.

app.py

@@ -2,6 +2,7 @@ import requests
 import gradio as gr
 import os
 import torch
+import json
 
 # Check if CUDA is available and set the device accordingly
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@@ -9,26 +10,63 @@ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 API_URL = "https://api-inference.huggingface.co/models/MIT/ast-finetuned-audioset-10-10-0.4593"
 headers = {"Authorization": f"Bearer {os.environ.get('HF_TOKEN')}"}
 
+def format_error(message):
+    """Helper function to format error messages as JSON"""
+    return [{"error": message}]
+
 def classify_audio(audio_file):
     """
     Classify the uploaded audio file using Hugging Face AST model
     """
     if audio_file is None:
-        return "Please upload an audio file."
+        return format_error("Please upload an audio file.")
     
     try:
+        # Debug: Print token status (masked)
+        token = os.environ.get('HF_TOKEN')
+        if not token:
+            return format_error("Error: HF_TOKEN environment variable is not set. Please set your Hugging Face API token.")
+        print(f"Token present: {'Yes' if token else 'No'}, Token length: {len(token) if token else 0}")
+        
+        # Debug: Print audio file info
+        print(f"Audio file path: {audio_file.name}")
+        print(f"Audio file size: {os.path.getsize(audio_file.name)} bytes")
+        
         with open(audio_file.name, "rb") as f:
             data = f.read()
+        
+        print("Sending request to Hugging Face API...")
         response = requests.post(API_URL, headers=headers, data=data)
         
+        # Print response for debugging
+        print(f"Response status code: {response.status_code}")
+        print(f"Response headers: {dict(response.headers)}")
+        print(f"Response content: {response.content.decode('utf-8', errors='ignore')}")
+        
         if response.status_code == 200:
             results = response.json()
-            return results
+            # Format results for better readability
+            formatted_results = []
+            for result in results:
+                formatted_results.append({
+                    'label': result['label'],
+                    'score': f"{result['score']*100:.2f}%"
+                })
+            return formatted_results
+        elif response.status_code == 401:
+            return format_error("Error: Invalid or missing API token. Please check your Hugging Face API token.")
+        elif response.status_code == 503:
+            return format_error("Error: Model is loading. Please try again in a few seconds.")
         else:
-            return f"Error: API returned status code {response.status_code}"
+            error_msg = f"Error: API returned status code {response.status_code}\n"
+            error_msg += f"Response headers: {dict(response.headers)}\n"
+            error_msg += f"Response: {response.text}"
+            return format_error(error_msg)
             
     except Exception as e:
-        return f"Error processing audio: {str(e)}"
+        import traceback
+        error_details = traceback.format_exc()
+        return format_error(f"Error processing audio: {str(e)}\nDetails:\n{error_details}")
 
 # Create Gradio interface
 iface = gr.Interface(