Update app.py

app.py

@@ -2,10 +2,10 @@ import streamlit as st
 import requests
 import pandas as pd
 from datetime import datetime, timedelta
-import nltk
-from wordcloud import WordCloud
-import base64
-from io import BytesIO
+# import nltk
+# from wordcloud import WordCloud
+# import base64
+# from io import BytesIO
 import numpy as np
 from sklearn.linear_model import LinearRegression
 import plotly.graph_objects as go
@@ -57,12 +57,12 @@ def analyze_text(text):
     return float(score)
 
 
-def generate_wordcloud(text):
-    stopwords = nltk.corpus.stopwords.words('english')
-    wordcloud = WordCloud(width=800, height=400, background_color="white", stopwords=stopwords).generate(text)
-    buf = BytesIO()
-    wordcloud.to_image().save(buf, format="PNG")
-    return base64.b64encode(buf.getvalue()).decode()
+# def generate_wordcloud(text):
+#     stopwords = nltk.corpus.stopwords.words('english')
+#     wordcloud = WordCloud(width=800, height=400, background_color="white", stopwords=stopwords).generate(text)
+#     buf = BytesIO()
+#     wordcloud.to_image().save(buf, format="PNG")
+#     return base64.b64encode(buf.getvalue()).decode()
 
 
 # --------------------------
@@ -208,11 +208,11 @@ def main():
     col2.metric("ข่าวเชิงบวก", f"{pos_pct:.1f}%")
     col3.metric("ข่าวเชิงลบ", f"{neg_pct:.1f}%")
 
-    # WordCloud
-    st.subheader("☁️ Word Cloud")
-    all_text = " ".join(news_df["text"].tolist())
-    img = generate_wordcloud(all_text)
-    st.image(f"data:image/png;base64,{img}", use_column_width=True)
+    # # WordCloud
+    # st.subheader("☁️ Word Cloud")
+    # all_text = " ".join(news_df["text"].tolist())
+    # img = generate_wordcloud(all_text)
+    # st.image(f"data:image/png;base64,{img}", use_column_width=True)
 
     # ---------------------------------------------------------
     # เตรียมข้อมูลสำหรับกราฟ Sentiment & Price
@@ -367,5 +367,5 @@ def main():
 # RUN APP
 # ---------------------------------------------------------
 if __name__ == "__main__":
-    nltk.download("stopwords", quiet=True)
+    # nltk.download("stopwords", quiet=True)
     main()