Create app.py

app.py

@@ -0,0 +1,115 @@
+import os
+import gradio as gr
+import faiss
+import numpy as np
+from groq import Groq
+from sentence_transformers import SentenceTransformer
+from PyPDF2 import PdfReader
+import requests
+from io import BytesIO
+
+# -------- SETTINGS --------
+EMBED_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
+INDEX_FILE = "faiss_index.bin"
+CHUNKS_FILE = "chunks.npy"
+CHUNK_SIZE = 500
+TOP_K = 3
+
+# ASME PDF URLs (fixed for this app)
+PDF_URLS = [
+    "https://www.asme.org/wwwasmeorg/media/resourcefiles/aboutasme/who%20we%20are/standards_and_certification/asme_codes_and_standards-examples_of_use_for_mechanical_engineering_students.pdf",
+    "https://www.asme.org/wwwasmeorg/media/resourcefiles/campaigns/marketing/2012/the-state-of-mechanical-engineering-survey.pdf"
+]
+
+# Load embedding model
+embedder = SentenceTransformer(EMBED_MODEL)
+embed_dim = embedder.get_sentence_embedding_dimension()
+
+# Initialize or load FAISS + chunks
+if os.path.exists(INDEX_FILE) and os.path.exists(CHUNKS_FILE):
+    index = faiss.read_index(INDEX_FILE)
+    chunks = np.load(CHUNKS_FILE, allow_pickle=True).tolist()
+else:
+    index = faiss.IndexFlatL2(embed_dim)
+    chunks = []
+
+# Groq client
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+# -------- FUNCTIONS --------
+def pdf_url_to_chunks(pdf_url, chunk_size=CHUNK_SIZE):
+    resp = requests.get(pdf_url)
+    resp.raise_for_status()
+    pdf_bytes = BytesIO(resp.content)
+    reader = PdfReader(pdf_bytes)
+    text_all = ""
+    for page in reader.pages:
+        page_text = page.extract_text()
+        if page_text:
+            text_all += page_text + " "
+    words = text_all.split()
+    return [
+        " ".join(words[i:i+chunk_size])
+        for i in range(0, len(words), chunk_size)
+        if len(words[i:i+chunk_size]) > 20
+    ]
+
+def build_vector_db():
+    global index, chunks
+    if len(chunks) > 0:
+        return f"✅ Knowledge base already built with {len(chunks)} chunks."
+    new_chunks = []
+    for url in PDF_URLS:
+        url_chunks = pdf_url_to_chunks(url)
+        new_chunks.extend(url_chunks)
+    embeddings = embedder.encode(new_chunks, convert_to_numpy=True)
+    index.add(embeddings)
+    chunks.extend(new_chunks)
+    faiss.write_index(index, INDEX_FILE)
+    np.save(CHUNKS_FILE, np.array(chunks, dtype=object))
+    return f"✅ Knowledge base built with {len(chunks)} chunks."
+
+def retrieve_chunks(query, top_k=TOP_K):
+    if len(chunks) == 0:
+        return []
+    query_vec = embedder.encode([query], convert_to_numpy=True)
+    distances, indices = index.search(query_vec, top_k)
+    return [chunks[i] for i in indices[0] if i < len(chunks)]
+
+def ask_with_rag(query):
+    if len(chunks) == 0:
+        return "⚠️ Please build the knowledge base first."
+    retrieved = retrieve_chunks(query)
+    context = "\n\n".join(retrieved)
+    prompt = f"""You are an assistant knowledgeable in ASME Standards.
+Context:
+{context}
+
+User Query:
+{query}
+
+Answer using the context. If not found, reply: “I could not find it in the provided ASME documents.”"""
+
+    chat_completion = client.chat.completions.create(
+        messages=[{"role": "user", "content": prompt}],
+        model="llama-3.3-70b-versatile",
+    )
+    return chat_completion.choices[0].message.content
+
+# -------- GRADIO UI --------
+with gr.Blocks() as demo:
+    gr.Markdown("# 📘 ASME RAG Assistant")
+    gr.Markdown("This app is powered by FAISS + Groq LLM. Knowledge base is built from 2 official ASME PDFs.")
+
+    build_btn = gr.Button("Build Knowledge Base")
+    build_status = gr.Textbox(label="Status")
+
+    build_btn.click(build_vector_db, inputs=None, outputs=build_status)
+
+    query_input = gr.Textbox(label="Ask a Question", lines=1)
+    answer_output = gr.Textbox(label="Answer", lines=5)
+    query_btn = gr.Button("Ask")
+
+    query_btn.click(ask_with_rag, inputs=query_input, outputs=answer_output)
+
+demo.launch()