Create app.py

app.py

@@ -0,0 +1,113 @@
+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")