Create app.py

app.py

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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.")