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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification | |
| fine_tuned_model = "andyqin18/test-finetuned" | |
| sample_text_num = 10 | |
| # Define analyze function | |
| def analyze(model_name: str, text: str, top_k=1) -> dict: | |
| ''' | |
| Output result of sentiment analysis of a text through a defined model | |
| ''' | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer, top_k=top_k) | |
| return classifier(text) | |
| # App title | |
| st.title("Sentiment Analysis App - Milestone2") | |
| st.write("This app is to analyze the sentiments behind a text.") | |
| st.write("Currently it uses pre-trained models without fine-tuning.") | |
| # Model hub | |
| model_descrip = { | |
| fine_tuned_model: "This is a customized BERT-base finetuned model that detects multiple toxicity for a text. \ | |
| Labels: toxic, severe_toxic, obscene, threat, insult, identity_hate", | |
| "distilbert-base-uncased-finetuned-sst-2-english": "This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2. \ | |
| Labels: POSITIVE; NEGATIVE ", | |
| "cardiffnlp/twitter-roberta-base-sentiment": "This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. \ | |
| Labels: 0 -> Negative; 1 -> Neutral; 2 -> Positive", | |
| "finiteautomata/bertweet-base-sentiment-analysis": "Model trained with SemEval 2017 corpus (around ~40k tweets). Base model is BERTweet, a RoBERTa model trained on English tweets. \ | |
| Labels: POS; NEU; NEG" | |
| } | |
| df = pd.read_csv("/milestone3/comp/test_comment.csv") | |
| test_texts = df["comment_text"].values | |
| sample_texts = np.random.choice(test_texts, size=sample_text_num, replace=False) | |
| init_table_dict = { | |
| "Text": [], | |
| "Highest Toxicity Class": [], | |
| "Highest Score": [], | |
| "Second Highest Toxicity Class": [], | |
| "Second Highest Score": [] | |
| } | |
| for text in sample_texts: | |
| result = analyze(fine_tuned_model, text, top_k=2) | |
| init_table_dict["Text"].append(text[:50]) | |
| init_table_dict["Highest Toxicity Class"].append(result[0][0]['label']) | |
| init_table_dict["Highest Score"].append(result[0][0]['score']) | |
| init_table_dict["Second Highest Toxicity Class"].append(result[0][1]['label']) | |
| init_table_dict["Second Highest Score"].append(result[0][1]['score']) | |
| user_input = st.text_input("Enter your text:", value="NYU is the better than Columbia.") | |
| user_model = st.selectbox("Please select a model:", model_descrip) | |
| # Display model information | |
| st.write("### Model Description:") | |
| st.write(model_descrip[user_model]) | |
| # Perform analysis and print result | |
| if st.button("Analyze"): | |
| if not user_input: | |
| st.write("Please enter a text.") | |
| else: | |
| with st.spinner("Hang on.... Analyzing..."): | |
| if user_model == fine_tuned_model: | |
| result = analyze(user_model, user_input, top_k=2) | |
| df = pd.DataFrame(init_table_dict) | |
| st.dataframe(df) | |
| else: | |
| result = analyze(user_model, user_input) | |
| st.write("Result:") | |
| st.write(f"Label: **{result[0]['label']}**") | |
| st.write(f"Confidence Score: **{result[0]['score']}**") | |
| else: | |
| st.write("Go on! Try the app!") |