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| import streamlit as st | |
| from transformers import pipeline | |
| import json | |
| import langdetect | |
| from keybert import KeyBERT | |
| from sklearn.feature_extraction.text import CountVectorizer | |
| # Load Pretrained Models | |
| def load_models(): | |
| return { | |
| "emotion": pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True), | |
| "sentiment": pipeline("text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment"), | |
| "summarization": pipeline("summarization", model="facebook/bart-large-cnn"), | |
| "ner": pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english", grouped_entities=True), | |
| "toxicity": pipeline("text-classification", model="unitary/unbiased-toxic-roberta"), | |
| "keyword_extraction": KeyBERT() | |
| } | |
| models = load_models() | |
| # Function: Emotion Detection | |
| def analyze_emotions(text): | |
| results = models["emotion"](text) | |
| return {r['label']: round(r['score'], 2) for r in results[0]} | |
| # Function: Sentiment Analysis | |
| def analyze_sentiment(text): | |
| result = models["sentiment"](text)[0] | |
| return {result['label']: round(result['score'], 2)} | |
| # Function: Text Summarization | |
| def summarize_text(text): | |
| return models["summarization"](text[:1024])[0]['summary_text'] | |
| # Function: Keyword Extraction | |
| def extract_keywords(text): | |
| vectorizer = CountVectorizer(ngram_range=(1, 2)) | |
| return models["keyword_extraction"].extract_keywords(text, vectorizer=vectorizer, stop_words='english') | |
| # Function: Named Entity Recognition (NER) | |
| def analyze_ner(text): | |
| entities = models["ner"](text) | |
| return {entity["word"]: entity["entity_group"] for entity in entities} | |
| # Function: Language Detection | |
| def detect_language(text): | |
| try: | |
| return langdetect.detect(text) | |
| except: | |
| return "Error detecting language" | |
| # Function: Toxicity Detection | |
| def detect_toxicity(text): | |
| results = models["toxicity"](text) | |
| return {results[0]['label']: round(results[0]['score'], 2)} | |
| # Streamlit UI | |
| st.title("🚀 AI-Powered Text Intelligence App") | |
| st.markdown("Analyze text with multiple NLP features: Emotion Detection, Sentiment Analysis, Summarization, NER, Keywords, Language Detection, and more!") | |
| # User Input | |
| text_input = st.text_area("Enter text to analyze:", "") | |
| if st.button("Analyze Text"): | |
| if text_input.strip(): | |
| st.subheader("🔹 Emotion Detection") | |
| st.json(analyze_emotions(text_input)) | |
| st.subheader("🔹 Sentiment Analysis") | |
| st.json(analyze_sentiment(text_input)) | |
| st.subheader("🔹 Text Summarization") | |
| st.write(summarize_text(text_input)) | |
| st.subheader("🔹 Keyword Extraction") | |
| st.json(extract_keywords(text_input)) | |
| st.subheader("🔹 Named Entity Recognition (NER)") | |
| st.json(analyze_ner(text_input)) | |
| st.subheader("🔹 Language Detection") | |
| st.write(f"Detected Language: `{detect_language(text_input)}`") | |
| st.subheader("🔹 Toxicity Detection") | |
| st.json(detect_toxicity(text_input)) | |
| # Save results to JSON | |
| results = { | |
| "emotion": analyze_emotions(text_input), | |
| "sentiment": analyze_sentiment(text_input), | |
| "summary": summarize_text(text_input), | |
| "keywords": extract_keywords(text_input), | |
| "ner": analyze_ner(text_input), | |
| "language": detect_language(text_input), | |
| "toxicity": detect_toxicity(text_input) | |
| } | |
| st.download_button("Download JSON Report", json.dumps(results, indent=2), "text_analysis.json", "application/json") | |
| else: | |
| st.warning("⚠️ Please enter some text to analyze.") | |