Create app.py

app.py

@@ -0,0 +1,56 @@
+import streamlit as st
+import nltk
+import spacy
+import matplotlib.pyplot as plt
+from transformers import pipeline
+import random
+
+# Load NLP models
+nltk.download("vader_lexicon")
+from nltk.sentiment import SentimentIntensityAnalyzer
+sia = SentimentIntensityAnalyzer()
+
+nlp = spacy.load("en_core_web_sm")
+emotion_pipeline = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
+
+# Sample texts
+sample_texts = [
+    "The digital world is transforming the way we read and engage with text.",
+    "Reading is an essential skill that shapes our understanding of the world.",
+    "AI-driven education tools can personalize the learning experience for students."
+]
+
+# Streamlit UI
+st.title("📖 AI-Powered Adaptive Reading Engagement")
+st.write("Analyze how users engage with digital reading using AI-powered insights.")
+
+# Text Input
+text_option = st.selectbox("Choose a sample text or enter your own:", ["Use Sample"] + sample_texts)
+if text_option == "Use Sample":
+    text = st.text_area("Read this passage:", random.choice(sample_texts), height=150)
+else:
+    text = st.text_area("Enter your own text:", height=150)
+
+# Sentiment Analysis
+if st.button("Analyze Engagement"):
+    if text:
+        sentiment_score = sia.polarity_scores(text)
+        emotion_results = emotion_pipeline(text)
+
+        # Display Sentiment
+        st.subheader("📊 Sentiment Analysis")
+        st.write(f"Positive: {sentiment_score['pos'] * 100:.2f}%, Negative: {sentiment_score['neg'] * 100:.2f}%, Neutral: {sentiment_score['neu'] * 100:.2f}%")
+
+        # Display Emotion
+        st.subheader("🎭 Emotion Detection")
+        top_emotion = max(emotion_results[0], key=lambda x: x['score'])
+        st.write(f"Detected Emotion: **{top_emotion['label']}** (Confidence: {top_emotion['score']:.2f})")
+
+        # Visualization
+        labels = [e['label'] for e in emotion_results[0]]
+        scores = [e['score'] for e in emotion_results[0]]
+        fig, ax = plt.subplots()
+        ax.bar(labels, scores)
+        st.pyplot(fig)
+    else:
+        st.warning("Please enter a text to analyze.")