integrated the labeling functionality

app.py

@@ -4,6 +4,7 @@ import seaborn as sns
 import matplotlib.pyplot as plt
 from dataset_import import load_data
 from data_cleaning import clean_data
+from data_labelling import label_dataset
 
 st.title("AnalyzeYT Dataset Analysis")
 
@@ -24,13 +25,6 @@ if data is not None:
     st.write("Data Description:")
     st.write(data.describe())
 
-    # Add correlation matrix
-#    st.write("Correlation Matrix:")
-#    corr = data.corr()
-#    st.write(corr)
-#    sns.heatmap(corr, annot=True, cmap='coolwarm')
-#    st.pyplot()
-
     # Data visualization options
     st.write("Data Visualization:")
     chart_type = st.selectbox("Select Chart Type", ['Line Chart', 'Bar Chart', 'Histogram'])
@@ -58,3 +52,16 @@ if data is not None:
         plt.xlabel(selected_column)
         plt.ylabel('Frequency')
         st.pyplot()
+
+    # Add option for labeling the dataset
+    st.write("Labeling Options:")
+    label_option = st.radio("Do you want to label your dataset?", ('No', 'Yes'))
+
+    if label_option == 'Yes':
+        output_name = st.text_input("Enter Output File Name", "labeled_dataset.csv")
+        if st.button("Run Labeling"):
+            client = st.text_input("Enter your Gradio Client API Key")  # Placeholder for Gradio API key input
+            labeled_data = label_dataset(data, client)
+            labeled_data.to_csv(output_name, index=False)
+            st.write("Labeling Completed. Download your file below:")
+            st.download_button(label="Download Labeled Dataset", data=labeled_data.to_csv(), file_name=output_name, mime='text/csv')