Create app.py

app.py

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+import streamlit as st
+from pypdf import PdfReader
+from transformers import pipeline
+
+# -----------------------------
+# PAGE CONFIG
+# -----------------------------
+st.set_page_config(page_title="AI Reading Assistant", layout="wide")
+
+st.title("📚 AI Reading Assistant")
+st.write("Upload a PDF or paste text. Click a word/sentence to get explanation, meaning, and simplification.")
+
+# -----------------------------
+# LOAD MODELS (FREE HF MODELS)
+# -----------------------------
+@st.cache_resource
+def load_models():
+    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+    qa_model = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
+    return summarizer, qa_model
+
+summarizer, qa_model = load_models()
+
+# -----------------------------
+# FILE UPLOAD
+# -----------------------------
+uploaded_file = st.file_uploader("Upload PDF", type=["pdf"])
+
+text_data = ""
+
+if uploaded_file:
+    reader = PdfReader(uploaded_file)
+    for page in reader.pages:
+        text_data += page.extract_text()
+
+# -----------------------------
+# TEXT INPUT OPTION
+# -----------------------------
+text_input = st.text_area("Or paste your text here:")
+
+if text_input:
+    text_data = text_input
+
+# -----------------------------
+# DISPLAY TEXT
+# -----------------------------
+if text_data:
+    st.subheader("📄 Your Text")
+    st.write(text_data[:2000])  # limit display for performance
+
+    # -----------------------------
+    # SIMPLIFY TEXT
+    # -----------------------------
+    if st.button("✨ Simplify Paragraph"):
+        with st.spinner("Simplifying..."):
+            summary = summarizer(text_data[:1000], max_length=120, min_length=30, do_sample=False)
+            st.success(summary[0]['summary_text'])
+
+    # -----------------------------
+    # QUESTION ANSWERING
+    # -----------------------------
+    st.subheader("❓ Ask a Question from Text")
+    question = st.text_input("Enter your question:")
+
+    if question:
+        with st.spinner("Finding answer..."):
+            answer = qa_model(question=question, context=text_data[:2000])
+            st.success(f"Answer: {answer['answer']}")
+
+    # -----------------------------
+    # WORD MEANING (SIMPLE)
+    # -----------------------------
+    st.subheader("🔍 Word Explanation")
+    word = st.text_input("Enter a difficult word:")
+
+    if word:
+        explanation_prompt = f"Explain the meaning of the word '{word}' in simple English with an example."
+        with st.spinner("Explaining..."):
+            explanation = summarizer(explanation_prompt, max_length=60, min_length=20, do_sample=False)
+            st.success(explanation[0]['summary_text'])
+
+else:
+    st.info("Upload a PDF or paste text to begin.")
+
+# -----------------------------
+# FOOTER
+# -----------------------------
+st.markdown("---")
+st.caption("Built with ❤️ using Streamlit + Hugging Face")