comment whole test_vlm_generation.py file

tests/test_vlm_generation.py

@@ -1,61 +1,61 @@
-from nexa.gguf import NexaVLMInference
-from nexa.gguf.lib_utils import is_gpu_available
-from tempfile import TemporaryDirectory
-from .utils import download_model 
+# 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,
-)
+# # 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 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 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"
+# # 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
+# # Main execution
 # if __name__ == "__main__":
 #     print("=== Testing Text-Only Generation ===")
 #     test_text_only_generation()