Update app.py

app.py

@@ -135,47 +135,26 @@ if st.button("🔮 Predict Sepsis"):
         # Convert the input data to a pandas DataFrame
         input_df = pd.DataFrame([input_data])
 
-        # Display DataFrame
+        # Display the DataFrame
         st.table(input_df)
 
-        # Download Button
-        download_btn = st.button("📥 Download Prediction")
-
-        if download_btn:
-            # Prompt user for patient's name
-            patient_name = st.text_input("Enter Patient's Name:")
-
-            # Create heading
-            heading = f"Sepsis Status Prediction For {patient_name}"
-
-            # Export to Excel
-            excel_file = BytesIO()
-            with pd.ExcelWriter(excel_file, engine='openpyxl') as writer:
-                input_df.to_excel(writer, sheet_name='Sheet1', index=False)
-                writer.sheets['Sheet1'].title = heading
-            excel_file.seek(0)
-            st.download_button(
-                label="Download Excel",
-                data=excel_file,
-                file_name=f"sepsis_prediction_{patient_name}.xlsx",
-                key='excel-download',
-            )
-
-            # Export to PDF
-            pdf_file = BytesIO()
-            pdf_canvas = canvas.Canvas(pdf_file)
-            pdf_canvas.drawString(100, 800, heading)
-            table = input_df.to_string().split('\n')
-            pdf_canvas.drawString(100, 780, '\n'.join(table))
-            pdf_canvas.save()
-            pdf_file.seek(0)
-            st.download_button(
-                label="Download PDF",
-                data=pdf_file,
-                file_name=f"sepsis_prediction_{patient_name}.pdf",
-                key='pdf-download',
-            )
-
+        # Download Button Section
+        st.subheader("Download Prediction")
+        patient_name = st.text_input("Enter the patient's name:")
+        if st.button("Download Prediction"):
+            if patient_name:
+                # Generate the heading
+                heading = f"Sepsis Status Prediction For {patient_name}"
+                
+                # Save the DataFrame to a CSV file
+                input_df.to_csv(f"{patient_name}_sepsis_prediction.csv", index=False, encoding='utf-8-sig')
+
+                # Provide a download link
+                st.markdown(f"### [Download {patient_name}'s Prediction CSV](data:{patient_name}_sepsis_prediction.csv)")
+
+            else:
+                st.warning("Please enter the patient's name.")
+    
     except Exception as e:
         st.error(f"An error occurred: {e}")