pushing temp fix

.gitignore

@@ -1 +1,3 @@
-**/.DS_Store
+**/.DS_Store
+
+models/*

src/main.py

@@ -91,7 +91,7 @@ if "patent_data" not in st.session_state:
     st.session_state.patent_num = list(patent_data.keys())[0]
     st.session_state.weight = 0.5
     st.session_state.patent_abstract_model = ModelImplementation(
-        './models/uspto_abstracts',
+        'rk2546/uspto-patents-abstracts',
         DistilBertForSequenceClassification,
         'distilbert-base-uncased',
         DistilBertTokenizerFast,
@@ -101,7 +101,7 @@ if "patent_data" not in st.session_state:
     )
     print("Patent abstracts model initialized")
     st.session_state.patent_claim_model = ModelImplementation(
-        './models/uspto_claims',
+        'rk2546/uspto-patents-claims',
         DistilBertForSequenceClassification,
         'distilbert-base-uncased',
         DistilBertTokenizerFast,

src/test.py

@@ -1,277 +0,0 @@
-import os
-import json
-import random
-
-import streamlit as st
-from transformers import TextClassificationPipeline, pipeline
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, DistilBertTokenizerFast, DistilBertForSequenceClassification
-
-# We'll be using Torch this time around
-import torch
-import torch.nn.functional as F
-
-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
-
-        self.history = []
-    
-    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']
-    if self.transformer_model_name == "cardiffnlp/twitter-roberta-base-sentiment":
-        if label == "LABEL_0":
-            label = "Negative"
-        elif label == "LABEL_2":
-            label = "Positive"
-        else:
-            label = "Neutral"
-    return label, score
-
-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 "page" not in st.session_state:
-    st.session_state.page = "home"
-
-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(
-        './models/uspto_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(
-        './models/uspto_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)**_")
-st.markdown("---")
-
-def PageToHome():
-    st.session_state.page = "home"
-def PageToEmotion():
-    st.session_state.page = "emotion"
-def PageToPatent():
-    st.session_state.page = "patent"
-
-with st.sidebar:
-    st.subheader("Toolbox")
-    home_selected = st.button("Home", on_click=PageToHome)
-    emotion_selected = st.button(
-        "Emotion Analysis [Milestone #2]", 
-        on_click=PageToEmotion
-    )
-    patent_selected = st.button(
-        "Patent Prediction [Milestone #3]", 
-        on_click=PageToPatent
-    )
-
-if st.session_state.page == "emotion":
-    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()
-            st.write("You entered:")
-            st.markdown("> {}".format(to_eval))
-            st.write("Using the NLP model:")
-            st.markdown("> {}".format(st.session_state.emotion_model_name))
-            label, score = st.session_state.emotion_model.predict(to_eval)  
-            st.markdown("#### Result:")
-            st.markdown("**{}**: {}".format(label,score))
-    
-elif st.session_state.page == "patent":
-    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",
-    )
-
-    print(patent_index_option)
-
-    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=400
-                )
-            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=400
-                )
-            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
-
-                #tokenized_claim = st.session_state.patent_claim_model.tokenizer.encode(claim_to_eval, padding=True, truncation=True, max_length=512, add_special_tokens = True)
-                #untokenized_claim = st.session_state.patent_claim_model.tokenizer.decode(tokenized_claim)
-                #claim_to_eval2 = untokenized_claim.replace("[CLS]","")
-                #claim_to_eval2 = claim_to_eval2.replace("[SEP]","")
-                #print(claim_to_eval2)
-
-                abstract_response = st.session_state.patent_abstract_model.predict(abstract_to_eval)
-                claim_response = st.session_state.patent_claim_model.predict(claim_to_eval)
-                print(abstract_response[0])
-                print(claim_response[0])
-                print(weight_val)
-
-                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) 
-                print(aggregate_score_sorted)
-                print(f'Original Rating: {st.session_state.patent_data[st.session_state.patent_num]["label"]}')
-
-                st.markdown("---")
-                answerCol1, answerCol2 = st.columns(2)
-                with answerCol1:
-                    st.markdown("### Abstract Ratings")
-                    st.markdown("""
-                        > **Reject**: {}  
-                        > **Accept**: {}
-                    """.format(abstract_response[0][0]["score"], abstract_response[0][1]["score"]))
-                with answerCol2:
-                    st.markdown("### Claims Ratings")
-                    st.markdown("""
-                        > **Reject**: {}  
-                        > **Accept**: {}
-                    """.format(claim_response[0][0]["score"], claim_response[0][1]["score"]))
-                
-                st.markdown(f'### Final Rating: **{aggregate_score_sorted[0]["label"]}**')
-                st.markdown("""
-                    > **Reject**: {}  
-                    > **Accept**: {}
-                """.format(aggregate_score[0]['score'], aggregate_score[1]['score']))
-                
-                #if not is_custom:
-                #    st.markdown('**Original Score:**')
-                #    st.markdown(st.session_state.patent_data[st.session_state.patent_num]["label"])
-                
-
-            
-
-
-else:
-    st.write("To get started, access the sidebar on the left (click the arrow in the top-left corner of the screen) and select a tool.")
-
-st.write("")
-st.write("")