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| import streamlit as st | |
| import pandas as pd | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline | |
| # Function to load the pre-trained model | |
| def load_finetune_model(model_name): | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| return tokenizer, model | |
| def load_model(model_name): | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| sentiment_pipeline = pipeline("sentiment-analysis", tokenizer=tokenizer, model=model) | |
| return sentiment_pipeline | |
| # Streamlit app | |
| st.title("Multi-label Toxicity Detection App") | |
| st.write("Enter a text and select the fine-tuned model to get the toxicity analysis.") | |
| # Input text | |
| default_text = "You might be the most stupid person in the world." | |
| text = st.text_input("Enter your text:", value=default_text) | |
| category = {'LABEL_0': 'toxic', 'LABEL_1': 'severe_toxic', 'LABEL_2': 'obscene', 'LABEL_3': 'threat', 'LABEL_4': 'insult', 'LABEL_5': 'identity_hate'} | |
| # Model selection | |
| model_options = { | |
| "Olivernyu/finetuned_bert_base_uncased": { | |
| "description": "This model detects different types of toxicity like threats, obscenity, insults, and identity-based hate in text. The table is prepopulated with some data, the table will be displayed once you hit analyze.", | |
| }, | |
| "distilbert-base-uncased-finetuned-sst-2-english": { | |
| "labels": ["NEGATIVE", "POSITIVE"], | |
| "description": "This model classifies text into positive or negative sentiment. It is based on DistilBERT and fine-tuned on the Stanford Sentiment Treebank (SST-2) dataset.", | |
| }, | |
| "textattack/bert-base-uncased-SST-2": { | |
| "labels": ["LABEL_0", "LABEL_1"], | |
| "description": "This model classifies text into positive(LABEL_1) or negative(LABEL_0) sentiment. It is based on BERT and fine-tuned on the Stanford Sentiment Treebank (SST-2) dataset.", | |
| }, | |
| "cardiffnlp/twitter-roberta-base-sentiment": { | |
| "labels": ["LABEL_0", "LABEL_1", "LABEL_2"], | |
| "description": "This model classifies tweets into negative (LABEL_0), neutral(LABEL_1), or positive(LABEL_2) sentiment. It is based on RoBERTa and fine-tuned on a large dataset of tweets.", | |
| }, | |
| } | |
| selected_model = st.selectbox("Choose a fine-tuned model:", model_options) | |
| st.write("### Model Information") | |
| st.write(f"**Description:** {model_options[selected_model]['description']}") | |
| initial_table_df = pd.DataFrame(columns=["Text (portion)", "Toxicity class 1", "Class 1 probability", "Toxicity class 2", "Class 2 probability"]) | |
| initial_table_data = [{'Text (portion)': ["who's speaking? \n you goddamn cocksucker you know "], | |
| 'Toxicity class 1': ['obscene'], | |
| 'Class 1 probability': 0.7282997369766235, | |
| 'Toxicity class 2': ['toxic'], | |
| 'Class 2 probability': 0.2139672487974167}, | |
| {'Text (portion)': ['::Here is another source: Melissa Sue Halverson (2'], | |
| 'Toxicity class 1': ['toxic'], | |
| 'Class 1 probability': 0.24484945833683014, | |
| 'Toxicity class 2': ['obscene'], | |
| 'Class 2 probability': 0.1627064049243927}, | |
| {'Text (portion)': [', 8 November 2007 (UTC) \n\n All I can say is, havin'], | |
| 'Toxicity class 1': ['toxic'], | |
| 'Class 1 probability': 0.7277262806892395, | |
| 'Toxicity class 2': ['obscene'], | |
| 'Class 2 probability': 0.2502792477607727}, | |
| {'Text (portion)': ['::::I only see that at birth two persons are given'], | |
| 'Toxicity class 1': ['toxic'], | |
| 'Class 1 probability': 0.2711867094039917, | |
| 'Toxicity class 2': ['insult'], | |
| 'Class 2 probability': 0.15477754175662994}, | |
| {'Text (portion)': ["* There you have it: one man's Barnstar is another"], | |
| 'Toxicity class 1': ['toxic'], | |
| 'Class 1 probability': 0.5408656001091003, | |
| 'Toxicity class 2': ['insult'], | |
| 'Class 2 probability': 0.12563346326351166}, | |
| {'Text (portion)': ['" \n\n == Fact == \n\n Could just be abit of trivial f'], | |
| 'Toxicity class 1': ['toxic'], | |
| 'Class 1 probability': 0.35239243507385254, | |
| 'Toxicity class 2': ['obscene'], | |
| 'Class 2 probability': 0.1686778962612152}, | |
| {'Text (portion)': ['HE IS A GHAY ASS FUCKER@@!!'], | |
| 'Toxicity class 1': ['obscene'], | |
| 'Class 1 probability': 0.7819343209266663, | |
| 'Toxicity class 2': ['toxic'], | |
| 'Class 2 probability': 0.16951803863048553}, | |
| {'Text (portion)': ["I'VE SEEN YOUR CRIMES AGAINST CHILDREN AND I'M ASH"], | |
| 'Toxicity class 1': ['toxic'], | |
| 'Class 1 probability': 0.8491994738578796, | |
| 'Toxicity class 2': ['threat'], | |
| 'Class 2 probability': 0.04749392718076706}, | |
| {'Text (portion)': [':While with a lot of that essay says, general time'], | |
| 'Toxicity class 1': ['toxic'], | |
| 'Class 1 probability': 0.282654732465744, | |
| 'Toxicity class 2': ['obscene'], | |
| 'Class 2 probability': 0.15901680290699005}, | |
| {'Text (portion)': ['== Help == \n\n Please members of wiki, help me. My '], | |
| 'Toxicity class 1': ['toxic'], | |
| 'Class 1 probability': 0.3118911385536194, | |
| 'Toxicity class 2': ['obscene'], | |
| 'Class 2 probability': 0.16506287455558777}] | |
| for d in initial_table_data: | |
| initial_table_df = pd.concat([initial_table_df, pd.DataFrame(d)], ignore_index=True) | |
| # Load the model and perform toxicity analysis | |
| if "table" not in st.session_state: | |
| st.session_state['table'] = initial_table_df | |
| if st.button("Analyze"): | |
| if not text: | |
| st.write("Please enter a text.") | |
| else: | |
| with st.spinner("Analyzing toxicity..."): | |
| if selected_model == "Olivernyu/finetuned_bert_base_uncased": | |
| toxicity_detector = load_model(selected_model) | |
| outputs = toxicity_detector(text, top_k=2) | |
| category_names = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"] | |
| results = [] | |
| for item in outputs: | |
| results.append((category[item['label']], item['score'])) | |
| # Create a table with the input text (or a portion of it), the highest toxicity class, and its probability | |
| table_data = { | |
| "Text (portion)": [text[:50]], | |
| "Toxicity class 1": [results[0][0]], | |
| f"Class 1 probability": results[0][1], | |
| "Toxicity class 2": [results[1][0]], | |
| f"Class 2 probability": results[1][1] | |
| } | |
| # print("Before concatenation:") | |
| # print(table_df) | |
| # Concatenate the new data frame with the existing data frame | |
| st.session_state['table'] = pd.concat([pd.DataFrame(table_data), st.session_state['table']], ignore_index=True) | |
| # print("After concatenation:") | |
| # print(table_df) | |
| # Update the table with the new data frame | |
| st.table(st.session_state['table']) | |
| else: | |
| st.empty() | |
| sentiment_pipeline = load_model(selected_model) | |
| result = sentiment_pipeline(text) | |
| st.write(f"Sentiment: {result[0]['label']} (confidence: {result[0]['score']:.2f})") | |
| if result[0]['label'] in ['POSITIVE', 'LABEL_1'] and result[0]['score']> 0.9: | |
| st.balloons() | |
| elif result[0]['label'] in ['NEGATIVE', 'LABEL_0'] and result[0]['score']> 0.9: | |
| st.error("Hater detected.") | |
| else: | |
| st.write("Enter a text and click 'Analyze' to perform toxicity analysis.") | |