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yabramuvdi
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ba06738
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Parent(s):
97c6f77
Create app.py
Browse files
app.py
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import torch
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import gradio
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from transformers import TextClassificationPipeline, DistilBertTokenizer, DistilBertForSequenceClassification
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# model path in hugginface
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model_path = "yabramuvdi/distilbert-wfh"
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tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
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model = DistilBertForSequenceClassification.from_pretrained(model_path)
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# create a pipeline for predictions
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classifier = TextClassificationPipeline(model=model,
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tokenizer=tokenizer,
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return_all_scores=True)
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# basic elements of page
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title = "Work From Home Predictor"
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description = "Demo application that predicts the pressence of work from home in any given sequence of text."
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article = "" # text at the end of the app
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examples = [
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["This is a work from home position", 0.998],
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["This position does not allow working from home", 0.998],
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]
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#%%
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def predict_wfh(input_text, input_slider):
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# get scores from model
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predictions = classifier(input_text)[0]
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# use selected threshold to classify as WFH
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prob_wfh = predictions[1]["score"]
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if prob_wfh > input_slider:
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wfh = 1
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no_wfh = 0
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else:
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wfh = 0
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no_wfh = 1
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return({"Not work from home": no_wfh, "Work from home": wfh}, f"Probability of WFH: {np.round(prob_wfh, 3)}")
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label = gr.outputs.Label(num_top_classes=1, type="confidences", label="Binary classification")
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text_output = gr.outputs.Textbox(type="auto", label="Predicted probability")
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app = gr.Interface(fn=[predict_wfh],
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inputs=[gr.inputs.Textbox(lines=10, label="Input text"), gr.inputs.Slider(0, 1, 0.001, label="Classification threshold")],
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outputs=[label, text_output],
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theme="huggingface",
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title=title,
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description=description,
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article=article,
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examples=examples,
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allow_flagging="manual",
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flagging_options=["mistake", "borderline"]
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)
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app.launch()
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