Update app.py

app.py

@@ -12,6 +12,7 @@ load_dotenv()
 openai.api_key = os.getenv("OPENAI_API_KEY")
 
 app = FastAPI()
+
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -20,26 +21,41 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
+# Store documents and vectors per notebook
 notebooks = {}
 
 class Query(BaseModel):
     question: str
     notebook_id: str
 
+@app.get("/")
+def read_root():
+    return {
+        "message": "✅ NotebookLM OpenAI Backend is running!",
+        "endpoints": {
+            "/upload-pdf": "POST a PDF file with notebook_id",
+            "/ask": "POST question + notebook_id to get answer"
+        }
+    }
+
 @app.post("/ask")
 def ask(query: Query):
     nb = notebooks.get(query.notebook_id)
     if not nb:
         return {"answer": "Notebook not found."}
+
     question_embedding = openai.Embedding.create(
         input=[query.question],
         model="text-embedding-ada-002"
     )["data"][0]["embedding"]
+
     if len(nb["texts"]) == 0:
         return {"answer": "No documents indexed in this notebook."}
+
     D, I = nb["index"].search(np.array([question_embedding]).astype("float32"), k=3)
     context = "\n\n".join([f"[{i+1}] {nb['texts'][i]}" for i in I[0]])
     citation_refs = [nb['citations'][i] for i in I[0]]
+
     response = openai.ChatCompletion.create(
         model="gpt-4",
         messages=[
@@ -53,14 +69,23 @@ def ask(query: Query):
 @app.post("/upload-pdf")
 def upload_pdf(notebook_id: str = Form(...), file: UploadFile = File(...)):
     if notebook_id not in notebooks:
-        notebooks[notebook_id] = {"index": faiss.IndexFlatL2(1536), "texts": [], "citations": []}
+        notebooks[notebook_id] = {
+            "index": faiss.IndexFlatL2(1536),
+            "texts": [],
+            "citations": []
+        }
+
     nb = notebooks[notebook_id]
     reader = PdfReader(file.file)
     for i, page in enumerate(reader.pages):
         content = page.extract_text()
         if content:
-            embedding = openai.Embedding.create(input=[content], model="text-embedding-ada-002")["data"][0]["embedding"]
+            embedding = openai.Embedding.create(
+                input=[content],
+                model="text-embedding-ada-002"
+            )["data"][0]["embedding"]
             nb["index"].add(np.array([embedding]).astype("float32"))
             nb["texts"].append(content)
             nb["citations"].append(f"{file.filename}, page {i+1}")
+
     return {"status": f"{file.filename} uploaded and parsed"}