Update app.py

app.py

@@ -16,42 +16,60 @@ from googleapiclient.discovery import build
 import warnings
 warnings.filterwarnings('ignore')
 
+# Set random seeds for reproducibility
 np.random.seed(42)
 tf.random.set_seed(42)
 
+# Streamlit page configuration
 st.set_page_config(page_title="Sentiment Pulse", layout="wide")
 st.markdown("<h1 style='text-align: center; color: #7B68EE;'>Sentiment Pulse: Multi-Platform Analysis</h1>", unsafe_allow_html=True)
 
+# API credentials (replace with your own if needed)
 REDDIT_CLIENT_ID = "S7pTXhj5JDFGDb3-_zrJEA"
 REDDIT_CLIENT_SECRET = "QP3NYN4lrAKVLrBamzLGrpFywiVg8w"
 REDDIT_USER_AGENT = "SoundaryaR_Bot/1.0"
 YOUTUBE_API_KEY = "AIzaSyAChqXPaiNE9hKhApkgjgonzdgiCCOo"
 
+# Initialize APIs
 reddit = praw.Reddit(client_id=REDDIT_CLIENT_ID, client_secret=REDDIT_CLIENT_SECRET, user_agent=REDDIT_USER_AGENT)
 youtube = build('youtube', 'v3', developerKey=YOUTUBE_API_KEY)
+
+# Load sentiment analysis models
 bert_classifier = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
 vader_analyzer = SentimentIntensityAnalyzer()
 
 @st.cache_data
 def load_twitter_data():
-    df = pd.read_csv("twitter_dataset.csv", encoding='latin-1', 
-                     names=['sentiment', 'id', 'date', 'query', 'user', 'text'])
-    df['date'] = pd.to_datetime(df['date'])
-    df['sentiment'] = df['sentiment'].map({0: 'negative', 4: 'positive'})
-    return df.sample(10000)
+    try:
+        df = pd.read_csv("twitter_dataset.csv", encoding='latin-1', 
+                         names=['sentiment', 'id', 'date', 'query', 'user', 'text'])
+        df['date'] = pd.to_datetime(df['date'], errors='coerce')
+        df['sentiment'] = df['sentiment'].map({0: 'negative', 4: 'positive'})
+        return df.sample(10000, random_state=42)
+    except FileNotFoundError:
+        st.error("twitter_dataset.csv not found. Please ensure the file is in the working directory.")
+        return pd.DataFrame()
 
 def fetch_reddit_data(keyword):
-    subreddit = reddit.subreddit("all")
-    posts = subreddit.search(keyword, limit=100)
-    return pd.DataFrame([{'date': datetime.fromtimestamp(post.created_utc), 'text': post.title + " " + post.selftext} for post in posts])
+    try:
+        subreddit = reddit.subreddit("all")
+        posts = subreddit.search(keyword, limit=100)
+        return pd.DataFrame([{'date': datetime.fromtimestamp(post.created_utc), 'text': post.title + " " + post.selftext} for post in posts])
+    except Exception as e:
+        st.error(f"Error fetching Reddit data: {e}")
+        return pd.DataFrame()
 
 def fetch_youtube_data(keyword):
-    request = youtube.search().list(q=keyword, part="snippet", maxResults=50, type="video")
-    response = request.execute()
-    return pd.DataFrame([{
-        'date': datetime.strptime(item['snippet']['publishedAt'], "%Y-%m-%dT%H:%M:%SZ"),
-        'text': item['snippet']['title'] + " " + item['snippet']['description']
-    } for item in response['items']])
+    try:
+        request = youtube.search().list(q=keyword, part="snippet", maxResults=50, type="video")
+        response = request.execute()
+        return pd.DataFrame([{
+            'date': datetime.strptime(item['snippet']['publishedAt'], "%Y-%m-%dT%H:%M:%SZ"),
+            'text': item['snippet']['title'] + " " + item['snippet']['description']
+        } for item in response['items']])
+    except Exception as e:
+        st.error(f"Error fetching YouTube data: {e}")
+        return pd.DataFrame()
 
 def get_bert_sentiment(text):
     try:
@@ -70,9 +88,11 @@ def combined_sentiment(text):
     avg_score = (bert_score + abs(vader_score)) / 2
     return 1 if avg_score > 0.5 else 0, avg_score
 
+# Sidebar for keyword input
 st.sidebar.title("Keyword Search")
 keyword = st.sidebar.text_input("Enter a keyword (e.g., 'happy')", value="happy")
 
+# Load and filter data
 twitter_df = load_twitter_data()
 twitter_filtered = twitter_df[twitter_df['text'].str.contains(keyword, case=False, na=False)]
 reddit_df = fetch_reddit_data(keyword)
@@ -103,6 +123,7 @@ else:
             st.warning(f"Not enough {platform} data for prediction.")
             fig, ax = plt.subplots()
             ax.plot(daily_sentiment['date'], daily_sentiment['combined_score'], label='Historical')
+            ax.legend()
             st.pyplot(fig)
             continue
 
@@ -159,4 +180,4 @@ else:
         explainer = shap.Explainer(lr_model, X_lr)
         shap_values = explainer(X_lr)
         shap.plots.beeswarm(shap_values, show=False)
-        st.pyplot(plt.gcf())
+        st.pyplot(plt.gcf())