src/main.py

import os
import json
import random

import streamlit as st
from transformers import TextClassificationPipeline, pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification, DistilBertTokenizerFast, DistilBertForSequenceClassification

emotion_model_names = (
    "cardiffnlp/twitter-roberta-base-sentiment",
    "finiteautomata/beto-sentiment-analysis",
    "bhadresh-savani/distilbert-base-uncased-emotion",
    "siebert/sentiment-roberta-large-english"
)

class ModelImplementation(object):
    def __init__(
            self, 
            transformer_model_name, 
            model_transformer, 
            tokenizer_model_name,
            tokenizer_func, 
            pipeline_func, 
            parser_func,
            classifier_args={},
            placeholders=[""]
    ):
        self.transformer_model_name = transformer_model_name
        self.tokenizer_model_name = tokenizer_model_name
        self.placeholders = placeholders

        self.model = model_transformer.from_pretrained(self.transformer_model_name)
        self.tokenizer = tokenizer_func.from_pretrained(self.tokenizer_model_name)
        self.classifier = pipeline_func(model=self.model, tokenizer=self.tokenizer, padding=True, truncation=True, **classifier_args)
        self.parser = parser_func
    
    def predict(self, val):
        result = self.classifier(val)
        return self.parser(self, result)

def ParseEmotionOutput(self, result):
    label = result[0]['label']
    score = result[0]['score']
    output_func = st.info
    if self.transformer_model_name == "cardiffnlp/twitter-roberta-base-sentiment":
        if label == "LABEL_0":
            label = "NEGATIVE"
            output_func = st.error
        elif label == "LABEL_2":
            label = "POSITIVE"
            output_func = st.success
        else:
            label = "NEUTRAL"
    elif self.transformer_model_name == "finiteautomata/beto-sentiment-analysis":
        if label == "NEG":
            label = "NEGATIVE"
            output_func = st.error
        elif label == "POS":
            label = "POSITIVE"
            output_func = st.success
        else:
            label = "NEUTRAL"
    elif self.transformer_model_name == "bhadresh-savani/distilbert-base-uncased-emotion":
        if label == "sadness":
            output_func = st.info
        elif label == "joy":
            output_func = st.success
        elif label == "love":
            output_func = st.success
        elif label == "anger":
            output_func = st.error
        elif label == "fear":
            output_func = st.info
        elif label == "surprise":
            output_func = st.error
        label = label.upper()
    elif self.transformer_model_name == "siebert/sentiment-roberta-large-english":
        if label == "NEGATIVE":
            output_func = st.error
        elif label == "POSITIVE":
            output_func = st.success
    return label, score, output_func

def ParsePatentOutput(self, result):
    return result

def emotion_model_change():
    st.session_state.emotion_model = ModelImplementation(
        st.session_state.emotion_model_name,
        AutoModelForSequenceClassification,
        st.session_state.emotion_model_name,
        AutoTokenizer,
        pipeline,
        ParseEmotionOutput,
        classifier_args={ "task" : "sentiment-analysis" },
        placeholders=["@AmericanAir just landed - 3hours Late Flight - and now we need to wait TWENTY MORE MINUTES for a gate! I have patience but none for incompetence."]
    )
    
if "emotion_model_name" not in st.session_state:
    st.session_state.emotion_model_name = "cardiffnlp/twitter-roberta-base-sentiment"
    emotion_model_change()

if "patent_data" not in st.session_state:
    f = open('./data/val.json')
    valData = json.load(f)
    f.close()

    patent_data = {}
    for num, label, abstract, claim in zip(valData["patent_numbers"],valData["labels"], valData["abstracts"], valData["claims"]):
        patent_data[num] = {"patent_number":num,"label":label,"abstract":abstract,"claim":claim}

    st.session_state.patent_data = patent_data
    st.session_state.patent_num = list(patent_data.keys())[0]
    st.session_state.weight = 0.5
    st.session_state.patent_abstract_model = ModelImplementation(
        'rk2546/uspto-patents-abstracts',
        DistilBertForSequenceClassification,
        'distilbert-base-uncased',
        DistilBertTokenizerFast,
        TextClassificationPipeline,
        ParsePatentOutput,
        classifier_args={"return_all_scores":True},
    )
    print("Patent abstracts model initialized")
    st.session_state.patent_claim_model = ModelImplementation(
        'rk2546/uspto-patents-claims',
        DistilBertForSequenceClassification,
        'distilbert-base-uncased',
        DistilBertTokenizerFast,
        TextClassificationPipeline,
        ParsePatentOutput,
        classifier_args={"return_all_scores":True},
    )
    print("Patent claims model initialized")

# Title
st.title("CSGY-6613 Project")
# Subtitle
st.markdown("_**Ryan Kim (rk2546)**_")

sentimentTab, patentTab = st.tabs([
    "Emotion Analysis [Milestone #2]",
    "Patent Prediction [Milestone #3]"
])

with sentimentTab:
    st.subheader("Sentiment Analysis")
    if "emotion_model" not in st.session_state:
        st.write("Loading model...")
    else:
        model_option = st.selectbox(
            "What sentiment analysis model do you want to use? NOTE: Lag may occur when loading a new model!",
            emotion_model_names,
            on_change=emotion_model_change,
            key="emotion_model_name"
        )
        form = st.form(key='sentiment-analysis-form')
        text_input = form.text_area(
            "Enter some text for sentiment analysis! If you just want to test it out without entering anything, just press the \"Submit\" button and the model will look at the placeholder.", 
            placeholder=st.session_state.emotion_model.placeholders[0]
        )
        submit = form.form_submit_button('Submit')
        if submit:
            if text_input is None or len(text_input.strip()) == 0:
                to_eval = st.session_state.emotion_model.placeholders[0]
            else:
                to_eval = text_input.strip()
            label, score, output_func = st.session_state.emotion_model.predict(to_eval)  
            output_func("**{}**: {}".format(label,score))
    
with patentTab:
    st.subheader("USPTO Patent Evaluation")
    st.markdown("Below are two inputs - one for an **ABSTRACT** and another for a list of **CLAIMS**. Enter both and select the \"Submit\" button to evaluate the patenteability of your idea.")

    patent_select_list = list(st.session_state.patent_data.keys())
    patent_index_option = st.selectbox(
        "Want to pre-populate with an existing patent? Select the index number of  below.",
        patent_select_list,
        key="patent_num",
    )

    if "patent_abstract_model" not in st.session_state or "patent_claim_model" not in st.session_state:
        st.write("Loading models...")
    else:
        with st.form(key='patent-form'):
            col1, col2 = st.columns(2)
            with col1:
                abstract_input = st.text_area(
                    "Enter the abstract of the patent below", 
                    placeholder=st.session_state.patent_data[st.session_state.patent_num]["abstract"],
                    height=200
                )
            with col2:
                claim_input = st.text_area(
                    "Enter the claims of the patent below", 
                    placeholder=st.session_state.patent_data[st.session_state.patent_num]["claim"],
                    height=200
                )
            weight_val = st.slider(
                "How much do the abstract and claims weight when aggregating a total softmax score?",
                min_value=-1.0,
                max_value=1.0,
                value=0.5,
            )
            submit = st.form_submit_button('Submit')

            if submit:
                
                is_custom = False
                if abstract_input is None or len(abstract_input.strip()) == 0:
                    abstract_to_eval = st.session_state.patent_data[st.session_state.patent_num]["abstract"].strip()
                else:
                    abstract_to_eval = abstract_input.strip()
                    is_custom = True

                if claim_input is None or len(claim_input.strip()) == 0:
                    claim_to_eval = st.session_state.patent_data[st.session_state.patent_num]["claim"].strip()
                else:
                    claim_to_eval = claim_input.strip()
                    is_custom = True

                abstract_response = st.session_state.patent_abstract_model.predict(abstract_to_eval)
                claim_response = st.session_state.patent_claim_model.predict(claim_to_eval)

                claim_weight = (1+weight_val)/2
                abstract_weight = 1-claim_weight
                aggregate_score = [
                    {'label':'REJECTED','score':abstract_response[0][0]['score']*abstract_weight + claim_response[0][0]['score']*claim_weight},
                    {'label':'ACCEPTED','score':abstract_response[0][1]['score']*abstract_weight + claim_response[0][1]['score']*claim_weight}
                ]
                aggregate_score_sorted = sorted(aggregate_score, key=lambda d: d['score'], reverse=True) 

                answerCol1, answerCol2, answerCol3 = st.columns(3)
                with answerCol1:
                    st.slider(
                        "Abstract Acceptance Likelihood", 
                        min_value=0.0, 
                        max_value=100.0,
                        value=abstract_response[0][1]["score"]*100.0,
                        disabled=True
                    )
                with answerCol2:
                    output_func = st.info
                    if aggregate_score_sorted[0]["label"] == "REJECTED":
                        output_func = st.error
                    else:
                        output_func = st.success
                    output_func("""
                        **Final Rating: {}**
                        {}%
                    """.format(aggregate_score_sorted[0]["label"],aggregate_score_sorted[0]["score"]*100.0))
                with answerCol3:
                    st.slider(
                        "Claim Acceptance Likelihood", 
                        min_value=0.0, 
                        max_value=100.0,
                        value=claim_response[0][1]["score"]*100.0,
                        disabled=True
                    )
                
                #if not is_custom:
                #    st.markdown('**Original Score:**')
                #    st.markdown(st.session_state.patent_data[st.session_state.patent_num]["label"])
                
st.write("")