import streamlit as st
from transformers import pipeline
from datasets import load_dataset

# load the dataset and
# use the patent number, abstract and claim columns for UI
with st.spinner("Setting up the app..."):
    dataset_dict = load_dataset(
        "HUPD/hupd",
        name="sample",
        data_files="https://huggingface.co/datasets/HUPD/hupd/blob/main/hupd_metadata_2022-02-22.feather",
        icpr_label=None,
        train_filing_start_date="2016-01-01",
        train_filing_end_date="2016-01-21",
        val_filing_start_date="2016-01-22",
        val_filing_end_date="2016-01-31",
    )

    # widget for selecting our finetuned langugae model
    language_model_path = "juliaannjose/finetuned_model"

# pass the model to transformers pipeline - model selection component.
classifier_model = pipeline(model=language_model_path)

# drop down menu with patent numbers
_patent_id = st.selectbox(
    "Select the Patent Number",
    dataset_dict["train"]["patent_number"],
)

if _patent_id:
    # get abstract and claim corresponding to this patent id
    _abstract = dataset_dict["train"][["patent_number"] == _patent_id]["abstract"]
    _claim = dataset_dict["train"][["patent_number"] == _patent_id]["claims"]

    # display abstract and claim
    st.write(_abstract)
    st.write(_claim)

# when submit button clicked, run the model and get result
if st.button("Submit"):
    results = classifier_model([_abstract + _claim])
    st.write(results)