wehub-resource-sync
62 lines
1.4 KiB
Python
62 lines
1.4 KiB
Python
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""" This example illustrates the use of Vision models with OpenVINO, to convert
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an image with text instruction -> text generation output.
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Prereqs:
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-- pip install openvino_genai
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-- pip install PIL (Pillow library for image preparation)
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If you want to use the web streamer:
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-- pip install pywebio
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"""
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from llmware.models import ModelCatalog
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import openvino_genai as ovg
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try:
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from pywebio.output import put_text
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except:
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pass
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# this is the default streamer included in the OVGenerativeModel class -
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# if no streamer explicitly passed, then this will be used
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def ov_default_streamer(x):
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print(x, end="", flush=True)
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return ovg.StreamingStatus.RUNNING
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# here is a simple example that will stream the text to local host
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# to run this example: `pip install pywebio`
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def web_streamer(x):
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put_text(x,inline=True)
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return ovg.StreamingStatus.RUNNING
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# supported OpenVINO vision models in llmware:
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# -- qwen2.5-vl-3b-ov
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# -- gemma-3-4b-ov
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# -- phi-3.5-vision-ov
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# -- phi-4-mm-ov
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model = ModelCatalog().load_model("phi-4-mm-ov",
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max_output=500, device="GPU")
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image_path = "C:\\Users\\path\\to\\image_file"
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prompt = "Describe this image."
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import time
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t0=time.time()
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response = model.stream(prompt, image_path, streamer=ov_default_streamer)
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t1= time.time()
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print("\n\ncompleted streaming response - ", response)
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print("\ntime taken: ", t1-t0)
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