Update app.py

app.py

@@ -4,10 +4,13 @@ import praw
 import googleapiclient.discovery
 import joblib
 import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
 from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.linear_model import LinearRegression
 from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
 from transformers import pipeline
+from fbprophet import Prophet
 
 # Load environment variables
 REDDIT_CLIENT_ID = os.getenv("REDDIT_CLIENT_ID")
@@ -23,57 +26,46 @@ def authenticate_reddit():
         user_agent=REDDIT_USER_AGENT
     )
 
-# Authenticate YouTube
-def authenticate_youtube():
-    return googleapiclient.discovery.build("youtube", "v3", developerKey=YOUTUBE_API_KEY)
-
-# VADER Sentiment Analysis
+# Initialize sentiment analysis tools
 vader = SentimentIntensityAnalyzer()
-
-def get_vader_sentiment(text):
-    scores = vader.polarity_scores(text)
-    return scores['compound']  # Ranges from -1 (negative) to +1 (positive)
-
-# BERT Sentiment Analysis
 bert_sentiment = pipeline("sentiment-analysis")
 
-def get_bert_sentiment(text):
-    result = bert_sentiment(text)[0]
-    return result['label'], result['score']
-
-# Regression Sentiment Analysis
-vectorizer = TfidfVectorizer()
-regressor = LinearRegression()
-
-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")
-
-train_regression_model()
-
-# 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]
-
 # Streamlit UI
-st.title("Sentiment Analysis App")
+st.title("Social Media Sentiment Analysis")
 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)
+
+if user_input:
+    # VADER Sentiment
+    vader_score = vader.polarity_scores(user_input)["compound"]
+    
+    # BERT Sentiment
+    bert_result = bert_sentiment(user_input)[0]
+    bert_label = bert_result["label"]
+    bert_score = bert_result["score"]
     
+    # Display results
     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}")
+    
+    # Generate fake sentiment data for forecasting (Replace with real data)
+    date_rng = pd.date_range(start=pd.Timestamp.today(), periods=30, freq='D')
+    sentiment_data = pd.DataFrame({
+        'ds': date_rng,
+        'y': np.random.uniform(-1, 1, size=len(date_rng))
+    })
+    
+    # Train Prophet Model
+    model = Prophet()
+    model.fit(sentiment_data)
+    future = model.make_future_dataframe(periods=7)
+    forecast = model.predict(future)
+    
+    # Plot Sentiment Forecast
+    st.subheader("Sentiment Forecast for Next 7 Days")
+    fig, ax = plt.subplots()
+    sns.lineplot(x=forecast['ds'], y=forecast['yhat'], ax=ax, label='Predicted Sentiment')
+    ax.fill_between(forecast['ds'], forecast['yhat_lower'], forecast['yhat_upper'], alpha=0.3)
+    ax.set_title("Sentiment Trend Prediction")
+    ax.set_xlabel("Date")
+    ax.set_ylabel("Sentiment Score")
+    st.pyplot(fig)