main.py

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


from pydantic import BaseModel
import os
import glob
import shutil
import subprocess

# import torch
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory


# from langchain.embeddings import HuggingFaceEmbeddings
from run_localGPT import load_model

# from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.vectorstores import Chroma

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

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

DEVICE_TYPE = "cuda"

SHOW_SOURCES = True

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, STREAMER = load_model(device_type=DEVICE_TYPE, model_id=MODEL_ID, model_basename=MODEL_BASENAME, stream=False)

template = """you are a helpful, respectful and honest assistant.
Your name is Katara llma. You should only use the source documents provided to answer the questions.
You should only respond only topics that contains in documents use to training.
Use the following pieces of context to answer the question at the end.
Always answer in the most helpful and safe way possible.
If you don't know the answer to a question, just say that you don't know, don't try to make up an answer, don't share false information.
Use 15 sentences maximum. Keep the answer as concise as possible.
Always say "thanks for asking!" at the end of the answer.
Context: {history} \n {context}
Question: {question}
"""

memory = ConversationBufferMemory(input_key="question", memory_key="history")

QA_CHAIN_PROMPT = PromptTemplate(input_variables=["history", "context", "question"], template=template)

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


class Predict(BaseModel):
    prompt: str

class Delete(BaseModel):
    filename: str

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": QA_CHAIN_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:
        # 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))
            )


        generated_text = ""
        for new_text in STREAMER:
            generated_text += new_text
            print(generated_text)

        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")
async def websocket_endpoint(websocket: WebSocket):
    await websocket.accept()
    while True:
        data = await websocket.receive_text()
        await websocket.send_text(f"Message text was: {data}")