from fastapi import FastAPI
# from transformers import pipeline
from txtai.embeddings import Embeddings
from txtai.pipeline import Extractor

# NOTE - we configure docs_url to serve the interactive Docs at the root path
# of the app. This way, we can use the docs as a landing page for the app on Spaces.
app = FastAPI(docs_url="/")

# Create embeddings model with content support
embeddings = Embeddings({"path": "sentence-transformers/all-MiniLM-L6-v2", "content": True})
embeddings.load('index')

# Create extractor instance
extractor = Extractor(embeddings, "google/flan-t5-base")

# pipe = pipeline("text2text-generation", model="google/flan-t5-small")


# @app.get("/generate")
# def generate(text: str):
#     """
#     Using the text2text-generation pipeline from `transformers`, generate text
#     from the given input text. The model used is `google/flan-t5-small`, which
#     can be found [here](https://huggingface.co/google/flan-t5-small).
#     """
#     output = pipe(text)
#     return {"output": output[0]["generated_text"]}


def prompt(question):
  return f"""Answer the following question using only the context below. Say 'no answer' when the question can't be answered.
            Question: {question}
            Context: """


def search(query, question=None):
  # Default question to query if empty
  if not question:
    question = query

  return extractor([("answer", query, prompt(question), False)])[0][1]


@app.get("/rag")
def rag(question: str):
    # question = "what is the document about?"
    answer = search(question)
    # print(question, answer)
    return {answer}