add new features
Browse files- .github/workflows/main.yml +1 -1
- app.py +27 -5
- flagged/log.csv +4 -0
.github/workflows/main.yml
CHANGED
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name: Sync to Hugging Face hub
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on:
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branches: [main]
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# to run this workflow manually from the Actions tab
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name: Sync to Hugging Face hub
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on:
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push:
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branches: [main]
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# to run this workflow manually from the Actions tab
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app.py
CHANGED
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from transformers import pipeline
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import gradio as gr
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return summary
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with gr.Blocks() as demo:
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textbox = gr.Textbox(placeholder = "Enter text block to summarize", lines = 4)
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gr.
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demo.launch()
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from transformers import pipeline
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import gradio as gr
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model_name_converter = {"bart_large":"juliosocher/bart-large-cnn-finetuned-scientific-articles",
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"mt5-small-finetuned-mt5":"jacks392/mt5-small-finetuned-mt5",
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"facebook": "facebook/bart-large-cnn",
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"google" : "google/pegasus-xsum"
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}
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def predict(prompt,model_name, max_length):
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if model_name ==None:
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model_name = "google/pegasus-xsum"
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else:
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model_name = model_name_converter[model_name]
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print('la')
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print(model_name)
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print(max_length)
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model = pipeline("summarization",model = model_name)
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summary = model(prompt,max_length)[0]["summary_text"]
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return summary
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def extract_model(option):
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if option ==None:
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model_name = "google/pegasus-xsum"
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else:
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model_name = model_name_converter[option]
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return print(model_name)
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options_1 = model_name_converter.keys()
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with gr.Blocks() as demo:
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drop_down = gr.Dropdown(choices=options_1, label="model")
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textbox = gr.Textbox(placeholder = "Enter text block to summarize", lines = 4)
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length=gr.Number(value = 200, label="the max number of characher for summerized")
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gr.Interface(fn=predict, inputs=[textbox, drop_down, length], outputs = "text")
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demo.launch()
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flagged/log.csv
ADDED
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prompt,output,flag,username,timestamp
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,a fff f s . fs ft fd fn fl .,,,2024-06-08 11:22:28.177838
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,a fff f s . fs ft fd fn fl .,,,2024-06-08 11:22:30.198869
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,bart_large,200,,,,2024-06-08 11:50:37.097705
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