Use gr.Textbox for JSON API to receive raw file paths

app.py

@@ -210,12 +210,15 @@ def predict_tone_json(audio_file):
 
     try:
         # Handle different input types from Gradio API
-        # audio_file can be: string path, dict with 'path' key, or other formats
-        if isinstance(audio_file, dict):
-            # FileData format from API
-            file_path = audio_file.get('path', audio_file.get('name', str(audio_file)))
-        elif isinstance(audio_file, str):
+        # gr.File returns the file path as a string
+        if isinstance(audio_file, str):
             file_path = audio_file
+        elif hasattr(audio_file, 'name'):
+            # File-like object with name attribute
+            file_path = audio_file.name
+        elif isinstance(audio_file, dict):
+            # FileData format from API - prefer 'path' over 'name'/'orig_name'
+            file_path = audio_file.get('path') or audio_file.get('name', str(audio_file))
         else:
             # Try to get path attribute or convert to string
             file_path = getattr(audio_file, 'path', str(audio_file))
@@ -432,14 +435,15 @@ with gr.Blocks(css=custom_css, title="Pro-TeVA Tone Recognition") as demo:
                 show_label=True
             )
 
-    # JSON API interface - use gr.File to properly handle file paths from API
+    # JSON API interface - use gr.Textbox to receive raw file path from API
+    # This avoids Gradio's file component preprocessing issues
     json_api = gr.Interface(
         fn=predict_tone_json,
-        inputs=gr.File(label="Audio File", file_types=["audio"]),
+        inputs=gr.Textbox(label="Audio File Path", placeholder="Path to uploaded audio file"),
         outputs=gr.JSON(label="Prediction Result"),
         api_name="predict_json",
         title="Pro-TeVA JSON API",
-        description="Returns pure JSON response for programmatic access"
+        description="Upload file first via /gradio_api/upload, then pass the returned path here"
     )
 
 # API Documentation tab