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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("patent_classifier_v2.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
)
)
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