server.py

from functools import wraps
from flask import (
    Flask,
    jsonify,
    request,
    Response,
    render_template_string,
    abort,
    send_from_directory,
    send_file,
)
from flask_cors import CORS
from flask_compress import Compress
import markdown
import argparse
from transformers import AutoTokenizer, AutoProcessor, pipeline
from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM
from transformers import BlipForConditionalGeneration
import unicodedata
import torch
import time
import os
import gc
import secrets
from PIL import Image
import base64
from io import BytesIO
from random import randint
import webuiapi
import hashlib
from constants import *
from colorama import Fore, Style, init as colorama_init

colorama_init()


class SplitArgs(argparse.Action):
    def __call__(self, parser, namespace, values, option_string=None):
        setattr(
            namespace, self.dest, values.replace('"', "").replace("'", "").split(",")
        )


# Script arguments
parser = argparse.ArgumentParser(
    prog="SillyTavern Extras", description="Web API for transformers models"
)
parser.add_argument(
    "--port", type=int, help="Specify the port on which the application is hosted"
)
parser.add_argument(
    "--listen", action="store_true", help="Host the app on the local network"
)
parser.add_argument(
    "--share", action="store_true", help="Share the app on CloudFlare tunnel"
)
parser.add_argument("--cpu", action="store_true", help="Run the models on the CPU")
parser.add_argument("--cuda", action="store_false", dest="cpu", help="Run the models on the GPU")
parser.set_defaults(cpu=True)
parser.add_argument("--summarization-model", help="Load a custom summarization model")
parser.add_argument(
    "--classification-model", help="Load a custom text classification model"
)
parser.add_argument("--captioning-model", help="Load a custom captioning model")
parser.add_argument("--embedding-model", help="Load a custom text embedding model")
parser.add_argument("--chroma-host", help="Host IP for a remote ChromaDB instance")
parser.add_argument("--chroma-port", help="HTTP port for a remote ChromaDB instance (defaults to 8000)")
parser.add_argument("--chroma-folder", help="Path for chromadb persistence folder", default='.chroma_db')
parser.add_argument('--chroma-persist', help="Chromadb persistence", default=True, action=argparse.BooleanOptionalAction)
parser.add_argument(
    "--secure", action="store_true", help="Enforces the use of an API key"
)

sd_group = parser.add_mutually_exclusive_group()

local_sd = sd_group.add_argument_group("sd-local")
local_sd.add_argument("--sd-model", help="Load a custom SD image generation model")
local_sd.add_argument("--sd-cpu", help="Force the SD pipeline to run on the CPU", action="store_true")

remote_sd = sd_group.add_argument_group("sd-remote")
remote_sd.add_argument(
    "--sd-remote", action="store_true", help="Use a remote backend for SD"
)
remote_sd.add_argument(
    "--sd-remote-host", type=str, help="Specify the host of the remote SD backend"
)
remote_sd.add_argument(
    "--sd-remote-port", type=int, help="Specify the port of the remote SD backend"
)
remote_sd.add_argument(
    "--sd-remote-ssl", action="store_true", help="Use SSL for the remote SD backend"
)
remote_sd.add_argument(
    "--sd-remote-auth",
    type=str,
    help="Specify the username:password for the remote SD backend (if required)",
)

parser.add_argument(
    "--enable-modules",
    action=SplitArgs,
    default=[],
    help="Override a list of enabled modules",
)

args = parser.parse_args()

port = 7860
host = "0.0.0.0"
summarization_model = (
    args.summarization_model
    if args.summarization_model
    else DEFAULT_SUMMARIZATION_MODEL
)
classification_model = (
    args.classification_model
    if args.classification_model
    else DEFAULT_CLASSIFICATION_MODEL
)
captioning_model = (
    args.captioning_model if args.captioning_model else DEFAULT_CAPTIONING_MODEL
)
embedding_model = (
    args.embedding_model if args.embedding_model else DEFAULT_EMBEDDING_MODEL
)

sd_use_remote = False if args.sd_model else True
sd_model = args.sd_model if args.sd_model else DEFAULT_SD_MODEL
sd_remote_host = args.sd_remote_host if args.sd_remote_host else DEFAULT_REMOTE_SD_HOST
sd_remote_port = args.sd_remote_port if args.sd_remote_port else DEFAULT_REMOTE_SD_PORT
sd_remote_ssl = args.sd_remote_ssl
sd_remote_auth = args.sd_remote_auth

modules = (
    args.enable_modules if args.enable_modules and len(args.enable_modules) > 0 else []
)

if len(modules) == 0:
    print(
        f"{Fore.RED}{Style.BRIGHT}You did not select any modules to run! Choose them by adding an --enable-modules option"
    )
    print(f"Example: --enable-modules=caption,summarize{Style.RESET_ALL}")

# Models init
device_string = "cuda:0" if torch.cuda.is_available() and not args.cpu else "cpu"
device = torch.device(device_string)
torch_dtype = torch.float32 if device_string == "cpu" else torch.float16

if not torch.cuda.is_available() and not args.cpu:
    print(f"{Fore.YELLOW}{Style.BRIGHT}torch-cuda is not supported on this device. Defaulting to CPU mode.{Style.RESET_ALL}")

print(f"{Fore.GREEN}{Style.BRIGHT}Using torch device: {device_string}{Style.RESET_ALL}")

if "caption" in modules:
    print("Initializing an image captioning model...")
    captioning_processor = AutoProcessor.from_pretrained(captioning_model)
    if "blip" in captioning_model:
        captioning_transformer = BlipForConditionalGeneration.from_pretrained(
            captioning_model, torch_dtype=torch_dtype
        ).to(device)
    else:
        captioning_transformer = AutoModelForCausalLM.from_pretrained(
            captioning_model, torch_dtype=torch_dtype
        ).to(device)

if "summarize" in modules:
    print("Initializing a text summarization model...")
    summarization_tokenizer = AutoTokenizer.from_pretrained(summarization_model)
    summarization_transformer = AutoModelForSeq2SeqLM.from_pretrained(
        summarization_model, torch_dtype=torch_dtype
    ).to(device)

if "classify" in modules:
    print("Initializing a sentiment classification pipeline...")
    classification_pipe = pipeline(
        "text-classification",
        model=classification_model,
        top_k=None,
        device=device,
        torch_dtype=torch_dtype,
    )

if "sd" in modules and not sd_use_remote:
    from diffusers import StableDiffusionPipeline
    from diffusers import EulerAncestralDiscreteScheduler

    print("Initializing Stable Diffusion pipeline")
    sd_device_string = (
        "cuda" if torch.cuda.is_available() and not args.sd_cpu else "cpu"
    )
    sd_device = torch.device(sd_device_string)
    sd_torch_dtype = torch.float32 if sd_device_string == "cpu" else torch.float16
    sd_pipe = StableDiffusionPipeline.from_pretrained(
        sd_model, custom_pipeline="lpw_stable_diffusion", torch_dtype=sd_torch_dtype
    ).to(sd_device)
    sd_pipe.safety_checker = lambda images, clip_input: (images, False)
    sd_pipe.enable_attention_slicing()
    # pipe.scheduler = KarrasVeScheduler.from_config(pipe.scheduler.config)
    sd_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(
        sd_pipe.scheduler.config
    )
elif "sd" in modules and sd_use_remote:
    print("Initializing Stable Diffusion connection")
    try:
        sd_remote = webuiapi.WebUIApi(
            host=sd_remote_host, port=sd_remote_port, use_https=sd_remote_ssl
        )
        if sd_remote_auth:
            username, password = sd_remote_auth.split(":")
            sd_remote.set_auth(username, password)
        sd_remote.util_wait_for_ready()
    except Exception as e:
        # remote sd from modules
        print(
            f"{Fore.RED}{Style.BRIGHT}Could not connect to remote SD backend at http{'s' if sd_remote_ssl else ''}://{sd_remote_host}:{sd_remote_port}! Disabling SD module...{Style.RESET_ALL}"
        )
        modules.remove("sd")

if "tts" in modules:
    print("tts module is deprecated. Please use silero-tts instead.")
    modules.remove("tts")
    modules.append("silero-tts")


if "silero-tts" in modules:
    if not os.path.exists(SILERO_SAMPLES_PATH):
        os.makedirs(SILERO_SAMPLES_PATH)
    print("Initializing Silero TTS server")
    from silero_api_server import tts

    tts_service = tts.SileroTtsService(SILERO_SAMPLES_PATH)
    if len(os.listdir(SILERO_SAMPLES_PATH)) == 0:
        print("Generating Silero TTS samples...")
        tts_service.update_sample_text(SILERO_SAMPLE_TEXT)
        tts_service.generate_samples()


if "edge-tts" in modules:
    print("Initializing Edge TTS client")
    import tts_edge as edge


if "chromadb" in modules:
    print("Initializing ChromaDB")
    import chromadb
    import posthog
    from chromadb.config import Settings
    from sentence_transformers import SentenceTransformer

    # Assume that the user wants in-memory unless a host is specified
    # Also disable chromadb telemetry
    posthog.capture = lambda *args, **kwargs: None
    if args.chroma_host is None:
        if args.chroma_persist:
            chromadb_client = chromadb.Client(Settings(anonymized_telemetry=False, persist_directory=args.chroma_folder, chroma_db_impl='duckdb+parquet'))
            print(f"ChromaDB is running in-memory with persistence. Persistence is stored in {args.chroma_folder}. Can be cleared by deleting the folder or purging db.")
        else:
            chromadb_client = chromadb.Client(Settings(anonymized_telemetry=False))
            print(f"ChromaDB is running in-memory without persistence.")
    else:
        chroma_port=(
            args.chroma_port if args.chroma_port else DEFAULT_CHROMA_PORT
        )
        chromadb_client = chromadb.Client(
            Settings(
                anonymized_telemetry=False,
                chroma_api_impl="rest",
                chroma_server_host=args.chroma_host,
                chroma_server_http_port=chroma_port
            )
        )
        print(f"ChromaDB is remotely configured at {args.chroma_host}:{chroma_port}")

    chromadb_embedder = SentenceTransformer(embedding_model)
    chromadb_embed_fn = lambda *args, **kwargs: chromadb_embedder.encode(*args, **kwargs).tolist()

    # Check if the db is connected and running, otherwise tell the user
    try:
        chromadb_client.heartbeat()
        print("Successfully pinged ChromaDB! Your client is successfully connected.")
    except:
        print("Could not ping ChromaDB! If you are running remotely, please check your host and port!")

# Flask init
app = Flask(__name__)
CORS(app)  # allow cross-domain requests
Compress(app) # compress responses
app.config["MAX_CONTENT_LENGTH"] = 100 * 1024 * 1024


def require_module(name):
    def wrapper(fn):
        @wraps(fn)
        def decorated_view(*args, **kwargs):
            if name not in modules:
                abort(403, "Module is disabled by config")
            return fn(*args, **kwargs)

        return decorated_view

    return wrapper


# AI stuff
def classify_text(text: str) -> list:
    output = classification_pipe(
        text,
        truncation=True,
        max_length=classification_pipe.model.config.max_position_embeddings,
    )[0]
    return sorted(output, key=lambda x: x["score"], reverse=True)


def caption_image(raw_image: Image, max_new_tokens: int = 20) -> str:
    inputs = captioning_processor(raw_image.convert("RGB"), return_tensors="pt").to(
        device, torch_dtype
    )
    outputs = captioning_transformer.generate(**inputs, max_new_tokens=max_new_tokens)
    caption = captioning_processor.decode(outputs[0], skip_special_tokens=True)
    return caption


def summarize_chunks(text: str, params: dict) -> str:
    try:
        return summarize(text, params)
    except IndexError:
        print(
            "Sequence length too large for model, cutting text in half and calling again"
        )
        new_params = params.copy()
        new_params["max_length"] = new_params["max_length"] // 2
        new_params["min_length"] = new_params["min_length"] // 2
        return summarize_chunks(
            text[: (len(text) // 2)], new_params
        ) + summarize_chunks(text[(len(text) // 2) :], new_params)


def summarize(text: str, params: dict) -> str:
    # Tokenize input
    inputs = summarization_tokenizer(text, return_tensors="pt").to(device)
    token_count = len(inputs[0])

    bad_words_ids = [
        summarization_tokenizer(bad_word, add_special_tokens=False).input_ids
        for bad_word in params["bad_words"]
    ]
    summary_ids = summarization_transformer.generate(
        inputs["input_ids"],
        num_beams=2,
        max_new_tokens=max(token_count, int(params["max_length"])),
        min_new_tokens=min(token_count, int(params["min_length"])),
        repetition_penalty=float(params["repetition_penalty"]),
        temperature=float(params["temperature"]),
        length_penalty=float(params["length_penalty"]),
        bad_words_ids=bad_words_ids,
    )
    summary = summarization_tokenizer.batch_decode(
        summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
    )[0]
    summary = normalize_string(summary)
    return summary


def normalize_string(input: str) -> str:
    output = " ".join(unicodedata.normalize("NFKC", input).strip().split())
    return output


def generate_image(data: dict) -> Image:
    prompt = normalize_string(f'{data["prompt_prefix"]} {data["prompt"]}')

    if sd_use_remote:
        image = sd_remote.txt2img(
            prompt=prompt,
            negative_prompt=data["negative_prompt"],
            sampler_name=data["sampler"],
            steps=data["steps"],
            cfg_scale=data["scale"],
            width=data["width"],
            height=data["height"],
            restore_faces=data["restore_faces"],
            enable_hr=data["enable_hr"],
            save_images=True,
            send_images=True,
            do_not_save_grid=False,
            do_not_save_samples=False,
        ).image
    else:
        image = sd_pipe(
            prompt=prompt,
            negative_prompt=data["negative_prompt"],
            num_inference_steps=data["steps"],
            guidance_scale=data["scale"],
            width=data["width"],
            height=data["height"],
        ).images[0]

    image.save("./debug.png")
    return image


def image_to_base64(image: Image, quality: int = 75) -> str:
    buffer = BytesIO()
    image.convert("RGB")
    image.save(buffer, format="JPEG", quality=quality)
    img_str = base64.b64encode(buffer.getvalue()).decode("utf-8")
    return img_str
    
ignore_auth = []    

api_key = os.environ.get("password")

def is_authorize_ignored(request):
    view_func = app.view_functions.get(request.endpoint)

    if view_func is not None:
        if view_func in ignore_auth:
            return True
    return False

@app.before_request
def before_request():
    # Request time measuring
    request.start_time = time.time()

    # Checks if an API key is present and valid, otherwise return unauthorized
    # The options check is required so CORS doesn't get angry
    try:
        if request.method != 'OPTIONS' and is_authorize_ignored(request) == False and getattr(request.authorization, 'token', '') != api_key:
            print(f"WARNING: Unauthorized API key access from {request.remote_addr}")
            response = jsonify({ 'error': '401: Invalid API key' })
            response.status_code = 401
            return "personal HF space"
    except Exception as e:
        print(f"API key check error: {e}")
        return "personal HF space"


@app.after_request
def after_request(response):
    duration = time.time() - request.start_time
    response.headers["X-Request-Duration"] = str(duration)
    return response


@app.route("/", methods=["GET"])
def index():
    with open("./README.md", "r", encoding="utf8") as f:
        content = f.read()
    return render_template_string(markdown.markdown(content, extensions=["tables"]))


@app.route("/api/extensions", methods=["GET"])
def get_extensions():
    extensions = dict(
        {
            "extensions": [
                {
                    "name": "not-supported",
                    "metadata": {
                        "display_name": """<span style="white-space:break-spaces;">Extensions serving using Extensions API is no longer supported. Please update the mod from: <a href="https://github.com/Cohee1207/SillyTavern">https://github.com/Cohee1207/SillyTavern</a></span>""",
                        "requires": [],
                        "assets": [],
                    },
                }
            ]
        }
    )
    return jsonify(extensions)


@app.route("/api/caption", methods=["POST"])
@require_module("caption")
def api_caption():
    data = request.get_json()

    if "image" not in data or not isinstance(data["image"], str):
        abort(400, '"image" is required')

    image = Image.open(BytesIO(base64.b64decode(data["image"])))
    image = image.convert("RGB")
    image.thumbnail((512, 512))
    caption = caption_image(image)
    thumbnail = image_to_base64(image)
    print("Caption:", caption, sep="\n")
    gc.collect()
    return jsonify({"caption": caption, "thumbnail": thumbnail})


@app.route("/api/summarize", methods=["POST"])
@require_module("summarize")
def api_summarize():
    data = request.get_json()

    if "text" not in data or not isinstance(data["text"], str):
        abort(400, '"text" is required')

    params = DEFAULT_SUMMARIZE_PARAMS.copy()

    if "params" in data and isinstance(data["params"], dict):
        params.update(data["params"])

    print("Summary input:", data["text"], sep="\n")
    summary = summarize_chunks(data["text"], params)
    print("Summary output:", summary, sep="\n")
    gc.collect()
    return jsonify({"summary": summary})


@app.route("/api/classify", methods=["POST"])
@require_module("classify")
def api_classify():
    data = request.get_json()

    if "text" not in data or not isinstance(data["text"], str):
        abort(400, '"text" is required')

    print("Classification input:", data["text"], sep="\n")
    classification = classify_text(data["text"])
    print("Classification output:", classification, sep="\n")
    gc.collect()
    return jsonify({"classification": classification})


@app.route("/api/classify/labels", methods=["GET"])
@require_module("classify")
def api_classify_labels():
    classification = classify_text("")
    labels = [x["label"] for x in classification]
    return jsonify({"labels": labels})


@app.route("/api/image", methods=["POST"])
@require_module("sd")
def api_image():
    required_fields = {
        "prompt": str,
    }

    optional_fields = {
        "steps": 30,
        "scale": 6,
        "sampler": "DDIM",
        "width": 512,
        "height": 512,
        "restore_faces": False,
        "enable_hr": False,
        "prompt_prefix": PROMPT_PREFIX,
        "negative_prompt": NEGATIVE_PROMPT,
    }

    data = request.get_json()

    # Check required fields
    for field, field_type in required_fields.items():
        if field not in data or not isinstance(data[field], field_type):
            abort(400, f'"{field}" is required')

    # Set optional fields to default values if not provided
    for field, default_value in optional_fields.items():
        type_match = (
            (int, float)
            if isinstance(default_value, (int, float))
            else type(default_value)
        )
        if field not in data or not isinstance(data[field], type_match):
            data[field] = default_value

    try:
        print("SD inputs:", data, sep="\n")
        image = generate_image(data)
        base64image = image_to_base64(image, quality=90)
        return jsonify({"image": base64image})
    except RuntimeError as e:
        abort(400, str(e))


@app.route("/api/image/model", methods=["POST"])
@require_module("sd")
def api_image_model_set():
    data = request.get_json()

    if not sd_use_remote:
        abort(400, "Changing model for local sd is not supported.")
    if "model" not in data or not isinstance(data["model"], str):
        abort(400, '"model" is required')

    old_model = sd_remote.util_get_current_model()
    sd_remote.util_set_model(data["model"], find_closest=False)
    # sd_remote.util_set_model(data['model'])
    sd_remote.util_wait_for_ready()
    new_model = sd_remote.util_get_current_model()

    return jsonify({"previous_model": old_model, "current_model": new_model})


@app.route("/api/image/model", methods=["GET"])
@require_module("sd")
def api_image_model_get():
    model = sd_model

    if sd_use_remote:
        model = sd_remote.util_get_current_model()

    return jsonify({"model": model})


@app.route("/api/image/models", methods=["GET"])
@require_module("sd")
def api_image_models():
    models = [sd_model]

    if sd_use_remote:
        models = sd_remote.util_get_model_names()

    return jsonify({"models": models})


@app.route("/api/image/samplers", methods=["GET"])
@require_module("sd")
def api_image_samplers():
    samplers = ["Euler a"]

    if sd_use_remote:
        samplers = [sampler["name"] for sampler in sd_remote.get_samplers()]

    return jsonify({"samplers": samplers})


@app.route("/api/modules", methods=["GET"])
def get_modules():
    return jsonify({"modules": modules})


@app.route("/api/tts/speakers", methods=["GET"])
@require_module("silero-tts")
def tts_speakers():
    voices = [
        {
            "name": speaker,
            "voice_id": speaker,
            "preview_url": f"{str(request.url_root)}api/tts/sample/{speaker}",
        }
        for speaker in tts_service.get_speakers()
    ]
    return jsonify(voices)


@app.route("/api/tts/generate", methods=["POST"])
@require_module("silero-tts")
def tts_generate():
    voice = request.get_json()
    if "text" not in voice or not isinstance(voice["text"], str):
        abort(400, '"text" is required')
    if "speaker" not in voice or not isinstance(voice["speaker"], str):
        abort(400, '"speaker" is required')
    # Remove asterisks
    voice["text"] = voice["text"].replace("*", "")
    try:
        audio = tts_service.generate(voice["speaker"], voice["text"])
        return send_file(audio, mimetype="audio/x-wav")
    except Exception as e:
        print(e)
        abort(500, voice["speaker"])


@app.route("/api/tts/sample/<speaker>", methods=["GET"])
@require_module("silero-tts")
def tts_play_sample(speaker: str):
    return send_from_directory(SILERO_SAMPLES_PATH, f"{speaker}.wav")


@app.route("/api/edge-tts/list", methods=["GET"])
@require_module("edge-tts")
def edge_tts_list():
    voices = edge.get_voices()
    return jsonify(voices)


@app.route("/api/edge-tts/generate", methods=["POST"])
@require_module("edge-tts")
def edge_tts_generate():
    data = request.get_json()
    if "text" not in data or not isinstance(data["text"], str):
        abort(400, '"text" is required')
    if "voice" not in data or not isinstance(data["voice"], str):
        abort(400, '"voice" is required')
    if "rate" in data and isinstance(data['rate'], int):
        rate = data['rate']
    else:
        rate = 0
    # Remove asterisks
    data["text"] = data["text"].replace("*", "")
    try:
        audio = edge.generate_audio(text=data["text"], voice=data["voice"], rate=rate)
        return Response(audio, mimetype="audio/mpeg")
    except Exception as e:
        print(e)
        abort(500, data["voice"])


@app.route("/api/chromadb", methods=["POST"])
@require_module("chromadb")
def chromadb_add_messages():
    data = request.get_json()
    if "chat_id" not in data or not isinstance(data["chat_id"], str):
        abort(400, '"chat_id" is required')
    if "messages" not in data or not isinstance(data["messages"], list):
        abort(400, '"messages" is required')

    chat_id_md5 = hashlib.md5(data["chat_id"].encode()).hexdigest()
    collection = chromadb_client.get_or_create_collection(
        name=f"chat-{chat_id_md5}", embedding_function=chromadb_embed_fn
    )

    documents = [m["content"] for m in data["messages"]]
    ids = [m["id"] for m in data["messages"]]
    metadatas = [
        {"role": m["role"], "date": m["date"], "meta": m.get("meta", "")}
        for m in data["messages"]
    ]

    collection.upsert(
        ids=ids,
        documents=documents,
        metadatas=metadatas,
    )

    return jsonify({"count": len(ids)})


@app.route("/api/chromadb/purge", methods=["POST"])
@require_module("chromadb")
def chromadb_purge():
    data = request.get_json()
    if "chat_id" not in data or not isinstance(data["chat_id"], str):
        abort(400, '"chat_id" is required')

    chat_id_md5 = hashlib.md5(data["chat_id"].encode()).hexdigest()
    collection = chromadb_client.get_or_create_collection(
        name=f"chat-{chat_id_md5}", embedding_function=chromadb_embed_fn
    )

    count = collection.count()
    collection.delete()
    #Write deletion to persistent folder
    chromadb_client.persist()
    print("ChromaDB embeddings deleted", count)
    return 'Ok', 200


@app.route("/api/chromadb/query", methods=["POST"])
@require_module("chromadb")
def chromadb_query():
    data = request.get_json()
    if "chat_id" not in data or not isinstance(data["chat_id"], str):
        abort(400, '"chat_id" is required')
    if "query" not in data or not isinstance(data["query"], str):
        abort(400, '"query" is required')

    if "n_results" not in data or not isinstance(data["n_results"], int):
        n_results = 1
    else:
        n_results = data["n_results"]

    chat_id_md5 = hashlib.md5(data["chat_id"].encode()).hexdigest()
    collection = chromadb_client.get_or_create_collection(
        name=f"chat-{chat_id_md5}", embedding_function=chromadb_embed_fn
    )

    n_results = min(collection.count(), n_results)
    query_result = collection.query(
        query_texts=[data["query"]],
        n_results=n_results,
    )

    documents = query_result["documents"][0]
    ids = query_result["ids"][0]
    metadatas = query_result["metadatas"][0]
    distances = query_result["distances"][0]

    messages = [
        {
            "id": ids[i],
            "date": metadatas[i]["date"],
            "role": metadatas[i]["role"],
            "meta": metadatas[i]["meta"],
            "content": documents[i],
            "distance": distances[i],
        }
        for i in range(len(ids))
    ]

    return jsonify(messages)


@app.route("/api/chromadb/export", methods=["POST"])
@require_module("chromadb")
def chromadb_export():
    data = request.get_json()
    if "chat_id" not in data or not isinstance(data["chat_id"], str):
        abort(400, '"chat_id" is required')

    chat_id_md5 = hashlib.md5(data["chat_id"].encode()).hexdigest()
    collection = chromadb_client.get_or_create_collection(
        name=f"chat-{chat_id_md5}", embedding_function=chromadb_embed_fn
    )
    collection_content = collection.get()
    documents = collection_content.get('documents', [])
    ids = collection_content.get('ids', [])
    metadatas = collection_content.get('metadatas', [])

    unsorted_content = [
        {
            "id": ids[i],
            "metadata": metadatas[i],
            "document": documents[i],
        }
        for i in range(len(ids))
    ]

    sorted_content = sorted(unsorted_content, key=lambda x: x['metadata']['date'])

    export = {
        "chat_id": data["chat_id"],
        "content": sorted_content
    }

    return jsonify(export)

@app.route("/api/chromadb/import", methods=["POST"])
@require_module("chromadb")
def chromadb_import():
    data = request.get_json()
    content = data['content']
    if "chat_id" not in data or not isinstance(data["chat_id"], str):
        abort(400, '"chat_id" is required')

    chat_id_md5 = hashlib.md5(data["chat_id"].encode()).hexdigest()
    collection = chromadb_client.get_or_create_collection(
        name=f"chat-{chat_id_md5}", embedding_function=chromadb_embed_fn
    )

    documents = [item['document'] for item in content]
    metadatas = [item['metadata'] for item in content]
    ids = [item['id'] for item in content]


    collection.upsert(documents=documents, metadatas=metadatas, ids=ids)

    return jsonify({"count": len(ids)})

ignore_auth.append(tts_play_sample)
app.run(host=host, port=port)