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| import streamlit as st | |
| import pandas as pd | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import torch | |
| import torch.nn.functional as F | |
| def predictScore(abstract): | |
| model_dir = 'model-patent-score' | |
| tokenizer = AutoTokenizer.from_pretrained(model_dir) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_dir) | |
| #abstract = "The present invention relates to passive optical network (PON), and in particular, to an optical network terminal (ONT) in the PON system. In one embodiment, the optical network terminal includes a first interface coupled to a communications network, a second interface coupled to a network client and a processor including a memory coupled to the first interface and to the second interface, wherein the processor is capable of converting optical signals to electric signals, such that the network client can access the communications network." | |
| inputs = tokenizer.encode_plus(abstract, max_length=512, padding='max_length', truncation=True, return_tensors='pt') | |
| outputs = model(inputs['input_ids'], attention_mask=inputs['attention_mask']) | |
| scores = torch.softmax(outputs.logits, dim=1)[0] | |
| probs = F.softmax(scores, dim=0) | |
| accept_prob = probs[0].item() | |
| print(accept_prob) | |
| return accept_prob | |
| def main(): | |
| df = pd.read_pickle("my_dataframe.pkl") | |
| df = df.iloc[:50] | |
| title_list = df['title'].tolist() | |
| pnumber_list = df['patent_number'].tolist() | |
| display_list = [f"{a}-{b}" for a, b in zip(pnumber_list, title_list)] | |
| # Prepopulate the drop-down menu with the default value 'Option 2' | |
| default_option = display_list[0] | |
| # Create the drop-down menu with prepopulated default value | |
| selected_option = st.selectbox('Select a patent number which is displayed along with its title', display_list, index=display_list.index(default_option)) | |
| st.write(selected_option) | |
| pnumber = selected_option.split("-")[0] | |
| print(pnumber) | |
| pclaims = df.loc[df['patent_number'] == str(pnumber), 'claims'].iloc[0] | |
| pabstract = df.loc[df['patent_number'] == str(pnumber), 'abstract'].iloc[0] | |
| score = predictScore(pabstract) | |
| st.write(pclaims) | |
| st.write(pabstract) | |
| with st.form("my_form"): | |
| submit_button = st.form_submit_button(label='Submit') | |
| if submit_button: | |
| st.write("The patentability score of this patent: ", score) | |
| if __name__ == '__main__': | |
| main() |