implemented patent classifier

app_pt.py

@@ -0,0 +1,59 @@
+import streamlit as st
+import torch
+from datasets import load_dataset
+from transformers import AutoTokenizer 
+from transformers import AutoModelForSequenceClassification
+from transformers import pipeline
+
+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',
+)
+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)
+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")
+applications = {}
+
+for ds_index, example in enumerate(dataset):
+    applications.update({example['patent_number']: ds_index })
+
+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")
+
+patent_number = st.selectbox("Select a patent application:", applications, on_change=load_patent, key="id")
+title = st.text_area("Title", key="title", value=dataset[0]['title'], height=50)
+
+with st.form('Details'):
+    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))
+
+
+
+
+