Spaces:
Runtime error
Runtime error
| import streamlit as st | |
| from transformers import pipeline | |
| # Load models | |
| # Distilled Sentiment Classifier | |
| # Link: https://huggingface.co/lxyuan/distilbert-base-multilingual-cased-sentiments-student | |
| distilled_sentiment_classifier = pipeline( | |
| model="lxyuan/distilbert-base-multilingual-cased-sentiments-student", | |
| return_all_scores=True | |
| ) | |
| # Emotion Classifier | |
| # Link: https://huggingface.co/SamLowe/roberta-base-go_emotions | |
| emotion_text_classifier = pipeline("text-classification", model="SamLowe/roberta-base-go_emotions") | |
| # Named Entity Recognition | |
| # Link: https://huggingface.co/mdarhri00/named-entity-recognition | |
| named_entity_classifier = pipeline("token-classification", model="mdarhri00/named-entity-recognition") | |
| # Toxicity Classifier | |
| # Link: https://huggingface.co/s-nlp/roberta_toxicity_classifier | |
| toxicity_classifier = pipeline("text-classification", model="s-nlp/roberta_toxicity_classifier") | |
| # Streamlit app | |
| def main(): | |
| st.title("HuggingFace Model Demo App") | |
| # User input for text | |
| user_text = st.text_area("Enter some text:") | |
| if user_text: | |
| # Available Models | |
| analysis_type = st.selectbox("Select Analysis Type", ["", "Sentiment Analysis", "Emotion Analysis", "Named Entity Recognition", "Toxicity Analysis"]) | |
| # Run custom and display outputs | |
| st.header("Function Output:") | |
| if analysis_type == "Sentiment Analysis": | |
| st.subheader("Sentiment Analysis:") | |
| for labels_and_scores in data: | |
| for entry in labels_and_scores: | |
| label = entry["label"] | |
| score = entry["score"] | |
| st.write(f"Label: {label}, Score: {score}") | |
| # Find the label with the highest score | |
| max_score_entry = max(data, key=lambda x: x['score']) | |
| max_score_label = max_score_entry['label'] | |
| max_score = max_score_entry['score'] | |
| # Display the label with the highest score | |
| st.write(f"Label with the highest score: {max_score_label}, Highest Score: {max_score}") | |
| if analysis_type == "Emotion Analysis": | |
| st.subheader("Emotion Analysis:") | |
| # Parse JSON data | |
| data = emotion_text_classifier(user_text) | |
| # Extract and display label and score for each entry | |
| for entry in data: | |
| label = entry["label"] | |
| score = entry["score"] | |
| st.write(f"Label: {label}, Score: {score}") | |
| if analysis_type == "Named Entity Recognition": | |
| st.subheader("Named Entity Recognition:") | |
| # Parse JSON data | |
| data = named_entity_classifier(user_text) | |
| #st.write(data) | |
| # Extract and display data | |
| for labels in data: | |
| for entry in labels: | |
| entity_group = entry["entity_group"] | |
| score = entry["score"] | |
| word = entry["word"] | |
| start = entry["start"] | |
| end = entry["end"] | |
| st.write(f"Word: {word}, Entity Group: {entity_group}, Score: {score}, Start: {start}, End: {end}") | |
| if analysis_type == "Toxicity Analysis": | |
| st.subheader("Toxicity Analysis:") | |
| # Parse JSON data | |
| data = toxicity_classifier(user_text) | |
| #st.write(data) | |
| # Extract and display label and score values | |
| for entry in data: | |
| label = entry["label"] | |
| score = entry["score"] | |
| st.write(f"Label: {label}, Score: {score}") | |
| if __name__ == "__main__": | |
| main() | |