server_XL.py

#!/usr/bin/python
"""SillyTavern-extras server main program. See `README.md`."""

import argparse
import base64
from functools import wraps
import gc
import hashlib
from io import BytesIO
import os
from random import randint
import secrets
import sys
import time
from typing import List, Union
import unicodedata

from colorama import Fore, Style, init as colorama_init
import markdown

from PIL import Image

import numpy as np
import torch
from transformers import pipeline

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 webuiapi

from constants import (DEFAULT_SUMMARIZATION_MODEL,
                       DEFAULT_CLASSIFICATION_MODEL,
                       DEFAULT_CAPTIONING_MODEL,
                       DEFAULT_EMBEDDING_MODEL,
                       DEFAULT_SD_MODEL, DEFAULT_REMOTE_SD_HOST, DEFAULT_REMOTE_SD_PORT, PROMPT_PREFIX, NEGATIVE_PROMPT,
                       DEFAULT_CUDA_DEVICE,
                       DEFAULT_CHROMA_PORT)

# --------------------------------------------------------------------------------
# Inits that must run before we proceed any further

colorama_init()

if sys.hexversion < 0x030b0000:
    print(f"{Fore.BLUE}{Style.BRIGHT}Python 3.11 or newer is recommended to run this program.{Style.RESET_ALL}")
    time.sleep(2)

app = Flask(__name__)
CORS(app)  # allow cross-domain requests
Compress(app)  # compress responses

# will be populated later
args = []
modules = []

# --------------------------------------------------------------------------------
# General utilities

def require_module(name):
    """Parametric decorator. Mark an API endpoint implementation as requiring the specified module."""
    def wrapper(fn):
        @wraps(fn)
        def decorated_view(*args, **kwargs):
            if name not in modules:
                abort(403, f"Module '{name}' not enabled in config")
            return fn(*args, **kwargs)
        return decorated_view
    return wrapper

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

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 = []  # will be populated later
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

# --------------------------------------------------------------------------------
# Web API and its support functions

api_key = None  # will be populated later

@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 args.secure and not is_authorize_ignored(request) and getattr(request.authorization, 'token', '') != api_key:
            print(f"{Fore.RED}{Style.NORMAL}WARNING: Unauthorized API key access from {request.remote_addr}{Style.RESET_ALL}")
            response = jsonify({'error': '401: Invalid API key'})
            response.status_code = 401
            return response
    except Exception as e:
        print(f"API key check error: {e}")
        return "401 Unauthorized\n{}\n\n".format(e), 401

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

@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)

# ----------------------------------------
# caption

captioning_pipeline = None  # populated when the module is loaded
def _caption_image(raw_image: Image) -> str:
    return captioning_pipeline(raw_image.convert("RGB"))[0]['generated_text']

@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})

# ----------------------------------------
# summarize

summarization_pipeline = None  # populated when the module is loaded
def _summarize(text: str) -> str:
    summary = normalize_string(summarization_pipeline(text)[0]['summary_text'])
    return summary

def _summarize_chunks(text: str) -> str:
    """Summarize `text`, chunking it if necessary."""
    try:
        return _summarize(text)
    except IndexError:
        print("Sequence length too large for model, cutting text in half and calling again")
        return (_summarize_chunks(text[:(len(text) // 2)]) +
                _summarize_chunks(text[(len(text) // 2):]))

@app.route("/api/summarize", methods=["POST"])
@require_module("summarize")
def api_summarize():
    """Summarize the text posted in the request. Return the summary."""
    data = request.get_json()

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

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

# ----------------------------------------
# classify

classify_module = None  # populated when the module is loaded
def _classify_text(text: str) -> list:
    return classify_module.classify_text_emotion(text)

@app.route("/api/classify", methods=["POST"])
@require_module("classify")
def api_classify():
    """Perform sentiment analysis (classification) on the text posted in the request. Return the result.

    Also, if `talkinghead` is enabled, automatically update its emotion based on the classification result.
    """
    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()
    # TODO: Feature orthogonality: would be better if the client called the `set_emotion` endpoint explicitly
    #       also when it uses `classify`, if it intends to update the talkinghead state.
    if "talkinghead" in modules:  # send emotion to talkinghead
        print("Updating talkinghead emotion from classification results")
        talkinghead.set_emotion_from_classification(classification)
    return jsonify({"classification": classification})

@app.route("/api/classify/labels", methods=["GET"])
@require_module("classify")
def api_classify_labels():
    """Return the available classifier labels for text sentiment (character emotion)."""
    classification = _classify_text("")
    labels = [x["label"] for x in classification]
    if "talkinghead" in modules:
        labels.append('talkinghead')  # Add 'talkinghead' to the labels list
    return jsonify({"labels": labels})

# ----------------------------------------
# talkinghead

talkinghead = None  # populated when the module is loaded

@app.route("/api/talkinghead/load", methods=["POST"])
@require_module("talkinghead")
def api_talkinghead_load():
    """Load the talkinghead sprite posted in the request. Resume animation if the talkinghead module was paused."""
    file = request.files['file']
    # convert stream to bytes and pass to talkinghead
    return talkinghead.talkinghead_load_file(file.stream)

@app.route('/api/talkinghead/load_emotion_templates', methods=["POST"])
@require_module("talkinghead")
def api_talkinghead_load_emotion_templates():
    """Load custom emotion templates for talkinghead, or reset to defaults.

    Input format is JSON::

        {"emotion0": {"morph0": value0,
                      ...}
         ...}

    For details, see `Animator.load_emotion_templates` in `talkinghead/tha3/app/app.py`.

    To reload server defaults, send a blank JSON.

    This API endpoint becomes available after the talkinghead has been launched.
    """
    if talkinghead.global_animator_instance is None:
        abort(400, 'talkinghead not launched')
    data = request.get_json()
    if not len(data):
        data = None  # sending `None` to talkinghead will reset to defaults
    talkinghead.global_animator_instance.load_emotion_templates(data)
    return "OK"

@app.route('/api/talkinghead/load_animator_settings', methods=["POST"])
@require_module("talkinghead")
def api_talkinghead_load_animator_settings():
    """Load custom settings for talkinghead animator and postprocessor, or reset to defaults.

    Input format is JSON::

        {"name0": value0,
         ...}

    For details, see `Animator.load_animator_settings` in `talkinghead/tha3/app/app.py`.

    To reload server defaults, send a blank JSON.

    This API endpoint becomes available after the talkinghead has been launched.
    """
    if talkinghead.global_animator_instance is None:
        abort(400, 'talkinghead not launched')
    data = request.get_json()
    if not len(data):
        data = None  # sending `None` to talkinghead will reset to defaults
    talkinghead.global_animator_instance.load_animator_settings(data)
    return "OK"

@app.route('/api/talkinghead/unload')
@require_module("talkinghead")
def api_talkinghead_unload():
    """Pause the talkinghead module. To resume, load a character via '/api/talkinghead/load'."""
    return talkinghead.unload()

@app.route('/api/talkinghead/start_talking')
@require_module("talkinghead")
def api_talkinghead_start_talking():
    """Start the mouth animation for talking."""
    return talkinghead.start_talking()

@app.route('/api/talkinghead/stop_talking')
@require_module("talkinghead")
def api_talkinghead_stop_talking():
    """Stop the mouth animation for talking."""
    return talkinghead.stop_talking()

@app.route('/api/talkinghead/set_emotion', methods=["POST"])
@require_module("talkinghead")
def api_talkinghead_set_emotion():
    """Set talkinghead character emotion to that posted in the request.

    Input format is JSON::

        {"emotion_name": "curiosity"}

    where the key "emotion_name" is literal, and the value is the emotion to set.

    There is no getter, because SillyTavern keeps its state in the frontend
    and the plugins only act as slaves (in the technological sense of the word).
    """
    data = request.get_json()
    if "emotion_name" not in data or not isinstance(data["emotion_name"], str):
        abort(400, '"emotion_name" is required')
    emotion_name = data["emotion_name"]
    return talkinghead.set_emotion(emotion_name)

@app.route('/api/talkinghead/result_feed')
@require_module("talkinghead")
def api_talkinghead_result_feed():
    """Live character output. Stream of video frames, each as a PNG encoded image."""
    return talkinghead.result_feed()

# ----------------------------------------
# sd

sd_use_remote = None  # populated when the module is loaded
sd_pipe = None
sd_remote = None
sd_model = None
sd_vae = None

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"],
            seed=data["seed"],
            override_settings=dict({
                "CLIP_stop_at_last_layers": data["clip_skip"],
            }),
            override_settings_restore_afterwards=True,
            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

@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,
        "seed": -1,
        "clip_skip": 1,
    }

    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 Ancestral"]

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

    return jsonify({"samplers": samplers})

# ----------------------------------------
# tts

tts_service = None  # populated when the module is loaded

@app.route("/api/tts/speakers", methods=["GET"])
@require_module("silero-tts")
def api_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)

# Added fix for Silero not working as new files were unable to be created if one already existed. - Rolyat 7/7/23
@app.route("/api/tts/generate", methods=["POST"])
@require_module("silero-tts")
def api_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:
        # Remove the destination file if it already exists
        if os.path.exists('test.wav'):
            os.remove('test.wav')

        audio = tts_service.generate(voice["speaker"], voice["text"])
        audio_file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), os.path.basename(audio))

        os.rename(audio, audio_file_path)
        return send_file(audio_file_path, 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 api_tts_play_sample(speaker: str):
    return send_from_directory(SILERO_SAMPLES_PATH, f"{speaker}.wav")

# ----------------------------------------
# edge-tts

edge = None  # populated when the module is loaded

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

@app.route("/api/edge-tts/generate", methods=["POST"])
@require_module("edge-tts")
def api_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"])

# ----------------------------------------
# embeddings

sentence_embedder = None  # populated when the module is loaded

@app.route("/api/embeddings/compute", methods=["POST"])
@require_module("embeddings")
def api_embeddings_compute():
    """For making vector DB keys. Compute the vector embedding of one or more sentences of text.

    Input format is JSON::

        {"text": "Blah blah blah."}

    or::

        {"text": ["Blah blah blah.",
                  ...]}

    Output is also JSON::

        {"embedding": array}

    or::

        {"embedding": [array0,
                       ...]}

    respectively.

    This is the Extras backend for computing embeddings in the Vector Storage builtin extension.
    """
    data = request.get_json()
    if "text" not in data:
        abort(400, '"text" is required')
    sentences: Union[str, List[str]] = data["text"]
    if not (isinstance(sentences, str) or (isinstance(sentences, list) and all(isinstance(x, str) for x in sentences))):
        abort(400, '"text" must be string or array of strings')
    if isinstance(sentences, str):
        nitems = 1
    else:
        nitems = len(sentences)
    print(f"Computing vector embedding for {nitems} item{'s' if nitems != 1 else ''}")
    vectors: Union[np.array, List[np.array]] = sentence_embedder.encode(sentences,
                                                                        show_progress_bar=True,  # on ST-extras console
                                                                        convert_to_numpy=True,
                                                                        normalize_embeddings=True)
    # NumPy arrays are not JSON serializable, so convert to Python lists
    if isinstance(vectors, np.ndarray):
        vectors = vectors.tolist()
    else:  # isinstance(vectors, list) and all(isinstance(x, np.ndarray) for x in vectors)
        vectors = [x.tolist() for x in vectors]
    return jsonify({"embedding": vectors})

# ----------------------------------------
# chromadb

chromadb_client = None  # populated when the module is loaded
chromadb_embed_fn = None

@app.route("/api/chromadb", methods=["POST"])
@require_module("chromadb")
def api_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 api_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()

    try:
        chromadb_client.delete_collection(f"chat-{chat_id_md5}")
        print(f"Collection chat-{chat_id_md5} deleted")
    except ValueError:
        print(f"Collection chat-{chat_id_md5} does not exist, skipping deletion")

    return 'Ok', 200

@app.route("/api/chromadb/query", methods=["POST"])
@require_module("chromadb")
def api_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
    )

    if collection.count() == 0:
        print(f"Queried empty/missing collection for {repr(data['chat_id'])}.")
        return jsonify([])

    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/multiquery", methods=["POST"])
@require_module("chromadb")
def api_chromadb_multiquery():
    data = request.get_json()
    if "chat_list" not in data or not isinstance(data["chat_list"], list):
        abort(400, '"chat_list" is required and should be a list')
    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"]

    messages = []

    for chat_id in data["chat_list"]:
        if not isinstance(chat_id, str):
            continue

        try:
            chat_id_md5 = hashlib.md5(chat_id.encode()).hexdigest()
            collection = chromadb_client.get_collection(
                name=f"chat-{chat_id_md5}", embedding_function=chromadb_embed_fn
            )

            # Skip this chat if the collection is empty
            if collection.count() == 0:
                continue

            n_results_per_chat = min(collection.count(), n_results)
            query_result = collection.query(
                query_texts=[data["query"]],
                n_results=n_results_per_chat,
            )
            documents = query_result["documents"][0]
            ids = query_result["ids"][0]
            metadatas = query_result["metadatas"][0]
            distances = query_result["distances"][0]

            chat_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))
            ]

            messages.extend(chat_messages)
        except Exception as e:
            print(e)

    #remove duplicate msgs, filter down to the right number
    seen = set()
    messages = [d for d in messages if not (d['content'] in seen or seen.add(d['content']))]
    messages = sorted(messages, key=lambda x: x['distance'])[0:n_results]

    return jsonify(messages)

@app.route("/api/chromadb/export", methods=["POST"])
@require_module("chromadb")
def api_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()
    try:
        collection = chromadb_client.get_collection(
            name=f"chat-{chat_id_md5}", embedding_function=chromadb_embed_fn
        )
    except Exception as e:
        print(e)
        abort(400, "Chat collection not found in chromadb")

    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 api_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)
    print(f"Imported {len(ids)} (total {collection.count()}) content entries into {repr(data['chat_id'])}")

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

# ----------------------------------------
# websearch

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

    query = data["query"]
    engine = data["engine"] if "engine" in data else "google"
    import modules.websearch.script as websearch

    if engine == "duckduckgo":
        results = websearch.search_duckduckgo(query)
    else:
        results = websearch.search_google(query)

    return jsonify({"results": results[0], "links": results[1]})

# --------------------------------------------------------------------------------
# Main program

# Setting Root Folders for Silero Generations so it is compatible with STSL, should not effect regular runs. - Rolyat
parent_dir = os.path.dirname(os.path.abspath(__file__))
SILERO_SAMPLES_PATH = os.path.join(parent_dir, "tts_samples")
SILERO_SAMPLE_TEXT = os.path.join(parent_dir)

# Create directories if they don't exist
if not os.path.exists(SILERO_SAMPLES_PATH):
    os.makedirs(SILERO_SAMPLES_PATH)
if not os.path.exists(SILERO_SAMPLE_TEXT):
    os.makedirs(SILERO_SAMPLE_TEXT)

# ----------------------------------------
# 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("--lowvram", action="store_true", help="Runs SD XL with little vram")
parser.add_argument("--cuda", action="store_false", dest="cpu", help="Run the models on the GPU")
parser.add_argument("--cuda-device", help="Specify the CUDA device to use")
parser.add_argument("--mps", "--apple", "--m1", "--m2", action="store_false", dest="cpu", help="Run the models on Apple Silicon")
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"
)
parser.add_argument("--talkinghead-gpu", action="store_true", help="Run the talkinghead animation on the GPU (CPU is default)")
parser.add_argument(
    "--talkinghead-model", type=str, help="The THA3 model to use. 'float' models are fp32, 'half' are fp16. 'auto' (default) picks fp16 for GPU and fp32 for CPU.",
    required=False, default="auto",
    choices=["auto", "standard_float", "separable_float", "standard_half", "separable_half"],
)
parser.add_argument(
    "--talkinghead-models", metavar="HFREPO",
    type=str, help="If THA3 models are not yet installed, use the given HuggingFace repository to install them. Defaults to OktayAlpk/talking-head-anime-3.",
    default="OktayAlpk/talking-head-anime-3"
)

parser.add_argument("--coqui-gpu", action="store_true", help="Run the voice models on the GPU (CPU is default)")
parser.add_argument("--coqui-models", help="Install given Coqui-api TTS model at launch (comma separated list, last one will be loaded at start)")

parser.add_argument("--max-content-length", help="Set the max")
parser.add_argument("--rvc-save-file", action="store_true", help="Save the last rvc input/output audio file into data/tmp/ folder (for research)")

parser.add_argument("--stt-vosk-model-path", help="Load a custom vosk speech-to-text model")
parser.add_argument("--stt-whisper-model-path", help="Load a custom vosk speech-to-text model")
# sd_group = parser.add_mutually_exclusive_group()

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

remote_sd = parser.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)",
)

class SplitArgs(argparse.Action):
    """Remove quotes and split a comma-delimited list into a python list."""
    def __call__(self, parser, namespace, values, option_string=None):
        setattr(namespace, self.dest, values.replace('"', "").replace("'", "").split(","))
parser.add_argument(
    "--enable-modules",
    action=SplitArgs,
    default=[],
    help="Override a list of enabled modules",
)

args = parser.parse_args()

port = args.port if args.port else 5100
host = "0.0.0.0" if args.listen else "localhost"
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_vae = args.sd_vae
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 else []

if not modules:
    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}")

# ----------------------------------------
# Flask init

app.config["MAX_CONTENT_LENGTH"] = 500 * 1024 * 1024
max_content_length = args.max_content_length if args.max_content_length else None
if max_content_length is not None:
    print("Setting MAX_CONTENT_LENGTH to", max_content_length, "Mb")
    app.config["MAX_CONTENT_LENGTH"] = int(max_content_length) * 1024 * 1024

# ----------------------------------------
# Modules init

cuda_device = DEFAULT_CUDA_DEVICE if not args.cuda_device else args.cuda_device
device_string = cuda_device if torch.cuda.is_available() and not args.cpu else 'mps' if torch.backends.mps.is_available() and not args.cpu else 'cpu'
device = torch.device(device_string)
torch_dtype = torch.float32 if device_string != cuda_device 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.{Style.RESET_ALL}")
    if not torch.backends.mps.is_available() and not args.cpu:
        print(f"{Fore.YELLOW}{Style.BRIGHT}torch-mps is not supported on this device.{Style.RESET_ALL}")

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

if "talkinghead" in modules:
    talkinghead_path = os.path.abspath(os.path.join(os.getcwd(), "talkinghead"))
    sys.path.append(talkinghead_path)  # Add the path to the 'tha3' module to the sys.path list

    import sys
    mode = "cuda" if args.talkinghead_gpu else "cpu"
    model = args.talkinghead_model
    if model == "auto":  # default
        # FP16 boosts the rendering performance by ~1.5x, but is only supported on GPU.
        model = "separable_half" if args.talkinghead_gpu else "separable_float"
    print(f"Initializing {Fore.GREEN}{Style.BRIGHT}talkinghead{Style.RESET_ALL} pipeline in {Fore.GREEN}{Style.BRIGHT}{mode}{Style.RESET_ALL} mode with model {Fore.GREEN}{Style.BRIGHT}{model}{Style.RESET_ALL}...")

    try:
        from talkinghead.tha3.app.util import maybe_install_models as talkinghead_maybe_install_models

        # Install the THA3 models if needed
        talkinghead_models_dir = os.path.join(os.getcwd(), "talkinghead", "tha3", "models")
        talkinghead_maybe_install_models(hf_reponame=args.talkinghead_models, modelsdir=talkinghead_models_dir)

        import talkinghead.tha3.app.app as talkinghead
        # mode: choices='The device to use for PyTorch ("cuda" for GPU, "cpu" for CPU).'
        # model: choices=['standard_float', 'separable_float', 'standard_half', 'separable_half'],
        talkinghead.launch(mode, model)

    except ModuleNotFoundError:
        print("Error: Could not import the 'talkinghead' module.")

if "caption" in modules:
    print("Initializing an image captioning model...")
    captioning_pipeline = pipeline('image-to-text', model=captioning_model, device=device_string, torch_dtype=torch_dtype)

if "summarize" in modules:
    print("Initializing a text summarization model...")
    summarization_pipeline = pipeline('summarization', model=summarization_model, device=device_string, torch_dtype=torch_dtype)

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

    print("Initializing Stable Diffusion XL pipeline...")
    sd_device_string = cuda_device if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
    sd_device = torch.device(sd_device_string)
    sd_torch_dtype = torch.float32 if sd_device_string != cuda_device else torch.float16
    if args.lowvram :
        if args.sd_vae :
            vae = AutoencoderKL.from_pretrained(sd_vae,torch_dtype=torch.float16)
            print("Loading With Custom Vae")
            print("Loading With Low Vram")
            sd_pipe = StableDiffusionXLPipeline.from_pretrained(sd_model, vae=vae, custom_pipeline="uooogh/lpw_stable_diffusion_xl", torch_dtype=torch.float16, use_safetensors=True)
            sd_pipe.enable_sequential_cpu_offload()
            sd_pipe.enable_vae_tiling()
            sd_pipe.enable_vae_slicing()
        else :
            sd_pipe = StableDiffusionXLPipeline.from_pretrained(sd_model, custom_pipeline="uooogh/lpw_stable_diffusion_xl", torch_dtype=torch.float16, use_safetensors=True)
            print("Loading With Low Vram")
            sd_pipe.enable_sequential_cpu_offload()
            sd_pipe.enable_vae_tiling()
            sd_pipe.enable_vae_slicing()
    else :
        if args.sd_vae :
            vae = AutoencoderKL.from_pretrained(sd_vae,torch_dtype=torch.float16)
            print("Loading With Custom Vae")
            sd_pipe = StableDiffusionXLPipeline.from_pretrained(sd_model, vae=vae, custom_pipeline="uooogh/lpw_stable_diffusion_xl", torch_dtype=torch.float16, use_safetensors=True).to(sd_device)
        else :
            sd_pipe = StableDiffusionXLPipeline.from_pretrained(sd_model, custom_pipeline="uooogh/lpw_stable_diffusion_xl", torch_dtype=torch.float16, use_safetensors=True).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
    )
    sd_pipe.load_textual_inversion("casque/Stable_Yogis_PDXL_Positives",weight_name="Stable_Yogis_PDXL_Positives.safetensors",token="Stable_Yogis_PDXL_Positives")
    sd_pipe.load_textual_inversion("casque/Stable_Yogis_PDXL_Negatives-neg",weight_name="Stable_Yogis_PDXL_Negatives-neg.safetensors",token="Stable_Yogis_PDXL_Negatives-neg")
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:
        # 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 "embeddings" in modules:
    print("Initializing embeddings")
    from sentence_transformers import SentenceTransformer
    sentence_embedder = SentenceTransformer(embedding_model, device=device_string)

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

    # 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.PersistentClient(path=args.chroma_folder, settings=Settings(anonymized_telemetry=False))
            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.EphemeralClient(Settings(anonymized_telemetry=False))
            print("ChromaDB is running in-memory without persistence.")
    else:
        chroma_port = args.chroma_port if args.chroma_port else DEFAULT_CHROMA_PORT
        chromadb_client = chromadb.HttpClient(host=args.chroma_host, port=chroma_port, settings=Settings(anonymized_telemetry=False))
        print(f"ChromaDB is remotely configured at {args.chroma_host}:{chroma_port}")

    chromadb_embed_fn = embedding_functions.SentenceTransformerEmbeddingFunction(embedding_model, device=device_string)

    # 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 Exception:
        print("Could not ping ChromaDB! If you are running remotely, please check your host and port!")

if "classify" in modules:
    import modules.classify.classify_module as classify_module
    classify_module.init_text_emotion_classifier(classification_model, device, torch_dtype)

if "vosk-stt" in modules:
    print("Initializing Vosk speech-recognition (from ST request file)")
    vosk_model_path = (
        args.stt_vosk_model_path
        if args.stt_vosk_model_path
        else None)

    import modules.speech_recognition.vosk_module as vosk_module

    vosk_module.model = vosk_module.load_model(file_path=vosk_model_path)
    app.add_url_rule("/api/speech-recognition/vosk/process-audio", view_func=vosk_module.process_audio, methods=["POST"])

if "whisper-stt" in modules:
    print("Initializing Whisper speech-recognition (from ST request file)")
    whisper_model_path = (
        args.stt_whisper_model_path
        if args.stt_whisper_model_path
        else None)

    import modules.speech_recognition.whisper_module as whisper_module

    whisper_module.model = whisper_module.load_model(file_path=whisper_model_path)
    app.add_url_rule("/api/speech-recognition/whisper/process-audio", view_func=whisper_module.process_audio, methods=["POST"])

if "streaming-stt" in modules:
    print("Initializing vosk/whisper speech-recognition (from extras server microphone)")
    whisper_model_path = (
        args.stt_whisper_model_path
        if args.stt_whisper_model_path
        else None)

    import modules.speech_recognition.streaming_module as streaming_module

    streaming_module.whisper_model, streaming_module.vosk_model = streaming_module.load_model(file_path=whisper_model_path)
    app.add_url_rule("/api/speech-recognition/streaming/record-and-transcript", view_func=streaming_module.record_and_transcript, methods=["POST"])

if "rvc" in modules:
    print("Initializing RVC voice conversion (from ST request file)")
    print("Increasing server upload limit")
    rvc_save_file = (
        args.rvc_save_file
        if args.rvc_save_file
        else False)

    if rvc_save_file:
        print("RVC saving file option detected, input/output audio will be savec into data/tmp/ folder")

    sys.path.insert(0, 'modules/voice_conversion')

    import modules.voice_conversion.rvc_module as rvc_module
    rvc_module.save_file = rvc_save_file

    if "classify" in modules:
        rvc_module.classification_mode = True

    rvc_module.fix_model_install()
    app.add_url_rule("/api/voice-conversion/rvc/get-models-list", view_func=rvc_module.rvc_get_models_list, methods=["POST"])
    app.add_url_rule("/api/voice-conversion/rvc/upload-models", view_func=rvc_module.rvc_upload_models, methods=["POST"])
    app.add_url_rule("/api/voice-conversion/rvc/process-audio", view_func=rvc_module.rvc_process_audio, methods=["POST"])

if "coqui-tts" in modules:
    mode = "GPU" if args.coqui_gpu else "CPU"
    print("Initializing Coqui TTS client in " + mode + " mode")
    import modules.text_to_speech.coqui.coqui_module as coqui_module

    if mode == "GPU":
        coqui_module.gpu_mode = True

    coqui_models = (
        args.coqui_models
        if args.coqui_models
        else None
    )

    if coqui_models is not None:
        coqui_models = coqui_models.split(",")
        for i in coqui_models:
            if not coqui_module.install_model(i):
                raise ValueError("Coqui model loading failed, most likely a wrong model name in --coqui-models argument, check log above to see which one")

    # Coqui-api models
    app.add_url_rule("/api/text-to-speech/coqui/coqui-api/check-model-state", view_func=coqui_module.coqui_check_model_state, methods=["POST"])
    app.add_url_rule("/api/text-to-speech/coqui/coqui-api/install-model", view_func=coqui_module.coqui_install_model, methods=["POST"])

    # Users models
    app.add_url_rule("/api/text-to-speech/coqui/local/get-models", view_func=coqui_module.coqui_get_local_models, methods=["POST"])

    # Handle both coqui-api/users models
    app.add_url_rule("/api/text-to-speech/coqui/generate-tts", view_func=coqui_module.coqui_generate_tts, methods=["POST"])

# Read an API key from an already existing file. If that file doesn't exist, create it.
if args.secure:
    try:
        with open("api_key.txt", "r") as txt:
            api_key = txt.read().replace('\n', '')
    except Exception:
        api_key = secrets.token_hex(5)
        with open("api_key.txt", "w") as txt:
            txt.write(api_key)

    print(f"{Fore.YELLOW}{Style.BRIGHT}Your API key is {api_key}{Style.RESET_ALL}")
elif args.share and not args.secure:
    print(f"{Fore.RED}{Style.BRIGHT}WARNING: This instance is publicly exposed without an API key! It is highly recommended to restart with the \"--secure\" argument!{Style.RESET_ALL}")
else:
    print(f"{Fore.YELLOW}{Style.BRIGHT}No API key given because you are running locally.{Style.RESET_ALL}")

if args.share:
    print(f"Your Ngrok Tunnel is Ready")

ignore_auth.append(api_tts_play_sample)
ignore_auth.append(api_talkinghead_result_feed)
app.run(host=host, port=port)