update bar chart

app.py

@@ -5,42 +5,48 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 from wordcloud import WordCloud
 
+st.set_option('deprecation.showPyplotGlobalUse', False)
 # Define the Streamlit app
-st.title("Data Analysis and Visualization")
+st.title("Aspected-Based Sentiment Analysis with MVP")
+palette_color = sns.color_palette('Set1')
 
 # File upload and processing
 uploaded_file = st.file_uploader("Upload JSON File", type=["json"])
 if uploaded_file:
     loaded_dict = json.load(uploaded_file)
     df = pd.DataFrame(loaded_dict)
-    st.subheader("Dataframe (df)")
+    st.subheader(len(df)," sentiment tuples was detected")
     st.write(df)
 
-    # Group by and aggregate data
-    grouped = df.groupby('A').agg({'S': ['count', lambda x: (x == 'great').sum(), lambda x: (x == 'ok').sum(), lambda x: (x == 'bad').sum()]})
-    grouped.columns = grouped.columns.map('_'.join)
-    grouped = grouped.reset_index()
-    grouped = grouped.rename(columns={'A': 'Aspect', 'S_count': 'Freq', 'S_<lambda_0>': 'Great', 'S_<lambda_1>': 'Ok', 'S_<lambda_2>': 'Bad'})
-
-    st.subheader("Top Aspects by Frequency")
-    st.write(grouped.sort_values(by="Freq", ascending=False).head(5))
-
     # Sentiment Distribution Chart
-    sentiment_distribution = df["S"].value_counts(normalize=True) * 100
-    palette_color = sns.color_palette('bright')
+    sentiment_distribution_perc = df["S"].value_counts(normalize=True) * 100
+    sentiment_distribution = df["S"].value_counts()
 
     st.subheader("Sentiment Distribution")
     fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 6))
 
-    ax1.pie(sentiment_distribution, labels=sentiment_distribution.index, autopct='%1.1f%%', startangle=140)
+    ax1.pie(sentiment_distribution_perc, labels=sentiment_distribution_perc.index, autopct='%1.1f%%', startangle=140,colors=palette_color)
     ax1.axis('equal')
     ax1.set_title("Sentiment Distribution %")
 
-    sns.countplot(x="S", data=df, palette=palette_color, ax=ax2)
+    # sns.countplot(x="S", data=df, palette=palette_color, ax=ax2)
+    
     ax2.set_title("Sentiment Distribution Counts")
-
+    ax2.bar(sentiment_distribution.index, sentiment_distribution.values, color=palette_color)
+    # ax2.xlabel("Sentiment")
+    # ax2.ylabel("Times")
+    ax2.xticks(rotation=0)  # Rotate x-axis labels if needed
     st.pyplot(fig)
 
+    # Group by and aggregate data
+    grouped = df.groupby('A').agg({'S': ['count', lambda x: (x == 'great').sum(), lambda x: (x == 'ok').sum(), lambda x: (x == 'bad').sum()]})
+    grouped.columns = grouped.columns.map('_'.join)
+    grouped = grouped.reset_index()
+    grouped = grouped.rename(columns={'A': 'Aspect', 'S_count': 'Freq', 'S_<lambda_0>': 'Great', 'S_<lambda_1>': 'Ok', 'S_<lambda_2>': 'Bad'})
+
+    st.subheader("Top 5 Most Mentioned Product Apsects")
+    st.write(grouped.sort_values(by="Freq", ascending=False).head(5))
+
     # Word Cloud
     aspect_terms = " ".join(df["A"])
     wordcloud = WordCloud(