Update app.py

app.py

@@ -1,7 +1,8 @@
 import streamlit as st
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
 from datasets import load_dataset
-
+### Load HUPD dataset
+# Sample a small subset of the dataset that corresponds to all patent applications submitted in Jan 2016.
 dataset = load_dataset('HUPD/hupd',
     name='sample',
     data_files="https://huggingface.co/datasets/HUPD/hupd/blob/main/hupd_metadata_2022-02-22.feather", 
@@ -11,7 +12,9 @@ dataset = load_dataset('HUPD/hupd',
     val_filing_start_date='2016-01-30',
     val_filing_end_date='2016-01-31',
 )
+### Exclude pending applications (also removed in finetuned model)
 exclude_pending = dataset.filter(lambda example: example["decision"] != "PENDING")
+### Extract necessary data lists
 data = exclude_pending["validation"]
 p_number = data["patent_number"]
 p_abstract = data["abstract"]
@@ -20,7 +23,7 @@ p_decision = data["decision"]
 abstract = ""
 claims = ""
 decision = ""
-# Streamlit app
+### Streamlit app
 st.title("Patentability Score")
 st.write("Select a patent ID and click submit to get patentability score.")
 selected_id = st.selectbox("Patent ID:", p_number, index=len(p_number)-1)
@@ -28,26 +31,26 @@ selected_index = p_number.index(selected_id)
 abstract = p_abstract[selected_index]
 claims = p_claims[selected_index]
 decision = p_decision[selected_index]
-
 st.text_area("Abstract:", abstract)
 st.text_area("Claims:", claims)
-
+### Load model and tokenizer
 def get_pipeline():
     model = AutoModelForSequenceClassification.from_pretrained("moonahhyun/project-uspto")
     tokenizer = AutoTokenizer.from_pretrained("moonahhyun/project-uspto")
     pl = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
     return pl
-
-# Load the model and perform sentiment analysis
+### Perform patentability analysis
 if st.button("Submit"):
-    with st.spinner("Analyzing the patent claims..."):
+    with st.spinner("Analyzing the patentability..."):
         pl = get_pipeline()
         result = pl(abstract)
         label = result[0]['label']
         score = result[0]['score']
+        # Print score when label = accepted
         if label == "ACCEPTED":
             st.write(f"Patentability Score: {score}")
             st.write(f"Higher % of being: {label}")
+        # Print 1 - score when label = rejected
         else: 
             score = 1 - score
             st.write(f"Patentability Score: {score}")