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test_llama_omni_api.py

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+#!/usr/bin/env python3
+"""
+Test script for LLaMA-Omni API on Hugging Face Spaces.
+This script sends a text message to the LLaMA-Omni2 API and saves the response.
+"""
+
+import os
+import time
+from pathlib import Path
+from gradio_client import Client
+
+# API endpoint
+API_URL = "https://marcosremar2-llama-omni.hf.space"
+
+# Input and output paths
+INPUT_AUDIO_PATH = "/Users/marcos/Documents/projects/test/whisper-realtime/llama-omni/llama-omni/test.mp3"
+OUTPUT_DIR = "./output"
+OUTPUT_TEXT_PATH = os.path.join(OUTPUT_DIR, f"response_{int(time.time())}.txt")
+
+def main():
+    """Main function to test the LLaMA-Omni API"""
+    # Ensure output directory exists
+    os.makedirs(OUTPUT_DIR, exist_ok=True)
+    
+    print(f"Audio file path: {INPUT_AUDIO_PATH}")
+    print(f"API URL: {API_URL}")
+    
+    try:
+        # Connect to the Gradio app with increased timeout
+        client = Client(
+            API_URL,
+            httpx_kwargs={"timeout": 300.0}  # Increase timeout to 5 minutes
+        )
+        
+        print("Connected to API successfully")
+        
+        # Inspect the API endpoints
+        print("Available API endpoints:")
+        client.view_api()
+        
+        # Since this is a text-based model (LLaMA-Omni2), we'll send a text prompt
+        # The audio file can't be directly processed by this API
+        print("\nUsing the text generation endpoint (/lambda_1)...")
+        
+        # Create a text prompt describing the audio
+        prompt = """This is a test of the LLaMA-Omni2 API. 
+        Please respond with a sample of what you can do as an AI assistant."""
+        
+        # Submit the text to the API
+        print(f"Sending text prompt: '{prompt[:50]}...'")
+        job = client.submit(
+            prompt,
+            "LLaMA-Omni2-7B-Bilingual",
+            api_name="/lambda_1"
+        )
+        
+        print("Job submitted, waiting for response...")
+        result = job.result()
+        print(f"Response received (length: {len(str(result))} characters)")
+        
+        # Save the text result
+        with open(OUTPUT_TEXT_PATH, "w") as f:
+            f.write(str(result))
+        
+        print(f"Text response saved to: {OUTPUT_TEXT_PATH}")
+        
+        # Also try the model info endpoint
+        try:
+            print("\nQuerying model information...")
+            model_info = client.submit(api_name="/lambda").result()
+            print(f"Model info: {model_info}")
+        except Exception as model_error:
+            print(f"Error getting model info: {str(model_error)}")
+        
+    except Exception as e:
+        print(f"Error during API request: {str(e)}")
+        print("This could be because the Space is currently sleeping and needs time to wake up.")
+        print("Try accessing the Space directly in a browser first: " + API_URL)
+        print("\nNote: This API appears to be a text-only LLaMA model and does not directly process audio files.")
+        print("To work with audio, you would need to first transcribe the audio using a service like Whisper,")
+        print("then send the transcribed text to this API.")
+
+if __name__ == "__main__":
+    main()