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app.py
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import streamlit as st
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st.set_page_config(page_title="Explain Like I'm 5", page_icon="🧸", layout="centered")
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from transformers import pipeline
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from fpdf import FPDF
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# Load high-quality lightweight model
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@st.cache_resource
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def load_model():
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return pipeline("text2text-generation", model="google/flan-ul2")
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generator = load_model()
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# App header
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st.markdown("<h1 style='text-align: center;'>🧸 Explain Like I'm 5</h1>", unsafe_allow_html=True)
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st.markdown("<p style='text-align: center;'>Ask anything and I’ll explain it super simply 👶</p>", unsafe_allow_html=True)
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# User input
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user_input = st.text_input("🎯 Enter a topic or question:", placeholder="e.g., What is blockchain?")
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with st.expander("💡 Try These Examples"):
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st.markdown("- What is AI?\n- Why is the sky blue?\n- How does Wi-Fi work?\n- What is climate change?")
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# Generate response
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def generate_eli5_response(topic):
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prompt = f"Explain this like I’m 5: {topic}"
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result = generator(prompt, max_new_tokens=200)
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return result[0]['generated_text'].strip()
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# PDF Export
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def export_to_pdf(topic, explanation):
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pdf = FPDF()
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pdf.add_page()
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pdf.set_font("Arial", size=12)
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pdf.multi_cell(0, 10, f"Topic: {topic}\n\nExplanation:\n{explanation}")
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return pdf.output(dest='S').encode('latin-1')
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# On button click
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if st.button("✨ Explain it to me!"):
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if user_input.strip() == "":
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st.warning("Please enter a topic.")
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else:
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with st.spinner("Explaining like you're 5..."):
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explanation = generate_eli5_response(user_input)
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st.success("🍼 Here's your explanation:")
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st.markdown(f"**{explanation}**")
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# Export to PDF
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pdf_data = export_to_pdf(user_input, explanation)
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st.download_button("📄 Download as PDF", data=pdf_data, file_name=f"ELI5-{user_input[:30]}.pdf", mime="application/pdf")
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# Footer
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st.markdown("---")
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st.markdown("<p style='text-align: center;'>❤️ Made with Love. By Akash Shahade</p>", unsafe_allow_html=True)
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