Prod ready streamlit

src/streamlit_app.py

@@ -1,13 +1,77 @@
+import os
 import streamlit as st
+import numpy as np
+from sentence_transformers import SentenceTransformer
 from ingest import build_index
+from rag import retrieve
+from groq import Groq
+from config import EMBEDDING_MODEL
 
+# ---------------- CONFIG ----------------
+st.set_page_config(page_title="OTT Bot", layout="wide")
+
+# ---------------- LOAD MODELS ----------------
+@st.cache_resource
+def load_embedder():
+    return SentenceTransformer(EMBEDDING_MODEL)
+
+embedder = load_embedder()
+
+# ---------------- GROQ CLIENT ----------------
+GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
+if not GROQ_API_KEY:
+    st.error("❌ GROQ_API_KEY not set in HF Secrets")
+    st.stop()
+
+client = Groq(api_key=GROQ_API_KEY)
+
+# ---------------- UI ----------------
 st.title("📚 OTT Bot")
 
+st.markdown("Upload PDFs via HF Dataset and query them using AI")
+
+# ---------------- INGESTION ----------------
 if st.button("🔨 Build Index"):
-    with st.spinner("Building FAISS index..."):
+    with st.spinner("Building FAISS index from dataset..."):
         build_index()
-    st.success("Index built successfully!")
+    st.success("✅ Index built successfully")
 
 st.divider()
 
-st.info("Ask a question after building the index.")
+# ---------------- QUESTION INPUT ----------------
+question = st.text_input(
+    "Ask a question about the document",
+    placeholder="e.g. What is the main concept discussed?"
+)
+
+# ---------------- ANSWER ----------------
+if question:
+    with st.spinner("Searching document..."):
+        query_embedding = embedder.encode([question]).astype("float32")
+        contexts = retrieve(query_embedding)
+
+    context_text = "\n\n".join(c["text"] for c in contexts)
+
+    prompt = f"""
+Answer the question based on the context below.
+
+Context:
+{context_text}
+
+Question:
+{question}
+
+Answer clearly and concisely.
+"""
+
+    with st.spinner("Thinking..."):
+        response = client.chat.completions.create(
+            model="llama3-70b-8192",
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "user", "content": prompt},
+            ],
+        )
+
+    st.subheader("Answer")
+    st.write(response.choices[0].message.content)