add test_vlm_generation.py for ci

tests/test_vlm_generation.py

@@ -0,0 +1,67 @@
+# from nexa.gguf import NexaVLMInference
+# from nexa.gguf.lib_utils import is_gpu_available
+# from tempfile import TemporaryDirectory
+# from .utils import download_model 
+
+# # Initialize the model
+# model = NexaVLMInference(
+#     model_path="nanollava",
+#     verbose=False,
+#     n_gpu_layers=-1 if is_gpu_available() else 0,
+# )
+
+# # Test VLM generation without an image
+# def test_text_only_generation():
+#     output = model.create_chat_completion(
+#         messages=[
+#             {"role": "user", "content": "What is the capital of France?"}
+#         ],
+#         max_tokens=100,
+#         stream=False
+#     )
+#     response = output["choices"][0]["message"]["content"]
+#     print("Text-only response:", response)
+#     assert "Paris" in response, "The response should mention Paris"
+
+# # Test VLM generation with an image
+# def test_image_description():
+#     img_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
+#     with TemporaryDirectory() as temp_dir:
+#         img_path = download_model(img_url, temp_dir)
+#         user_input = "Describe this image in detail."
+#         output = model._chat(user_input, img_path)
+#         response = ""
+#         for chunk in output:
+#             delta = chunk["choices"][0]["delta"]
+#             if "content" in delta:
+#                 response += delta["content"]
+#         print("Image description:", response)
+#         assert len(response) > 50, "The image description should be detailed"
+
+# # Test streaming output
+# def test_streaming_output():
+#     global model
+#     messages = [
+#         {"role": "user", "content": "Write a short story about a robot learning to paint."}
+#     ]
+#     output = model.create_chat_completion(messages=messages, max_tokens=200, stream=True)
+#     story = ""
+#     for chunk in output:
+#         if "choices" in chunk and len(chunk["choices"]) > 0:
+#             delta = chunk["choices"][0]["delta"]
+#             if "content" in delta:
+#                 story += delta["content"]
+#                 print(delta["content"], end="", flush=True)
+#     print("\nFull story:", story)
+#     assert len(story) > 100, "The generated story should be of substantial length"
+
+# # Main execution
+# if __name__ == "__main__":
+#     print("=== Testing Text-Only Generation ===")
+#     test_text_only_generation()
+
+#     print("\n=== Testing Image Generation ===")
+#     test_image_description()
+
+#     print("\n=== Testing Streaming Output ===")
+#     test_streaming_output()