Update app.py

app.py

@@ -240,19 +240,27 @@ df_combined["chaos_score"] = np.log1p(df_combined.get("diag_srl_gamma", 0)) / (d
 def resolve_audio_path(row: pd.Series) -> str:
     """
     Intelligently reconstructs the full path to an audio file
-    based on the logic from the data generation scripts.
+    based on the actual file structure patterns.
+    
+    Dog files: combined/{label}/{filename} e.g., combined/bark/bark_bark (1).wav
+    Human files: human/Actor_XX/{filename} e.g., human/Actor_01/03-01-01-01-01-01-01.wav
     """
     basename = str(row.get("filepath", ""))
     source = row.get("source", "")
     label = row.get("label", "")
 
-    # For "Dog" data, the structure is: {base_path}/{label}/{filename}
+    # For "Dog" data, the structure is: combined/{label}/{filename}
     if source == "Dog":
         expected_path = os.path.join(DOG_AUDIO_BASE_PATH, label, basename)
         if os.path.exists(expected_path):
             return expected_path
+        
+        # Try without subdirectory in case files are flat
+        expected_path = os.path.join(DOG_AUDIO_BASE_PATH, basename)
+        if os.path.exists(expected_path):
+            return expected_path
 
-    # For "Human" data, we must search within all "Actor_XX" subfolders
+    # For "Human" data, search within all "Actor_XX" subfolders
     elif source == "Human":
         if os.path.isdir(HUMAN_AUDIO_BASE_PATH):
             for actor_folder in os.listdir(HUMAN_AUDIO_BASE_PATH):
@@ -260,12 +268,28 @@ def resolve_audio_path(row: pd.Series) -> str:
                     expected_path = os.path.join(HUMAN_AUDIO_BASE_PATH, actor_folder, basename)
                     if os.path.exists(expected_path):
                         return expected_path
+        
+        # Try without subdirectory in case files are flat
+        expected_path = os.path.join(HUMAN_AUDIO_BASE_PATH, basename)
+        if os.path.exists(expected_path):
+            return expected_path
     
     # Fallback for dummy data or other cases
     if os.path.exists(basename):
         return basename
     
-    # If all else fails, return the original basename and let it error out with a clear message
+    # Try in local directories (for dummy data)
+    if source == "Dog":
+        for label_dir in ["bark", "growl", "whine", "pant"]:
+            local_path = os.path.join(DOG_DIR, label_dir, basename)
+            if os.path.exists(local_path):
+                return local_path
+    elif source == "Human":
+        local_path = os.path.join(HUMAN_DIR, "Actor_01", basename)
+        if os.path.exists(local_path):
+            return local_path
+    
+    # If all else fails, return the original basename
     return basename
 
 def get_cmt_data(filepath: str, lens: str):
@@ -596,17 +620,20 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="teal", secondary_hue="cyan")) a
                     )
                     
                     # Primary file selection (filtered by species)
+                    dog_files = df_combined[df_combined["source"] == "Dog"]["filepath"].astype(str).tolist()
+                    human_files = df_combined[df_combined["source"] == "Human"]["filepath"].astype(str).tolist()
+                    
                     primary_dropdown = gr.Dropdown(
                         label="Primary Audio File", 
-                        choices=[], 
-                        value=""
+                        choices=dog_files, 
+                        value=dog_files[0] if dog_files else None
                     )
                     
                     # Automatically found neighbor (from opposite species)
                     neighbor_dropdown = gr.Dropdown(
                         label="Auto-Found Cross-Species Neighbor", 
-                        choices=[], 
-                        value="",
+                        choices=human_files, 
+                        value=human_files[0] if human_files else None,
                         interactive=True  # Allow manual override
                     )
                     
@@ -754,12 +781,7 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="teal", secondary_hue="cyan")) a
                                inputs=cross_species_inputs, 
                                outputs=cross_species_outputs)
 
-            # Initialize on load
-            demo.load(lambda: update_dropdowns_on_species_change("Dog"), 
-                     outputs=[primary_dropdown, neighbor_dropdown])
-            demo.load(update_cross_species_view, 
-                     inputs=cross_species_inputs, 
-                     outputs=cross_species_outputs)
+            # Initialize on load - let the pre-populated dropdowns handle the initial state
 
 if __name__ == "__main__":
     demo.launch(share=True, debug=True)