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elibrowne
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Parent(s):
919be7b
Initial commit for Gradio app
Browse files
app.py
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import gradio as gr
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return "Hello " + name + "!!"
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import gradio as gr
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# VARIABLES: will eventually be loaded with JSON from a dataset
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question_text = """
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### Bar Question
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What is the answer to this question?"""
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answers_text = ["A", "B", "C", "D"]
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# BLOCKS: main user interface
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with gr.Blocks() as user_eval:
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# Title text introducing study
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gr.Markdown("""
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# Legal Retriever Evaluation Study
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Thank you for your participation! Here are some basic instructions on how to complete the legal study.
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""")
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# Passages and user evaluations thereof
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with gr.Row(equal_height = False, visible = False) as evals:
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with gr.Column(scale = 2) as passages:
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passage_display = gr.Markdown("""
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### Relevant Passages
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- Dataset 1
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- Dataset 2
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- More text
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- More text
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- More text
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- More text
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### Auto-Generated Summary
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This is a summary of the above legal passages, which imitates how a RAG system might \
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encorporate retrieved data into its context to give a better response to a certain query.
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""")
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with gr.Column(scale = 1) as scores:
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desc_1 = gr.Markdown("How **relevant** are these passages to our query?")
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eval_1 = gr.Slider(1, 5)
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desc_2 = gr.Markdown("How **novel** are these passages compared to the previous passages?")
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eval_2 = gr.Slider(1, 5)
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btn = gr.Button("Next")
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# btn.click(fn=update, inputs=inp, outputs=eval_1)
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# Question and answering dynamics
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with gr.Row() as question:
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with gr.Column():
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gr.Markdown(question_text)
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a = gr.Button(answers_text[0])
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b = gr.Button(answers_text[1])
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c = gr.Button(answers_text[2])
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d = gr.Button(answers_text[3])
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def answer():
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return {
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question: gr.Row(visible = False),
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evals: gr.Row(visible = True)
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}
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a.click(fn = answer, outputs = [question, evals])
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b.click(fn = answer, outputs = [question, evals])
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c.click(fn = answer, outputs = [question, evals])
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d.click(fn = answer, outputs = [question, evals])
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# Starts on question, switches to evaluation after the user answers
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user_eval.launch()
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