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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))
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