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| import streamlit as st #Web App | |
| from PIL import Image #Image Processing | |
| import numpy as np #Image Processing | |
| from transformers import AutoModelForSequenceClassification | |
| from transformers import TFAutoModelForSequenceClassification | |
| from transformers import AutoTokenizer | |
| import numpy as np | |
| from scipy.special import softmax | |
| import csv | |
| import urllib.request | |
| import pandas as pd | |
| #Title | |
| st.title("Toxicity Analysis of Tweets") | |
| #Subtitle | |
| st.markdown("## Using a fine-tuned roBERTa model") | |
| st.markdown("Link to the app - [Basic Sentiment Analyzer on 🤗 Spaces](https://huggingface.co/spaces/rbbotadra/toxicity-analyzer-app)") | |
| #Dropdown menu for model options | |
| model_opt = st.selectbox( | |
| 'Select a finetuned model:', | |
| ('roBERTa tuned on Tweets [6-class toxicity analysis]','')) | |
| st.write('Model selected:', model_opt) | |
| #Tuned Model Path | |
| MODEL = f"./cardiffnlp/twitter-roberta-base-sentiment" | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL) | |
| model = AutoModelForSequenceClassification.from_pretrained(MODEL) | |
| #Label mapping | |
| labels = ["toxic","severe_toxic","obscene","threat","insult","identity_hate"] | |
| # Preprocess text (username and link placeholders) | |
| def preprocess(text): | |
| new_text = [] | |
| for t in text.split(" "): | |
| t = '@user' if t.startswith('@') and len(t) > 1 else t | |
| t = 'http' if t.startswith('http') else t | |
| new_text.append(t) | |
| return " ".join(new_text) | |
| text = st.text_input("Text Input", "War is cruelty. There is no use trying to reform it. The crueler it is, the sooner it will be over.") | |
| st.write("Current Text:", text) | |
| if st.button('Run Model'): | |
| MODEL = f"./cardiffnlp/twitter-roberta-base-sentiment/config.json" | |
| text = preprocess(text) | |
| encoded_input = tokenizer(text, return_tensors='pt') | |
| output = model(**encoded_input) | |
| scores = output[0][0].detach().numpy() | |
| scores = softmax(scores) | |
| output = {"Class No.":[], | |
| "Class Name":[], | |
| "Score":[] | |
| } | |
| ranking = np.argsort(scores) | |
| ranking = ranking[::-1] | |
| for i in range(scores.shape[0]): | |
| l = labels[ranking[i]] | |
| s = scores[ranking[i]] | |
| #st.write(f"{i+1}) {l}: {np.round(float(s), 4)}") | |
| output['Class No.'].append(i) | |
| output['Class Name'].append(l) | |
| output['Score'].append(s) | |
| table = pd.DataFrame(output) | |
| #Display as table | |
| st.table(table) | |
| else: | |
| st.write("Press button to run Senitment Analysis.") | |