main.py

from fastapi import FastAPI, HTTPException
from fastapi.staticfiles import StaticFiles

from pydantic import BaseModel
import pickle
import uvicorn

import logging
import os
import shutil
import subprocess

import torch
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceInstructEmbeddings

# from langchain.embeddings import HuggingFaceEmbeddings
from run_localGPT import load_model
from prompt_template_utils import get_prompt_template

# from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.vectorstores import Chroma
from werkzeug.utils import secure_filename

from constants import CHROMA_SETTINGS, EMBEDDING_MODEL_NAME, PERSIST_DIRECTORY, MODEL_ID, MODEL_BASENAME

if torch.backends.mps.is_available():
    DEVICE_TYPE = "mps"
elif torch.cuda.is_available():
    DEVICE_TYPE = "cuda"
else:
    DEVICE_TYPE = "cpu"

SHOW_SOURCES = True
logging.info(f"Running on: {DEVICE_TYPE}")
logging.info(f"Display Source Documents set to: {SHOW_SOURCES}")

EMBEDDINGS = HuggingFaceInstructEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs={"device": DEVICE_TYPE})

# load the vectorstore
DB = Chroma(
    persist_directory=PERSIST_DIRECTORY,
    embedding_function=EMBEDDINGS,
    client_settings=CHROMA_SETTINGS,
)

RETRIEVER = DB.as_retriever()

LLM = load_model(device_type=DEVICE_TYPE, model_id=MODEL_ID, model_basename=MODEL_BASENAME)
prompt, memory = get_prompt_template(promptTemplate_type="llama", history=False)

QA = RetrievalQA.from_chain_type(
    llm=LLM,
    chain_type="stuff",
    retriever=RETRIEVER,
    return_source_documents=SHOW_SOURCES,
    chain_type_kwargs={
        "prompt": prompt,
    },
)

class Predict(BaseModel):
    prompt: str


app = FastAPI()

@app.get("/")
def root():
    return {"API": "An API for Sepsis Prediction."}

# app.mount("/static", StaticFiles(directory="static"), name="static")

@app.post('/predict')
async def predict(data: Predict):
    user_prompt = data.prompt
    if user_prompt:
        # print(f'User Prompt: {user_prompt}')
        # Get the answer from the chain
        res = QA(user_prompt)
        answer, docs = res["result"], res["source_documents"]

        prompt_response_dict = {
            "Prompt": user_prompt,
            "Answer": answer,
        }

        prompt_response_dict["Sources"] = []
        for document in docs:
            prompt_response_dict["Sources"].append(
                (os.path.basename(str(document.metadata["source"])), str(document.page_content))
            )

        return {"response": prompt_response_dict}
    else:
        raise HTTPException(status_code=400, detail="Prompt Incorrect")

@app.get("/run_ingest")
def run_ingest_route():
    try:
        if os.path.exists(PERSIST_DIRECTORY):
            try:
                shutil.rmtree(PERSIST_DIRECTORY)
            except OSError as e:
                raise HTTPException(status_code=500, detail=f"Error: {e.filename} - {e.strerror}.")
        else:
            raise HTTPException(status_code=500, detail="The directory does not exist")

        run_langest_commands = ["python", "ingest.py"]
        if DEVICE_TYPE == "cpu":
            run_langest_commands.append("--device_type")
            run_langest_commands.append(DEVICE_TYPE)

        result = subprocess.run(run_langest_commands, capture_output=True)
        
        if result.returncode != 0:
            raise HTTPException(status_code=400, detail="Script execution failed: {}")
            
        # load the vectorstore
        DB = Chroma(
            persist_directory=PERSIST_DIRECTORY,
            embedding_function=EMBEDDINGS,
            client_settings=CHROMA_SETTINGS,
        )
        RETRIEVER = DB.as_retriever()
        prompt, memory = get_prompt_template(promptTemplate_type="llama", history=False)

        QA = RetrievalQA.from_chain_type(
            llm=LLM,
            chain_type="stuff",
            retriever=RETRIEVER,
            return_source_documents=SHOW_SOURCES,
            chain_type_kwargs={
                "prompt": prompt,
            },
        )

        response = "Script executed successfully: {}".format(result.stdout.decode("utf-8"))
        return {"response": response}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error occurred: {str(e)}")