Create app.py

app.py

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+# app.py
+import os
+import faiss
+import streamlit as st
+from PyPDF2 import PdfReader
+from sentence_transformers import SentenceTransformer
+from groq import Groq
+import numpy as np
+
+# --- SETUP GROQ ---
+client = Groq(api_key=os.getenv("GROQ_API_KEY"))
+
+# --- LOAD EMBEDDING MODEL ---
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
+# --- STREAMLIT UI ---
+st.set_page_config(page_title="RAG App with Groq", layout="wide")
+st.title("📄🧠 RAG-Based Question Answering App")
+
+# --- UPLOAD PDF ---
+uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
+if uploaded_file:
+    reader = PdfReader(uploaded_file)
+    text = ""
+    for page in reader.pages:
+        text += page.extract_text()
+    
+    # --- CHUNKING ---
+    def chunk_text(text, chunk_size=500):
+        words = text.split()
+        return [" ".join(words[i:i+chunk_size]) for i in range(0, len(words), chunk_size)]
+
+    chunks = chunk_text(text)
+
+    # --- VECTORIZE CHUNKS ---
+    embeddings = embedder.encode(chunks)
+    dim = embeddings[0].shape[0]
+    index = faiss.IndexFlatL2(dim)
+    index.add(np.array(embeddings))
+
+    st.success("✅ Document uploaded and indexed!")
+
+    # --- USER QUERY ---
+    user_query = st.text_input("Ask something about the document")
+    if user_query:
+        query_embedding = embedder.encode([user_query])
+        _, I = index.search(np.array(query_embedding), k=3)
+        retrieved_chunks = "\n\n".join([chunks[i] for i in I[0]])
+
+        # --- GROQ COMPLETION ---
+        response = client.chat.completions.create(
+            model="llama-3.1-8b-instant",
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant who answers based on the provided context."},
+                {"role": "user", "content": f"Context: {retrieved_chunks}\n\nQuestion: {user_query}"}
+            ]
+        )
+
+        st.subheader("📢 Answer")
+        st.write(response.choices[0].message.content)