updated

app.py

@@ -9,6 +9,7 @@ from transformers import AutoModel
 import os
 import tempfile
 from pathlib import Path
+import pandas as pd
 
 # Set page configuration
 st.set_page_config(
@@ -33,7 +34,7 @@ with st.sidebar:
     st.markdown("© 2024 Soumick Chatterjee | Glastonbury Group | Human Technopole")
 
 # Main content
-st.header("From Single-slice cardiac long-axis dynamic CINE scan (3D: TxHxW) to 128 latent factors...")
+st.header("From single-slice cardiac long-axis dynamic CINE scan (3D: TxHxW) to 128 latent factors...")
 
 # File uploader
 uploaded_file = st.file_uploader(
@@ -121,10 +122,14 @@ if uploaded_file is not None and process_button:
             # Convert output to NumPy array
             output_np = output.detach().cpu().numpy()
             output_list = output_np.flatten().tolist()
+            
+            # Prepare data for JSON
             output_data = {
                 "latent_factors": output_list
             }
             json_str = json.dumps(output_data, indent=4)
+            
+            # Download button for JSON file
             st.download_button(
                 label="Download Output as a JSON File",
                 data=json_str,
@@ -132,6 +137,18 @@ if uploaded_file is not None and process_button:
                 mime='application/json'
             )
             
+            # Prepare data for CSV
+            df = pd.DataFrame({'latent_factors': output_list})
+            csv_str = df.to_csv(index=False)
+            
+            # Download button for CSV file
+            st.download_button(
+                label="Download Output as CSV File",
+                data=csv_str,
+                file_name='latent_factors.csv',
+                mime='text/csv'
+            )
+            
     except Exception as e:
         st.error(f"An error occurred: {e}")
 elif uploaded_file is None:

requirements.txt

@@ -7,4 +7,5 @@ lpips
 torchvision
 pyssim
 pytorch_fid
-scikit-image
+scikit-image
+pandas