Create app.py

app.py

@@ -0,0 +1,89 @@
+import streamlit as st
+import PyPDF2
+import tiktoken
+import faiss
+import numpy as np
+from sentence_transformers import SentenceTransformer
+import requests
+
+# Load embedding model
+embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
+
+# GROQ API configuration
+GROQ_API_KEY = "YOUR_GROQ_API_KEY"  # ⛔ Replace this with your actual GROQ API key
+GROQ_URL = "https://api.groq.com/openai/v1/chat/completions"
+LLAMA3_MODEL = "llama3-8b-8192"
+
+# Extract text from PDF
+def load_pdf(pdf_file):
+    reader = PyPDF2.PdfReader(pdf_file)
+    return "".join(page.extract_text() for page in reader.pages)
+
+# Chunk text
+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)]
+
+# Generate embeddings
+def get_embeddings(chunks):
+    return embedding_model.encode(chunks)
+
+# Create FAISS index
+def create_faiss_index(embeddings):
+    index = faiss.IndexFlatL2(embeddings.shape[1])
+    index.add(np.array(embeddings))
+    return index
+
+# Search index
+def search_index(index, query, chunks, top_k=3):
+    q_embed = embedding_model.encode([query])
+    _, indices = index.search(np.array(q_embed), top_k)
+    return [chunks[i] for i in indices[0]]
+
+# Generate answer using GROQ
+def generate_answer(prompt):
+    headers = {
+        "Authorization": f"Bearer {GROQ_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    data = {
+        "model": LLAMA3_MODEL,
+        "messages": [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": prompt}
+        ]
+    }
+    response = requests.post(GROQ_URL, headers=headers, json=data)
+    response.raise_for_status()
+    return response.json()["choices"][0]["message"]["content"]
+
+# Streamlit UI
+def main():
+    st.set_page_config("RAG App", layout="centered")
+    st.title("📄 PDF QA App with LLaMA 3 & GROQ")
+
+    uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
+
+    if uploaded_file and st.button("Process PDF"):
+        with st.spinner("Processing..."):
+            text = load_pdf(uploaded_file)
+            chunks = chunk_text(text)
+            embeddings = get_embeddings(chunks)
+            index = create_faiss_index(embeddings)
+            st.session_state.chunks = chunks
+            st.session_state.index = index
+        st.success("✅ PDF processed and indexed.")
+
+    if "index" in st.session_state:
+        query = st.text_input("Ask a question about the PDF:")
+        if st.button("Get Answer"):
+            with st.spinner("Thinking..."):
+                top_chunks = search_index(st.session_state.index, query, st.session_state.chunks)
+                context = "\n\n".join(top_chunks)
+                prompt = f"Use the following context to answer the question:\n\n{context}\n\nQuestion: {query}"
+                answer = generate_answer(prompt)
+                st.markdown("### 🧠 Answer:")
+                st.write(answer)
+
+if __name__ == "__main__":
+    main()