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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
@st.cache_resource
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.") |