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import streamlit as st |
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from datasets import load_dataset |
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import pandas as pd |
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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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make_choice = st.selectbox('Select the Patent Application Number:', PAN) |
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form = st.form(key='patent-form') |
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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.header(':red[Patent Application 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.header(':red[Patent Application Claims]') |
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st.info(claims) |
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