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| import gradio as gr | |
| from docarray import Document | |
| from jina import Flow | |
| import clip_server | |
| from clip_client import Client | |
| import asyncio | |
| def setupServer(): | |
| cas_path = clip_server.__path__[0] | |
| flow_yaml = f''' | |
| jtype: Flow | |
| with: | |
| protocol: grpc | |
| port: 51000 | |
| executors: | |
| - name: clip_t | |
| uses: | |
| jtype: CLIPEncoder | |
| metas: | |
| py_modules: | |
| - {cas_path}/executors/clip_torch.py | |
| ''' | |
| f = Flow.load_config(flow_yaml) | |
| f.start() | |
| def setupClient(): | |
| c = Client('https://demo-cas.jina.ai:8443') | |
| return c | |
| # setupServer() | |
| c = setupClient() | |
| def rank(uri, options): | |
| r = c.rank([ | |
| Document( | |
| uri=uri, | |
| matches=[ | |
| Document(text=f'a photo of a {p}') | |
| for p in options.split(',') | |
| ], | |
| ) | |
| ]) | |
| return r['@m', ['text', 'scores__clip_score__value']] | |
| examples = [["https://picsum.photos/id/102/300/300", "three berry, four berries, ten berries"]] | |
| iface = gr.Interface(fn=rank, inputs=["text", "text"], outputs="text", examples=examples) | |
| iface.launch() |