Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from sentence_transformers import SentenceTransformer
+import faiss
+import json
+import random
+import os
+
+# ----------------- DATA SECTION -----------------
+
+# Quran + Hadith sample data (You can expand later)
+quran_data = [
+    {
+        "source": "Surah Al-Baqarah, Ayah 2",
+        "text": "This is the Book about which there is no doubt, a guidance for those conscious of Allah."
+    },
+    {
+        "source": "Surah Al-Ikhlas, Ayah 1",
+        "text": "Say, 'He is Allah, [who is] One.'"
+    }
+]
+
+hadith_data = [
+    {
+        "source": "Sahih Bukhari, Book 2, Hadith 13",
+        "text": "None of you will have faith till he wishes for his brother what he likes for himself."
+    },
+    {
+        "source": "Sahih Muslim, Book 1, Hadith 1",
+        "text": "Actions are judged by intentions."
+    }
+]
+
+# ----------------- EMBEDDING + FAISS -----------------
+
+@st.cache_resource
+def build_index(passages):
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+    texts = [p['text'] for p in passages]
+    embeddings = model.encode(texts)
+    index = faiss.IndexFlatL2(embeddings.shape[1])
+    index.add(embeddings)
+    return model, index, passages
+
+model, index, passages = build_index(quran_data + hadith_data)
+
+def retrieve_passages(query, k=3):
+    query_vec = model.encode([query])
+    scores, idxs = index.search(query_vec, k)
+    return [passages[i] for i in idxs[0]]
+
+# ----------------- TRANSLATION -----------------
+
+@st.cache_resource
+def load_translators():
+    trans_ur = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ur")
+    trans_ar = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ar")
+    return trans_ur, trans_ar
+
+translator_ur, translator_ar = load_translators()
+
+def translate(text, lang):
+    if lang == "Urdu":
+        return translator_ur(text)[0]['translation_text']
+    elif lang == "Arabic":
+        return translator_ar(text)[0]['translation_text']
+    return text
+
+# ----------------- DAILY VERSES -----------------
+
+def get_random_ayah():
+    return random.choice(quran_data)
+
+def get_random_hadith():
+    return random.choice(hadith_data)
+
+# ----------------- STREAMLIT UI -----------------
+
+st.set_page_config(page_title="Noor-e-Hidayat", layout="centered")
+st.title("🕊️ Noor-e-Hidayat – Your Islamic AI Assistant")
+
+lang = st.selectbox("🌐 Choose Language", ["English", "Urdu", "Arabic"])
+st.markdown("---")
+
+st.subheader("🔍 Ask Noor-e-Hidayat")
+query = st.text_input("Type your question related to Qur’an, Hadith, or Islamic guidance...")
+
+if query:
+    results = retrieve_passages(query)
+    for r in results:
+        st.markdown(f"📖 **{r['source']}**")
+        st.write(translate(r['text'], lang))
+        st.markdown("---")
+
+st.subheader("📜 Ayah of the Day")
+ayah = get_random_ayah()
+st.info(f"**{ayah['source']}**\n\n{translate(ayah['text'], lang)}")
+
+st.subheader("📜 Hadith of the Day")
+hadith = get_random_hadith()
+st.success(f"**{hadith['source']}**\n\n{translate(hadith['text'], lang)}")
+
+st.markdown("---")
+st.caption("⚙️ Powered by Transformers, Sentence-BERT, and FAISS • Built with ❤️ using Streamlit")