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import vllm
import torch
import gradio
import huggingface_hub
huggingface_hub.login(token=HF_TOKEN)
# Fava prompt
INPUT = "Read the following references:\n{evidence}\nPlease identify all the errors in the following text using the information in the references provided and suggest edits if necessary:\n[Text] {output}\n[Edited] "
model = vllm.LLM(model="uw-llm-factuality/FAVA")
def result(passage, reference):
prompt = [INPUT.format_map({"evidence":reference, "output":passage})]
print(prompt)
print("\n")
sampling_params = vllm.SamplingParams(
temperature=0,
top_p=1.0,
max_tokens=500,
)
outputs = model.generate(prompt, sampling_params)
outputs = [it.outputs[0].text for it in outputs]
output = outputs[0].replace("<mark>", "<span style='color: green; font-weight: bold;'> ")
output = output.replace("</mark>", " </span>")
output = output.replace("<delete>", "<span style='color: red; text-decoration: line-through;'>")
output = output.replace("</delete>", "</span>")
output = output.replace("<entity>", "<span style='background-color: #E9A2D9; border-bottom: 1px dotted;'>entity</span>")
output = output.replace("<relation>", "<span style='background-color: #F3B78B; border-bottom: 1px dotted;'>relation</span>")
output = output.replace("<contradictory>", "<span style='background-color: #FFFF9B; border-bottom: 1px dotted;'>contradictory</span>")
output = output.replace("<unverifiable>", "<span style='background-color: #B2DCE5; border-bottom: 1px dotted;'>unverifiable</span>")
output = output.replace("<invented>", "<span style='background-color: #BFE9B9; border-bottom: 1px dotted;'>invented</span>")
output = output.replace("<subjective>", "<span style='background-color: #B5B7F5; border-bottom: 1px dotted;'>subjective</span>")
output = output.replace("</entity>", "")
output = output.replace("</relation>", "")
output = output.replace("</contradictory>", "")
output = output.replace("</unverifiable>", "")
output = output.replace("</invented>", "")
output = output.replace("</subjective>", "")
output = output.replace("Edited:", "")
return f'<div style="font-weight: normal;">{output}</div>'; #output;
if __name__ == "__main__":
demo = gradio.Interface(fn=result, inputs=["text", "text"], outputs="html")
demo.launch(share=True)
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