| from fastapi import FastAPI |
| from fastapi.staticfiles import StaticFiles |
| from fastapi.responses import FileResponse |
|
|
| from setup_database import get_in_memory_document_store, add_data |
| from setup_modules import create_retriever, create_readers_and_pipeline, text_reader_types, table_reader_types |
| app = FastAPI() |
| document_index = "document" |
| document_store = get_in_memory_document_store(document_index) |
| filenames = ["processed_website_tables","processed_website_text","processed_schedule_tables"] |
| document_store, data = add_data(filenames, document_store, document_index) |
| document_store, retriever = create_retriever(document_store) |
| text_reader_type = text_reader_types['deberta-large'] |
| table_reader_type = table_reader_types['tapas'] |
| pipeline = create_readers_and_pipeline(retriever, text_reader_type, table_reader_type, True, True) |
|
|
|
|
| @app.get("/answer") |
| def t5(input): |
| prediction = pipeline.run( |
| query=input, params={"top_k": 3} |
| ) |
| answer_list = [a.answer for a in prediction["answers"]] |
| print(f"Answer List: {answer_list}") |
| return {"output": ("\n").join(answer_list)} |
|
|
| app.mount("/", StaticFiles(directory="static", html=True), name="static") |
|
|
| @app.get("/") |
| def index() -> FileResponse: |
| return FileResponse(path="/app/static/index.html", media_type="text/html") |
