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| import streamlit as st | |
| import pandas as pd | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| import torch | |
| from datasets import load_dataset | |
| # Load model and tokenizer | |
| model_path = "rb757/new_app" | |
| model = AutoModelForSequenceClassification.from_pretrained(model_path) | |
| tokenizer = AutoTokenizer.from_pretrained(model_path) | |
| # Load the dataset | |
| dataset_dict = load_dataset( | |
| 'HUPD/hupd', | |
| name='sample', | |
| data_files="https://huggingface.co/datasets/HUPD/hupd/resolve/main/hupd_metadata_2022-02-22.feather", | |
| 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', | |
| trust_remote_code=True | |
| ) | |
| # Convert to DataFrame | |
| train_df = pd.DataFrame(dataset_dict['train']) | |
| val_df = pd.DataFrame(dataset_dict['validation']) | |
| # Print columns to verify availability | |
| print("Train set columns:", train_df.columns.tolist()) | |
| print("Validation set columns:", val_df.columns.tolist()) | |
| # Check if 'patent_number' exists | |
| if 'patent_number' not in train_df.columns: | |
| st.error("Column 'patent_number' not found in the training dataset.") | |
| else: | |
| # Title and description | |
| st.title("📜 Milestone Patent Evaluation") | |
| st.write("Select a patent application to evaluate its patentability.") | |
| # Dropdown for patent numbers | |
| patent_numbers = train_df['patent_number'].unique() | |
| selected_patent = st.selectbox("Select Patent Number", patent_numbers) | |
| # Retrieve relevant information | |
| patent_info = train_df[train_df['patent_number'] == selected_patent].iloc[0] | |
| title = patent_info['title'] | |
| abstract = patent_info['abstract'] | |
| claims = patent_info['claims'] | |
| background = patent_info['background'] | |
| summary = patent_info['summary'] | |
| description = patent_info['description'] | |
| cpc_label = patent_info['cpc_label'] | |
| ipc_label = patent_info['ipc_label'] | |
| filing_date = patent_info['filing_date'] | |
| patent_issue_date = patent_info['patent_issue_date'] | |
| date_published = patent_info['date_published'] | |
| examiner_id = patent_info['examiner_id'] | |
| # Display the information | |
| st.markdown("### Title") | |
| st.markdown(f"**{title}**") | |
| st.markdown("---") | |
| st.markdown("### Abstract") | |
| st.text_area("Abstract", abstract, height=150) | |
| st.markdown("---") | |
| st.markdown("### Claims") | |
| st.text_area("Claims", claims, height=150) | |
| st.markdown("---") | |
| st.markdown("### Background") | |
| st.text_area("Background", background, height=150) | |
| st.markdown("---") | |
| st.markdown("### Summary") | |
| st.text_area("Summary", summary, height=150) | |
| st.markdown("---") | |
| st.markdown("### Description") | |
| st.text_area("Description", description, height=150) | |
| st.markdown("---") | |
| st.markdown("### CPC Label") | |
| st.markdown(f"**{cpc_label}**") | |
| st.markdown("### IPC Label") | |
| st.markdown(f"**{ipc_label}**") | |
| st.markdown("### Filing Date") | |
| st.markdown(f"**{filing_date}**") | |
| st.markdown("### Patent Issue Date") | |
| st.markdown(f"**{patent_issue_date}**") | |
| st.markdown("### Date Published") | |
| st.markdown(f"**{date_published}**") | |
| st.markdown("### Examiner ID") | |
| st.markdown(f"**{examiner_id}**") | |
| # Submit button | |
| if st.button("Get Patentability Score"): | |
| # Prepare the input text | |
| input_text = f"{title} {abstract} {claims} {background} {summary} {description}" | |
| inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True) | |
| # Get the model prediction | |
| with torch.no_grad(): | |
| logits = model(**inputs).logits | |
| predictions = torch.argmax(logits, dim=-1) | |
| # Display the patentability score | |
| decision_labels = ['REJECTED', 'ACCEPTED', 'PENDING', 'CONT-REJECTED', 'CONT-ACCEPTED', 'CONT-PENDING'] | |
| score = decision_labels[predictions.item()] | |
| st.success(f"Patentability Score: **{score}**") | |