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| from nubia_score import Nubia | |
| import gradio | |
| nubia = Nubia() | |
| def predict(inp_1, inp_2): | |
| features = nubia.score(inp_1, inp_2, get_features=True) | |
| labels = {k: v for k, v in features["features"].items()} | |
| return {"nubia_score": features["nubia_score"]}, labels | |
| title = "NUBIA" | |
| description = "NeUral Based Interchangeability Assessor. A SoTA evaluation metric for text generation." | |
| inputs = [gradio.inputs.Textbox(label="First Text"), gradio.inputs.Textbox(label="Second Text")] | |
| outputs = [gradio.outputs.Label(label="Interchangeability Score"), gradio.outputs.JSON(label="All Features")] | |
| examples = [ | |
| ["This car is expensive! I can't buy it.", "That automobile costs a fortune! Purchasing it? Impossible!"], | |
| ["This car is expensive! I can't buy it.", "That automobile costs a good amount. Purchasing it? Totally feasible!"], | |
| ["The dinner was delicious.", "The dinner did not taste good."] | |
| ] | |
| iface = gradio.Interface(fn=predict, inputs=inputs, outputs=outputs, capture_session=True, examples=examples, | |
| title=title, description=description, allow_flagging=False) | |
| iface.launch() | |