Spaces:
Runtime error
Runtime error
File size: 3,987 Bytes
69c1e4c f449d52 1d7f828 69c1e4c f449d52 69c1e4c 1d7f828 f449d52 f7c668b f449d52 1d7f828 f99e432 8bf28d6 1d7f828 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
import gradio as gr
from transformers import pipeline
import pandas as pd
import json
pipe = pipeline("summarization", model="Gabriel/bart-base-cnn-xsum-swe")
def generate(in_text):
print(in_text)
answer = pipe(in_text, num_beams=5 ,min_length=20, max_length=120)
print(answer)
return answer[0]["summary_text"]
def update_history(df, in_text, gen_text ,generation_type, parameters):
# get rid of first seed phrase
new_row = [{"In_text": in_text,
"Gen_text": gen_text,
"Generation Type": generation_type,
"Parameters": json.dumps(parameters)}]
return pd.concat([df, pd.DataFrame(new_row)])
def generate_transformer(in_text, num_beams ,history):
gen_text= generate(in_text)
return gen_text, update_history(history, in_text, gen_text, "Transformer", {"num_beams": num_beams})
with gr.Blocks() as demo:
gr.Markdown("""# Summarization Engine!""")
with gr.Accordion("See Details", open=False):
gr.Markdown("lorem ipsum")
with gr.Tabs():
with gr.TabItem("Transformer Generation"):
gr.Markdown(
"""The default parameters for distilgpt2 work well to generate moves. Use this tab as
a baseline for your experiments.""")
with gr.Row():
with gr.Column(scale=4):
text_baseline_transformer= gr.Textbox(lines=4,label="Input Text", placeholder="hej hej",)
with gr.Column(scale=3):
with gr.Row():
num_beams = gr.Slider(minimum=2, maximum=10, value=2, step=1, label="Number of Beams2")
output_basline_transformer = gr.Textbox(label="Output Text")
transformer_button = gr.Button("Summarize!")
# with gr.TabItem("Strong Baseline"):
# gr.Markdown(
# """The default parameters for distilgpt2 work well to generate moves. Use this tab as
# a baseline for your experiments.""")
# with gr.Row():
# with gr.Column(scale=4):
# text_baseline= gr.Textbox(lines=4,label="Input Text", placeholder="hej hej",)
# with gr.Column(scale=3):
# with gr.Row():
# num_beams2 = gr.Slider(minimum=2, maximum=10, value=2, step=1, label="Number of Beams2")
# num_beams3 = gr.Slider(minimum=2, maximum=10, value=2, step=1, label="Number of Beams3")
# output_basline = gr.Textbox(label="Output Text")
# baseline_button = gr.Button("Summarize!")
# with gr.TabItem("LexRank"):
# gr.Markdown(
# """The default parameters for distilgpt2 work well to generate moves. Use this tab as
# a baseline for your experiments.""")
# with gr.Row():
# label="Number of Beams")
# text_baseline= gr.Textbox(label="Input Text", placeholder="hej hej",)
# output_basline = gr.Textbox(label="Output Text")
# baseline_button = gr.Button("Summarize!")
gr.Examples([["hi", 5]], [text_baseline_transformer, num_beams])
with gr.Box():
gr.Markdown("<h3> Generation History <h3>")
# Displays a dataframe with the history of moves generated, with parameters
history = gr.Dataframe(headers=["In_text", "Gen_text", "Generation Type", "Parameters"], overflow_row_behaviour="show_ends", wrap=True)
with gr.Box():
gr.Markdown("<h3>How did you make this?<h3>")
# gr.Markdown("""hej bottom.""")
transformer_button.click(generate_transformer, inputs=[text_baseline_transformer, num_beams ,history], outputs=[output_basline_transformer , history] )
# baseline_button.click(generate_transformer, inputs=[text_baseline, num_beams2 ,history], outputs=[output_basline,history] )
demo.launch() |