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import streamlit as st |
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from datasets import load_dataset |
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from transformers import pipeline |
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import pandas as pd |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline |
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from datasets import load_dataset |
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dataset_dict = load_dataset('HUPD/hupd', |
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name='sample', |
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data_files="https://huggingface.co/datasets/HUPD/hupd/blob/main/hupd_metadata_2022-02-22.feather", |
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icpr_label=None, |
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train_filing_start_date='2016-01-01', |
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train_filing_end_date='2016-01-31', |
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val_filing_start_date='2017-01-22', |
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val_filing_end_date='2017-01-31', |
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) |
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df = pd.DataFrame.from_dict(dataset_dict["train"]) |
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df = pd.DataFrame(df,columns =['patent_number','decision', 'abstract', 'claims','filing_date']) |
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PAN = df['patent_number'].drop_duplicates() |
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st.title('Harvard USPTO Patentability Score') |
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with st.form("patent-form"): |
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make_choice = st.selectbox('Select the Patent Application Number:', PAN) |
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submitted = st.form_submit_button(label='submit') |
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if submitted: |
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model_name = "distilbert-base-uncased-finetuned-sst-2-english" |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) |
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decision = df['decision'].loc[df['patent_number'] == make_choice] |
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X_train = decision.to_string() |
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results = classifier(X_train, truncation=True) |
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for result in results: |
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print(result) |
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score = result['score'] |
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print(score) |
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st.write("The Patentability Score is:", score) |
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pd.options.display.max_colwidth = 100000 |
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abstract = df["abstract"].loc[df["patent_number"] == make_choice] |
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st.subheader(':red[Patent Application]') |
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st.subheader(':red[Abstract:]') |
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st.info(abstract) |
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claims = df["claims"].loc[df["patent_number"] == make_choice] |
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st.subheader(':red[Claim:]') |
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st.info(claims) |
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