)[0]
+    return result['label'], result['score']
 
-# 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", [])]
+# Regression Sentiment Analysis
+vectorizer = TfidfVectorizer()
+regressor = LinearRegression()
 
-# Streamlit UI
-st.title("Sentiment Analysis App")
-keyword = st.text_input("Enter a keyword to analyze:")
+def train_regression_model():
+    sample_data = [
+        ("I love this!", 1.0),
+        ("This is amazing", 0.9),
+        ("It's okay", 0.5),
+        ("Not great", 0.3),
+        ("I hate this", 0.1)
+    ]
+    texts, scores = zip(*sample_data)
+    X = vectorizer.fit_transform(texts)
+    regressor.fit(X, scores)
+    joblib.dump((vectorizer, regressor), "sentiment_model.pkl")
 
-if keyword:
-    reddit_data = get_reddit_data(keyword)
-    youtube_data = get_youtube_data(keyword)
+train_regression_model()
 
-    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("---")
+# Predict with Regression Model
+def get_regression_sentiment(text):
+    vectorizer, regressor = joblib.load("sentiment_model.pkl")
+    X = vectorizer.transform([text])
+    return regressor.predict(X)[0]
 
-    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("---")
+# Streamlit UI
+st.title("Sentiment Analysis App")
+user_input = st.text_area("Enter text for sentiment analysis")
+if st.button("Analyze"):
+    vader_score = get_vader_sentiment(user_input)
+    bert_label, bert_score = get_bert_sentiment(user_input)
+    regression_score = get_regression_sentiment(user_input)
+    
+    st.write(f"**VADER Sentiment Score:** {vader_score}")
+    st.write(f"**BERT Sentiment:** {bert_label} ({bert_score:.2f})")
+    st.write(f"**Regression Sentiment Score:** {regression_score:.2f}")