add data analyze

app.py

@@ -1,4 +1,63 @@
+import pandas as pd
+import json
 import streamlit as st
+import matplotlib.pyplot as plt
+import seaborn as sns
+from wordcloud import WordCloud
 
-x = st.slider("Select a value")
-st.write(x,"squared is",x*x)
+# Define the Streamlit app
+st.title("Data Analysis and Visualization")
+
+# 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.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')
+
+    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.axis('equal')
+    ax1.set_title("Sentiment Distribution %")
+
+    sns.countplot(x="S", data=df, palette=palette_color, ax=ax2)
+    ax2.set_title("Sentiment Distribution Counts")
+
+    st.pyplot(fig)
+
+    # Word Cloud
+    aspect_terms = " ".join(df["A"])
+    wordcloud = WordCloud(
+        width=800,
+        height=400,
+        background_color='white',
+        max_words=100,
+        colormap='inferno',
+        contour_width=3,
+        contour_color='red',
+    ).generate(aspect_terms)
+
+    st.subheader("Word Cloud for Most Mentioned Aspects")
+    plt.figure(figsize=(10, 5))
+    plt.imshow(wordcloud, interpolation='bilinear')
+    plt.title("Most mentioned aspect terms")
+    plt.axis("off")
+    st.pyplot()
+
+st.sidebar.markdown("**Upload a JSON file to get started.**")