removed old classifier

milestone-3.py

@@ -1,67 +1,77 @@
 import streamlit as st
 import torch
 from datasets import load_dataset
-from transformers import AutoTokenizer 
+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", 
+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',
+    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')
+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.pt", map_location=torch.device('cpu'))
+model = torch.load("patent_classifier.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.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")
+    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)
+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)
+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':
+    if res[0]["label"] == "LABEL_0":
         pred = "ACCEPTED"
-    elif res[0]["label"] == 'LABEL_1': 
+    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))
+    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))
-
-
-
-
-