Update app.py

app.py

@@ -4,7 +4,7 @@ from transformers import pipeline
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
-from pandas.plotting import parallel_coordinates
+import xlsxwriter  # Required for handling images in Excel
 
 # Initialize session state for results, image names, and image sizes if not already present
 if 'results' not in st.session_state:
@@ -64,7 +64,6 @@ if st.button("Generate HeatMap & DataFrame"):
         for i, result_set in enumerate(results):
             # Initialize a dictionary for the current set with zeros
             current_data = {
-            
                 'Happy': 0,
                 'Surprise': 0,
                 'Neutral': 0,
@@ -72,12 +71,7 @@ if st.button("Generate HeatMap & DataFrame"):
                 'Disgust': 0,
                 'Angry': 0,
                 'Fear': 0,
-
-                
-
-                # Add other emotions if necessary
                 'Image Name': image_names[i],
-                #'Image Size (KB)': image_sizes[i]
                 'Image Size (KB)': f"{image_sizes[i]:.1f}"  # Format the size to one decimal place
             }
             
@@ -103,24 +97,48 @@ if st.button("Generate HeatMap & DataFrame"):
         plt.xlabel('Emotion Categories')
         plt.ylabel('Data Points')
         st.pyplot(plt)
-        
 
-        
-        # Optional: Save the DataFrame to a CSV file
+        # Save the DataFrame to a CSV file
         df_emotions.to_csv('emotion_scores.csv', index=False)
         st.success('DataFrame generated and saved as emotion_scores.csv')
-        
+
         with open('emotion_scores.csv', 'r') as f:
             csv_file = f.read()
-        
+
         st.download_button(
             label='Download Emotion Scores as CSV',
             data=csv_file,
             file_name='emotion_scores.csv',
             mime='text/csv',
         )
-        
-        st.success('DataFrame generated and available for download.')
-        
+
+        # Save the DataFrame to an Excel file with images
+        if st.button("Save to Excel"):
+            # Create a new Excel writer object
+            writer = pd.ExcelWriter('emotion_scores.xlsx', engine='xlsxwriter')
+            df_emotions.to_excel(writer, index=False)
+
+            # Access the xlsxwriter workbook and worksheet objects
+            workbook  = writer.book
+            worksheet = writer.sheets['Sheet1']
+
+            # Iterate over the images and insert them into the 'Image View' column
+            for idx, image_path in enumerate(st.session_state['image_names']):
+                # Your logic to define the image_path
+                worksheet.insert_image(f'J{idx + 2}', image_path)  # 'J' is the 10th column
+
+            # Close the writer and save the Excel file
+            writer.save()
+            st.success('DataFrame generated and saved as emotion_scores.xlsx')
+
+            with open('emotion_scores.xlsx', 'rb') as f:
+                excel_file = f.read()
+
+            st.download_button(
+                label='Download Emotion Scores as Excel',
+                data=excel_file,
+                file_name='emotion_scores.xlsx',
+                mime='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet',
+            )
     else:
         st.error("No results to generate DataFrame. Please predict emotions first.")