milestone-3.py

import streamlit as st
import torch
from datasets import load_dataset
from transformers import AutoTokenizer 
from transformers import AutoModelForSequenceClassification
from transformers import pipeline

# Load HUPD dataset
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',
)

# Process data
filtered_dataset = dataset_dict['validation'].filter(lambda e: e['decision'] == 'ACCEPTED' or e['decision'] == 'REJECTED')
dataset = filtered_dataset.shuffle(seed=42).select(range(20))
dataset = dataset.sort("patent_number")


# Create pipeline using model trainned on Colab
model = torch.load("/workspaces/cs-gy-6613-project/patent_classification(1).pt", map_location=torch.device('cpu'))
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)



def load_patent():
    selected_application = dataset.select([applications[st.session_state.id]])
    st.session_state.abstract = selected_application['abstract'][0]
    st.session_state.claims = selected_application['claims'][0]
    st.session_state.title = selected_application['title'][0]



st.title("CS-GY-6613 Project Milestone 3")

# List patent numbers for select box
applications = {}
for ds_index, example in enumerate(dataset):
    applications.update({example['patent_number']: ds_index })
st.selectbox("Select a patent application:", applications, on_change=load_patent, key="id")

# Application title displayed for additional context only, not used with model
st.text_area("Title", key="title", value=dataset[0]['title'], height=50)

# Classifier input form
with st.form('Input Form'):
    abstract = st.text_area("Abstract", key="abstract", value=dataset[0]['abstract'], height=200)
    claims = st.text_area("Claims", key="claims", value=dataset[0]['abstract'], height=200)
    submitted = st.form_submit_button("Get Patentability Score")

if submitted:
    selected_application = dataset.select([applications[st.session_state.id]])
    res = classifier(abstract, claims)
    if res[0]["label"] == 'LABEL_0':
        pred = "ACCEPTED"
    elif res[0]["label"] == 'LABEL_1': 
        pred = "REJECTED"
    score = res[0]["score"]
    label = selected_application['decision'][0]
    result = st.markdown("This text was classified as  **{}** with a confidence score of **{}**.".format(pred, score))
    check = st.markdown("Actual Label: **{}**.".format(label))