Update app.py

app.py

@@ -42,18 +42,14 @@ def preprocess_eeg(
     uploaded_file = Path(eeg_file.name)
 
     if input_format == "mff":
-        # Validate: must be a ZIP file
         if not zipfile.is_zipfile(uploaded_file):
             raise ValueError("MFF input must be a ZIP file containing the .mff folder.")
-        # Extract directly into subject_dir as "sub.mff"
         eeg_path = subject_dir / f"{SUBJECT_ID}.mff"
         with zipfile.ZipFile(uploaded_file, 'r') as zf:
             zf.extractall(eeg_path)
-        # Optional: validate
         if not (eeg_path / "info.xml").exists():
             raise ValueError("Invalid MFF: missing 'info.xml' in the ZIP.")
     else:
-        # For FIF: copy the file into subject_dir with its original name
         eeg_path = subject_dir / uploaded_file.name
         shutil.copy(uploaded_file, eeg_path)
 
@@ -79,15 +75,42 @@ def preprocess_eeg(
             random_state=99,
         )
 
-        # Load cleaned data and save info summary
-        cleaned_fif = subject_dir / f"{SUBJECT_ID}_eeg_ica_cleaned_raw.fif"
+        # 🔧 FIX: Handle multiple cleaned FIF files
+        cleaned_fifs = list(subject_dir.glob("*_eeg_ica_cleaned_raw.fif"))
+        if not cleaned_fifs:
+            raise FileNotFoundError("No cleaned FIF file found after preprocessing.")
+
+        # Prefer the one with 'post_ica' in the name (final version)
+        final_fif = None
+        for f in cleaned_fifs:
+            if "post_ica" in f.name:
+                final_fif = f
+                break
+        if final_fif is None:
+            # If no post_ica file, use the most recently modified (fallback)
+            final_fif = max(cleaned_fifs, key=os.path.getmtime)
+
+        # Delete all cleaned FIFs except the final one
+        for f in cleaned_fifs:
+            if f != final_fif:
+                f.unlink()
+
+        # Rename to standard clean name
+        expected_name = subject_dir / f"{SUBJECT_ID}_eeg_ica_cleaned_raw.fif"
+        if final_fif != expected_name:
+            final_fif.rename(expected_name)
+            cleaned_fif = expected_name
+        else:
+            cleaned_fif = final_fif
+
+        # Load and save info summary
         raw = mne.io.read_raw_fif(str(cleaned_fif), preload=False)
         info_summary = subject_dir / f"{SUBJECT_ID}_info_summary.txt"
         with open(info_summary, "w") as f:
             f.write(f"=== EEG Info Summary for {SUBJECT_ID} ===\n")
             f.write(str(raw.info))
 
-        # Create ZIP of entire subject_dir
+        # Create ZIP
         zip_path = OUTPUT_DIR / f"{SUBJECT_ID}_preprocessing_output.zip"
         with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zf:
             for root, _, files in os.walk(subject_dir):