main.py

from typing import Any, Dict, List

import os
import glob
import shutil
import subprocess
import redis
import torch
import concurrent.futures
import json

from fastapi import FastAPI, HTTPException, UploadFile, WebSocket, WebSocketDisconnect
from fastapi.staticfiles import StaticFiles

from pydantic import BaseModel

# langchain
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import LLMResult
from langchain.vectorstores import Chroma

from prompt_template_utils import get_prompt_template
from load_models import load_model

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

class Predict(BaseModel):
    prompt: str

class Delete(BaseModel):
    filename: str

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

EMBEDDINGS = HuggingFaceInstructEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs={"device": DEVICE_TYPE})
DB = Chroma(persist_directory=PERSIST_DIRECTORY, embedding_function=EMBEDDINGS, client_settings=CHROMA_SETTINGS)
RETRIEVER = DB.as_retriever()


redisClient = redis.Redis(host='localhost', port=6379, db=0)


class MyCustomSyncHandler(BaseCallbackHandler):
    def __init__(self):
        self.message = ''

    def on_llm_new_token(self, token: str, **kwargs) -> Any:
        self.message += token
        redisClient.publish(f'{kwargs["tags"][0]}',  self.message)

    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> Any:
        self.message = "end"
        redisClient.publish(f'{kwargs["tags"][0]}',  self.message)

    def on_llm_error(
        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
    ) -> Any:
        self.message = "end"
        redisClient.publish(f'{kwargs["tags"][0]}',  self.message)

    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> Any:
        self.message = "end"
        redisClient.publish(f'{kwargs["tags"][0]}',  self.message)

LLM = load_model(device_type=DEVICE_TYPE, model_id=MODEL_ID, model_basename=MODEL_BASENAME, stream=True)

prompt, memory = get_prompt_template(promptTemplate_type="llama", history=True)

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


app = FastAPI(title="homepage-app")
api_app = FastAPI(title="api app")

app.mount("/api", api_app, name="api")
app.mount("/", StaticFiles(directory="static",html = True), name="static")

@api_app.get("/training")
def run_ingest_route():
    global DB
    global RETRIEVER
    global QA

    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()

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

        return {"response": "The training was successfully completed"}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error occurred: {str(e)}")

@api_app.get("/api/files")
def get_files():
    upload_dir = os.path.join(os.getcwd(), PATH_NAME_SOURCE_DIRECTORY)
    files = glob.glob(os.path.join(upload_dir, '*'))

    return {"directory": upload_dir, "files": files}

@api_app.delete("/api/delete_document")
def delete_source_route(data: Delete):
    filename = data.filename
    path_source_documents = os.path.join(os.getcwd(), PATH_NAME_SOURCE_DIRECTORY)
    file_to_delete = f"{path_source_documents}/{filename}"

    if os.path.exists(file_to_delete):
        try:
            os.remove(file_to_delete)
            print(f"{file_to_delete} has been deleted.")

            return {"message": f"{file_to_delete} has been deleted."}
        except OSError as e:
            raise HTTPException(status_code=400, detail=print(f"error: {e}."))
    else:
         raise HTTPException(status_code=400, detail=print(f"The file {file_to_delete} does not exist."))

@api_app.post('/predict')
async def predict(data: Predict):
    global QA
    user_prompt = data.prompt
    if user_prompt:
        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")

@api_app.post("/save_document/")
async def create_upload_file(file: UploadFile):
    # Get the file size (in bytes)
    file.file.seek(0, 2)
    file_size = file.file.tell()

    # move the cursor back to the beginning
    await file.seek(0)

    if file_size > 10 * 1024 * 1024:
        # more than 10 MB
        raise HTTPException(status_code=400, detail="File too large")

    content_type = file.content_type

    if content_type not in [
        "text/plain",
        "text/markdown",
        "text/x-markdown",
        "text/csv",
        "application/msword",
        "application/pdf",
        "application/vnd.ms-excel",
        "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
        "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
        "text/x-python",
        "application/x-python-code"]:
        raise HTTPException(status_code=400, detail="Invalid file type")

    upload_dir = os.path.join(os.getcwd(), PATH_NAME_SOURCE_DIRECTORY)
    if not os.path.exists(upload_dir):
        os.makedirs(upload_dir)

    dest = os.path.join(upload_dir, file.filename)

    with open(dest, "wb") as buffer:
        shutil.copyfileobj(file.file, buffer)

    return {"filename": file.filename}

@api_app.websocket("/ws/{client_id}")
async def websocket_endpoint(websocket: WebSocket,  client_id: int):
    global QA

    await websocket.accept()

    try:
        while True:
            prompt = await websocket.receive_text()
            pubsub = redisClient.pubsub()
            pubsub.subscribe(f'{client_id}')

            with concurrent.futures.ThreadPoolExecutor() as executor:
               executor.submit(QA(inputs=prompt, return_only_outputs=True, callbacks=[MyCustomSyncHandler()], tags=f'{client_id}', include_run_info=True))

            i  = 0
            for item in pubsub.listen():
                i+=1

                print(item["type"] == "message")

                if(i > 10 ): pubsub.unsubscribe({client_id})

                # await websocket.send_text(f'{item}')

    except WebSocketDisconnect:
        print('disconnect')
    except RuntimeError as error:
        print(error)