Update app.py

app.py

@@ -78,53 +78,33 @@ tavily_search = TavilySearchResults(max_results=4)
 
 @app.post("/uploadpdfs")
 async def upload_file(file: UploadFile = File(...)):
-    # Read file contents into memory
-    contents = await file.read()
-    file_stream = BytesIO(contents)
+    # global vector_index
 
-    # Load and parse document directly from memory
-    documents = SimpleDirectoryReader(input_file_objs=[file_stream], input_file_names=[file.filename]).load_data()
+    # Save uploaded file to a temp directory
+    temp_dir = "temp_uploads"
+    os.makedirs(temp_dir, exist_ok=True)
+    file_id = str(uuid.uuid4())
+    file_path = os.path.join(temp_dir, f"{file_id}_{file.filename}")
+
+    with open(file_path, "wb") as f:
+        shutil.copyfileobj(file.file, f)
+
+    # Load and parse document
+    documents = SimpleDirectoryReader(input_files=[file_path]).load_data()
     parser = SentenceSplitter(chunk_size=300, chunk_overlap=50)
     nodes = parser.get_nodes_from_documents(documents)
 
     # Create or update vector index
     embed_model = HuggingFaceEmbedding(model_name="WhereIsAI/UAE-Large-V1")
+    # if vector_index is None:
     if not hasattr(app.state, "vector_index") or app.state.vector_index is None:
         app.state.vector_index = VectorStoreIndex(nodes, embed_model=embed_model)
-        message = "New vector index created and file processed in-memory."
+        message = "New vector index created and file stored."
     else:
         app.state.vector_index.insert_nodes(nodes)
-        message = "File processed in-memory and vector index updated."
+        message = "File stored and vector index updated."
 
     return {"message": message, "filename": file.filename}
-# async def upload_file(file: UploadFile = File(...)):
-#     # global vector_index
-
-#     # Save uploaded file to a temp directory
-#     temp_dir = "temp_uploads"
-#     os.makedirs(temp_dir, exist_ok=True)
-#     file_id = str(uuid.uuid4())
-#     file_path = os.path.join(temp_dir, f"{file_id}_{file.filename}")
-
-#     with open(file_path, "wb") as f:
-#         shutil.copyfileobj(file.file, f)
-
-#     # Load and parse document
-#     documents = SimpleDirectoryReader(input_files=[file_path]).load_data()
-#     parser = SentenceSplitter(chunk_size=300, chunk_overlap=50)
-#     nodes = parser.get_nodes_from_documents(documents)
-
-#     # Create or update vector index
-#     embed_model = HuggingFaceEmbedding(model_name="WhereIsAI/UAE-Large-V1")
-#     # if vector_index is None:
-#     if not hasattr(app.state, "vector_index") or app.state.vector_index is None:
-#         app.state.vector_index = VectorStoreIndex(nodes, embed_model=embed_model)
-#         message = "New vector index created and file stored."
-#     else:
-#         app.state.vector_index.insert_nodes(nodes)
-#         message = "File stored and vector index updated."
-
-#     return {"message": message, "filename": file.filename}
 
 
 class QueryRequest(BaseModel):