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from fastapi import FastAPI |
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from fastapi.staticfiles import StaticFiles |
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from fastapi.responses import FileResponse |
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from setup_database import get_in_memory_document_store, add_data |
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from setup_modules import create_retriever, create_readers_and_pipeline, text_reader_types, table_reader_types |
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app = FastAPI() |
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document_index = "document" |
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document_store = get_in_memory_document_store(document_index) |
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filenames = ["processed_website_tables","processed_website_text","processed_schedule_tables"] |
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document_store, data = add_data |
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document_store, retriever = create_retriever(document_store) |
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text_reader_type = text_reader_types['deberta-large'] |
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table_reader_type = table_reader_types['tapas'] |
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pipeline = create_readers_and_pipeline(retriever, text_reader_type, table_reader_type, True, True) |
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@app.get("/infer_q") |
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def t5(input): |
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prediction = pipeline.run( |
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query=input, params={"top_k": 10} |
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) |
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answer_list = [a.answer for a in prediction["answers"]] |
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return {"output": ("\n").join(answer_list)} |
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app.mount("/", StaticFiles(directory="static", html=True), name="static") |
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@app.get("/") |
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def index() -> FileResponse: |
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return FileResponse(path="/app/static/index.html", media_type="text/html") |
