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import streamlit as st
from transformers import pipeline
import matplotlib.pyplot as plt
import json
import langdetect
from keybert import KeyBERT
# Load models with caching
@st.cache_resource
def load_models():
return {
"emotion": pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True),
"sentiment": pipeline("sentiment-analysis"),
"summarization": pipeline("summarization"),
"ner": pipeline("ner", 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)
emotions = {r['label']: round(r['score'], 2) for r in results[0]}
return emotions
# 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):
summary = models["summarization"](text["1024"])[0]['summary_text'] # Limit input to 1024 tokens
return summary
# Function: Keyword Extraction
def extract_keywords(text):
return models["keyword_extraction"].extract_keywords(text, keyphrase_ngram_range(1, 2), 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 and Translation
def detect_language(text):
try:
lang = langdetect.detect(text)
return lang
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")
emotions = analyze_emotions(text_input)
st.json(emotions)
st.subheader("🔹 Sentiment Analysis")
sentiment = analyze_sentiment(text_input)
st.json(sentiment)
st.subheader("🔹 Text Summarization")
summary = summarize_text(text_input)
st.write(summary)
st.subheader("🔹 Keyword Extraction")
keywords = extract_keywords(text_input)
st.json(keywords)
st.subheader("🔹 Named Entity Recognition (NER)")
ner_data = analyze_ner(text_input)
st.json(ner_data)
st.subheader("🔹 Language Detection")
lang = detect_language(text_input)
st.write(f"Detected Language: `{lang}`")
st.subheader("🔹 Toxicity Detection")
toxicity = detect_toxicity(text_input)
st.json(toxicity)
# JSON Download
result_data = {
"emotion": emotions,
"sentiment": sentiment,
"summary": summary,
"keywords": keywords,
"ner": ner_data,
"language": lang,
"toxicity": toxicity
}
json_result = json.dumps(result_data, indent=2)
st.download_button("Download Analysis Report", data=json_result, file_name="text_analysis.json", mime="application/json")
else:
st.warning("⚠️ Please enter some text to analyze") |