HuggingGPT / awesome_chat.py
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Duplicate from microsoft/HuggingGPT
850769f
import base64
import copy
from io import BytesIO
import io
import os
import random
import time
import traceback
import uuid
import requests
import re
import json
import logging
import argparse
import yaml
from PIL import Image, ImageDraw
from diffusers.utils import load_image
from pydub import AudioSegment
import threading
from queue import Queue
from get_token_ids import get_token_ids_for_task_parsing, get_token_ids_for_choose_model, count_tokens, get_max_context_length
from huggingface_hub.inference_api import InferenceApi
from huggingface_hub.inference_api import ALL_TASKS
from models_server import models, status
from functools import partial
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default="config.yaml.dev")
parser.add_argument("--mode", type=str, default="cli")
args = parser.parse_args()
if __name__ != "__main__":
args.config = "config.gradio.yaml"
config = yaml.load(open(args.config, "r"), Loader=yaml.FullLoader)
if not os.path.exists("logs"):
os.mkdir("logs")
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
handler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
if not config["debug"]:
handler.setLevel(logging.INFO)
logger.addHandler(handler)
log_file = config["log_file"]
if log_file:
filehandler = logging.FileHandler(log_file)
filehandler.setLevel(logging.DEBUG)
filehandler.setFormatter(formatter)
logger.addHandler(filehandler)
LLM = config["model"]
use_completion = config["use_completion"]
# consistent: wrong msra model name
LLM_encoding = LLM
if LLM == "gpt-3.5-turbo":
LLM_encoding = "text-davinci-003"
task_parsing_highlight_ids = get_token_ids_for_task_parsing(LLM_encoding)
choose_model_highlight_ids = get_token_ids_for_choose_model(LLM_encoding)
# ENDPOINT MODEL NAME
# /v1/chat/completions gpt-4, gpt-4-0314, gpt-4-32k, gpt-4-32k-0314, gpt-3.5-turbo, gpt-3.5-turbo-0301
# /v1/completions text-davinci-003, text-davinci-002, text-curie-001, text-babbage-001, text-ada-001, davinci, curie, babbage, ada
if use_completion:
api_name = "completions"
else:
api_name = "chat/completions"
if not config["dev"]:
if not config["openai"]["key"].startswith("sk-") and not config["openai"]["key"]=="gradio":
raise ValueError("Incrorrect OpenAI key. Please check your config.yaml file.")
OPENAI_KEY = config["openai"]["key"]
endpoint = f"https://api.openai.com/v1/{api_name}"
if OPENAI_KEY.startswith("sk-"):
HEADER = {
"Authorization": f"Bearer {OPENAI_KEY}"
}
else:
HEADER = None
else:
endpoint = f"{config['local']['endpoint']}/v1/{api_name}"
HEADER = None
PROXY = None
if config["proxy"]:
PROXY = {
"https": config["proxy"],
}
inference_mode = config["inference_mode"]
parse_task_demos_or_presteps = open(config["demos_or_presteps"]["parse_task"], "r").read()
choose_model_demos_or_presteps = open(config["demos_or_presteps"]["choose_model"], "r").read()
response_results_demos_or_presteps = open(config["demos_or_presteps"]["response_results"], "r").read()
parse_task_prompt = config["prompt"]["parse_task"]
choose_model_prompt = config["prompt"]["choose_model"]
response_results_prompt = config["prompt"]["response_results"]
parse_task_tprompt = config["tprompt"]["parse_task"]
choose_model_tprompt = config["tprompt"]["choose_model"]
response_results_tprompt = config["tprompt"]["response_results"]
MODELS = [json.loads(line) for line in open("data/p0_models.jsonl", "r").readlines()]
MODELS_MAP = {}
for model in MODELS:
tag = model["task"]
if tag not in MODELS_MAP:
MODELS_MAP[tag] = []
MODELS_MAP[tag].append(model)
METADATAS = {}
for model in MODELS:
METADATAS[model["id"]] = model
def convert_chat_to_completion(data):
messages = data.pop('messages', [])
tprompt = ""
if messages[0]['role'] == "system":
tprompt = messages[0]['content']
messages = messages[1:]
final_prompt = ""
for message in messages:
if message['role'] == "user":
final_prompt += ("<im_start>"+ "user" + "\n" + message['content'] + "<im_end>\n")
elif message['role'] == "assistant":
final_prompt += ("<im_start>"+ "assistant" + "\n" + message['content'] + "<im_end>\n")
else:
final_prompt += ("<im_start>"+ "system" + "\n" + message['content'] + "<im_end>\n")
final_prompt = tprompt + final_prompt
final_prompt = final_prompt + "<im_start>assistant"
data["prompt"] = final_prompt
data['stop'] = data.get('stop', ["<im_end>"])
data['max_tokens'] = data.get('max_tokens', max(get_max_context_length(LLM) - count_tokens(LLM_encoding, final_prompt), 1))
return data
def send_request(data):
global HEADER
openaikey = data.pop("openaikey")
if use_completion:
data = convert_chat_to_completion(data)
if openaikey and openaikey.startswith("sk-"):
HEADER = {
"Authorization": f"Bearer {openaikey}"
}
response = requests.post(endpoint, json=data, headers=HEADER, proxies=PROXY)
logger.debug(response.text.strip())
if "choices" not in response.json():
return response.json()
if use_completion:
return response.json()["choices"][0]["text"].strip()
else:
return response.json()["choices"][0]["message"]["content"].strip()
def replace_slot(text, entries):
for key, value in entries.items():
if not isinstance(value, str):
value = str(value)
text = text.replace("{{" + key +"}}", value.replace('"', "'").replace('\n', ""))
return text
def find_json(s):
s = s.replace("\'", "\"")
start = s.find("{")
end = s.rfind("}")
res = s[start:end+1]
res = res.replace("\n", "")
return res
def field_extract(s, field):
try:
field_rep = re.compile(f'{field}.*?:.*?"(.*?)"', re.IGNORECASE)
extracted = field_rep.search(s).group(1).replace("\"", "\'")
except:
field_rep = re.compile(f'{field}:\ *"(.*?)"', re.IGNORECASE)
extracted = field_rep.search(s).group(1).replace("\"", "\'")
return extracted
def get_id_reason(choose_str):
reason = field_extract(choose_str, "reason")
id = field_extract(choose_str, "id")
choose = {"id": id, "reason": reason}
return id.strip(), reason.strip(), choose
def record_case(success, **args):
if success:
f = open("logs/log_success.jsonl", "a")
else:
f = open("logs/log_fail.jsonl", "a")
log = args
f.write(json.dumps(log) + "\n")
f.close()
def image_to_bytes(img_url):
img_byte = io.BytesIO()
type = img_url.split(".")[-1]
load_image(img_url).save(img_byte, format="png")
img_data = img_byte.getvalue()
return img_data
def resource_has_dep(command):
args = command["args"]
for _, v in args.items():
if "<GENERATED>" in v:
return True
return False
def fix_dep(tasks):
for task in tasks:
args = task["args"]
task["dep"] = []
for k, v in args.items():
if "<GENERATED>" in v:
dep_task_id = int(v.split("-")[1])
if dep_task_id not in task["dep"]:
task["dep"].append(dep_task_id)
if len(task["dep"]) == 0:
task["dep"] = [-1]
return tasks
def unfold(tasks):
flag_unfold_task = False
try:
for task in tasks:
for key, value in task["args"].items():
if "<GENERATED>" in value:
generated_items = value.split(",")
if len(generated_items) > 1:
flag_unfold_task = True
for item in generated_items:
new_task = copy.deepcopy(task)
dep_task_id = int(item.split("-")[1])
new_task["dep"] = [dep_task_id]
new_task["args"][key] = item
tasks.append(new_task)
tasks.remove(task)
except Exception as e:
print(e)
traceback.print_exc()
logger.debug("unfold task failed.")
if flag_unfold_task:
logger.debug(f"unfold tasks: {tasks}")
return tasks
def chitchat(messages, openaikey=None):
data = {
"model": LLM,
"messages": messages,
"openaikey": openaikey
}
return send_request(data)
def parse_task(context, input, openaikey=None):
demos_or_presteps = parse_task_demos_or_presteps
messages = json.loads(demos_or_presteps)
messages.insert(0, {"role": "system", "content": parse_task_tprompt})
# cut chat logs
start = 0
while start <= len(context):
history = context[start:]
prompt = replace_slot(parse_task_prompt, {
"input": input,
"context": history
})
messages.append({"role": "user", "content": prompt})
history_text = "<im_end>\nuser<im_start>".join([m["content"] for m in messages])
num = count_tokens(LLM_encoding, history_text)
if get_max_context_length(LLM) - num > 800:
break
messages.pop()
start += 2
logger.debug(messages)
data = {
"model": LLM,
"messages": messages,
"temperature": 0,
"logit_bias": {item: config["logit_bias"]["parse_task"] for item in task_parsing_highlight_ids},
"openaikey": openaikey
}
return send_request(data)
def choose_model(input, task, metas, openaikey = None):
prompt = replace_slot(choose_model_prompt, {
"input": input,
"task": task,
"metas": metas,
})
demos_or_presteps = replace_slot(choose_model_demos_or_presteps, {
"input": input,
"task": task,
"metas": metas
})
messages = json.loads(demos_or_presteps)
messages.insert(0, {"role": "system", "content": choose_model_tprompt})
messages.append({"role": "user", "content": prompt})
logger.debug(messages)
data = {
"model": LLM,
"messages": messages,
"temperature": 0,
"logit_bias": {item: config["logit_bias"]["choose_model"] for item in choose_model_highlight_ids}, # 5
"openaikey": openaikey
}
return send_request(data)
def response_results(input, results, openaikey=None):
results = [v for k, v in sorted(results.items(), key=lambda item: item[0])]
prompt = replace_slot(response_results_prompt, {
"input": input,
})
demos_or_presteps = replace_slot(response_results_demos_or_presteps, {
"input": input,
"processes": results
})
messages = json.loads(demos_or_presteps)
messages.insert(0, {"role": "system", "content": response_results_tprompt})
messages.append({"role": "user", "content": prompt})
logger.debug(messages)
data = {
"model": LLM,
"messages": messages,
"temperature": 0,
"openaikey": openaikey
}
return send_request(data)
def huggingface_model_inference(model_id, data, task, huggingfacetoken=None):
if huggingfacetoken is None:
HUGGINGFACE_HEADERS = {}
else:
HUGGINGFACE_HEADERS = {
"Authorization": f"Bearer {huggingfacetoken}",
}
task_url = f"https://api-inference.huggingface.co/models/{model_id}" # InferenceApi does not yet support some tasks
inference = InferenceApi(repo_id=model_id, token=huggingfacetoken)
# NLP tasks
if task == "question-answering":
inputs = {"question": data["text"], "context": (data["context"] if "context" in data else "" )}
result = inference(inputs)
if task == "sentence-similarity":
inputs = {"source_sentence": data["text1"], "target_sentence": data["text2"]}
result = inference(inputs)
if task in ["text-classification", "token-classification", "text2text-generation", "summarization", "translation", "conversational", "text-generation"]:
inputs = data["text"]
result = inference(inputs)
# CV tasks
if task == "visual-question-answering" or task == "document-question-answering":
img_url = data["image"]
text = data["text"]
img_data = image_to_bytes(img_url)
img_base64 = base64.b64encode(img_data).decode("utf-8")
json_data = {}
json_data["inputs"] = {}
json_data["inputs"]["question"] = text
json_data["inputs"]["image"] = img_base64
result = requests.post(task_url, headers=HUGGINGFACE_HEADERS, json=json_data).json()
# result = inference(inputs) # not support
if task == "image-to-image":
img_url = data["image"]
img_data = image_to_bytes(img_url)
# result = inference(data=img_data) # not support
HUGGINGFACE_HEADERS["Content-Length"] = str(len(img_data))
r = requests.post(task_url, headers=HUGGINGFACE_HEADERS, data=img_data)
result = r.json()
if "path" in result:
result["generated image"] = result.pop("path")
if task == "text-to-image":
inputs = data["text"]
img = inference(inputs)
name = str(uuid.uuid4())[:4]
img.save(f"public/images/{name}.png")
result = {}
result["generated image"] = f"/images/{name}.png"
if task == "image-segmentation":
img_url = data["image"]
img_data = image_to_bytes(img_url)
image = Image.open(BytesIO(img_data))
predicted = inference(data=img_data)
colors = []
for i in range(len(predicted)):
colors.append((random.randint(100, 255), random.randint(100, 255), random.randint(100, 255), 155))
for i, pred in enumerate(predicted):
label = pred["label"]
mask = pred.pop("mask").encode("utf-8")
mask = base64.b64decode(mask)
mask = Image.open(BytesIO(mask), mode='r')
mask = mask.convert('L')
layer = Image.new('RGBA', mask.size, colors[i])
image.paste(layer, (0, 0), mask)
name = str(uuid.uuid4())[:4]
image.save(f"public/images/{name}.jpg")
result = {}
result["generated image with segmentation mask"] = f"/images/{name}.jpg"
result["predicted"] = predicted
if task == "object-detection":
img_url = data["image"]
img_data = image_to_bytes(img_url)
predicted = inference(data=img_data)
image = Image.open(BytesIO(img_data))
draw = ImageDraw.Draw(image)
labels = list(item['label'] for item in predicted)
color_map = {}
for label in labels:
if label not in color_map:
color_map[label] = (random.randint(0, 255), random.randint(0, 100), random.randint(0, 255))
for label in predicted:
box = label["box"]
draw.rectangle(((box["xmin"], box["ymin"]), (box["xmax"], box["ymax"])), outline=color_map[label["label"]], width=2)
draw.text((box["xmin"]+5, box["ymin"]-15), label["label"], fill=color_map[label["label"]])
name = str(uuid.uuid4())[:4]
image.save(f"public/images/{name}.jpg")
result = {}
result["generated image with predicted box"] = f"/images/{name}.jpg"
result["predicted"] = predicted
if task in ["image-classification"]:
img_url = data["image"]
img_data = image_to_bytes(img_url)
result = inference(data=img_data)
if task == "image-to-text":
img_url = data["image"]
img_data = image_to_bytes(img_url)
HUGGINGFACE_HEADERS["Content-Length"] = str(len(img_data))
r = requests.post(task_url, headers=HUGGINGFACE_HEADERS, data=img_data)
result = {}
if "generated_text" in r.json()[0]:
result["generated text"] = r.json()[0].pop("generated_text")
# AUDIO tasks
if task == "text-to-speech":
inputs = data["text"]
response = inference(inputs, raw_response=True)
# response = requests.post(task_url, headers=HUGGINGFACE_HEADERS, json={"inputs": text})
name = str(uuid.uuid4())[:4]
with open(f"public/audios/{name}.flac", "wb") as f:
f.write(response.content)
result = {"generated audio": f"/audios/{name}.flac"}
if task in ["automatic-speech-recognition", "audio-to-audio", "audio-classification"]:
audio_url = data["audio"]
audio_data = requests.get(audio_url, timeout=10).content
response = inference(data=audio_data, raw_response=True)
result = response.json()
if task == "audio-to-audio":
content = None
type = None
for k, v in result[0].items():
if k == "blob":
content = base64.b64decode(v.encode("utf-8"))
if k == "content-type":
type = "audio/flac".split("/")[-1]
audio = AudioSegment.from_file(BytesIO(content))
name = str(uuid.uuid4())[:4]
audio.export(f"public/audios/{name}.{type}", format=type)
result = {"generated audio": f"/audios/{name}.{type}"}
return result
def local_model_inference(model_id, data, task):
inference = partial(models, model_id)
# contronlet
if model_id.startswith("lllyasviel/sd-controlnet-"):
img_url = data["image"]
text = data["text"]
results = inference({"img_url": img_url, "text": text})
if "path" in results:
results["generated image"] = results.pop("path")
return results
if model_id.endswith("-control"):
img_url = data["image"]
results = inference({"img_url": img_url})
if "path" in results:
results["generated image"] = results.pop("path")
return results
if task == "text-to-video":
results = inference(data)
if "path" in results:
results["generated video"] = results.pop("path")
return results
# NLP tasks
if task == "question-answering" or task == "sentence-similarity":
results = inference(json=data)
return results
if task in ["text-classification", "token-classification", "text2text-generation", "summarization", "translation", "conversational", "text-generation"]:
results = inference(json=data)
return results
# CV tasks
if task == "depth-estimation":
img_url = data["image"]
results = inference({"img_url": img_url})
if "path" in results:
results["generated depth image"] = results.pop("path")
return results
if task == "image-segmentation":
img_url = data["image"]
results = inference({"img_url": img_url})
results["generated image with segmentation mask"] = results.pop("path")
return results
if task == "image-to-image":
img_url = data["image"]
results = inference({"img_url": img_url})
if "path" in results:
results["generated image"] = results.pop("path")
return results
if task == "text-to-image":
results = inference(data)
if "path" in results:
results["generated image"] = results.pop("path")
return results
if task == "object-detection":
img_url = data["image"]
predicted = inference({"img_url": img_url})
if "error" in predicted:
return predicted
image = load_image(img_url)
draw = ImageDraw.Draw(image)
labels = list(item['label'] for item in predicted)
color_map = {}
for label in labels:
if label not in color_map:
color_map[label] = (random.randint(0, 255), random.randint(0, 100), random.randint(0, 255))
for label in predicted:
box = label["box"]
draw.rectangle(((box["xmin"], box["ymin"]), (box["xmax"], box["ymax"])), outline=color_map[label["label"]], width=2)
draw.text((box["xmin"]+5, box["ymin"]-15), label["label"], fill=color_map[label["label"]])
name = str(uuid.uuid4())[:4]
image.save(f"public/images/{name}.jpg")
results = {}
results["generated image with predicted box"] = f"/images/{name}.jpg"
results["predicted"] = predicted
return results
if task in ["image-classification", "image-to-text", "document-question-answering", "visual-question-answering"]:
img_url = data["image"]
text = None
if "text" in data:
text = data["text"]
results = inference({"img_url": img_url, "text": text})
return results
# AUDIO tasks
if task == "text-to-speech":
results = inference(data)
if "path" in results:
results["generated audio"] = results.pop("path")
return results
if task in ["automatic-speech-recognition", "audio-to-audio", "audio-classification"]:
audio_url = data["audio"]
results = inference({"audio_url": audio_url})
return results
def model_inference(model_id, data, hosted_on, task, huggingfacetoken=None):
if huggingfacetoken:
HUGGINGFACE_HEADERS = {
"Authorization": f"Bearer {huggingfacetoken}",
}
else:
HUGGINGFACE_HEADERS = None
if hosted_on == "unknown":
r = status(model_id)
logger.debug("Local Server Status: " + str(r))
if "loaded" in r and r["loaded"]:
hosted_on = "local"
else:
huggingfaceStatusUrl = f"https://api-inference.huggingface.co/status/{model_id}"
r = requests.get(huggingfaceStatusUrl, headers=HUGGINGFACE_HEADERS, proxies=PROXY)
logger.debug("Huggingface Status: " + str(r.json()))
if "loaded" in r and r["loaded"]:
hosted_on = "huggingface"
try:
if hosted_on == "local":
inference_result = local_model_inference(model_id, data, task)
elif hosted_on == "huggingface":
inference_result = huggingface_model_inference(model_id, data, task, huggingfacetoken)
except Exception as e:
print(e)
traceback.print_exc()
inference_result = {"error":{"message": str(e)}}
return inference_result
def get_model_status(model_id, url, headers, queue = None):
endpoint_type = "huggingface" if "huggingface" in url else "local"
if "huggingface" in url:
r = requests.get(url, headers=headers, proxies=PROXY)
else:
r = status(model_id)
if "loaded" in r and r["loaded"]:
if queue:
queue.put((model_id, True, endpoint_type))
return True
else:
if queue:
queue.put((model_id, False, None))
return False
def get_avaliable_models(candidates, topk=10, huggingfacetoken = None):
all_available_models = {"local": [], "huggingface": []}
threads = []
result_queue = Queue()
HUGGINGFACE_HEADERS = {
"Authorization": f"Bearer {huggingfacetoken}",
}
for candidate in candidates:
model_id = candidate["id"]
if inference_mode != "local":
huggingfaceStatusUrl = f"https://api-inference.huggingface.co/status/{model_id}"
thread = threading.Thread(target=get_model_status, args=(model_id, huggingfaceStatusUrl, HUGGINGFACE_HEADERS, result_queue))
threads.append(thread)
thread.start()
if inference_mode != "huggingface" and config["local_deployment"] != "minimal":
thread = threading.Thread(target=get_model_status, args=(model_id, "", {}, result_queue))
threads.append(thread)
thread.start()
result_count = len(threads)
while result_count:
model_id, status, endpoint_type = result_queue.get()
if status and model_id not in all_available_models:
all_available_models[endpoint_type].append(model_id)
if len(all_available_models["local"] + all_available_models["huggingface"]) >= topk:
break
result_count -= 1
for thread in threads:
thread.join()
return all_available_models
def collect_result(command, choose, inference_result):
result = {"task": command}
result["inference result"] = inference_result
result["choose model result"] = choose
logger.debug(f"inference result: {inference_result}")
return result
def run_task(input, command, results, openaikey = None, huggingfacetoken = None):
id = command["id"]
args = command["args"]
task = command["task"]
deps = command["dep"]
if deps[0] != -1:
dep_tasks = [results[dep] for dep in deps]
else:
dep_tasks = []
logger.debug(f"Run task: {id} - {task}")
logger.debug("Deps: " + json.dumps(dep_tasks))
if deps[0] != -1:
if "image" in args and "<GENERATED>-" in args["image"]:
resource_id = int(args["image"].split("-")[1])
if "generated image" in results[resource_id]["inference result"]:
args["image"] = results[resource_id]["inference result"]["generated image"]
if "audio" in args and "<GENERATED>-" in args["audio"]:
resource_id = int(args["audio"].split("-")[1])
if "generated audio" in results[resource_id]["inference result"]:
args["audio"] = results[resource_id]["inference result"]["generated audio"]
if "text" in args and "<GENERATED>-" in args["text"]:
resource_id = int(args["text"].split("-")[1])
if "generated text" in results[resource_id]["inference result"]:
args["text"] = results[resource_id]["inference result"]["generated text"]
text = image = audio = None
for dep_task in dep_tasks:
if "generated text" in dep_task["inference result"]:
text = dep_task["inference result"]["generated text"]
logger.debug("Detect the generated text of dependency task (from results):" + text)
elif "text" in dep_task["task"]["args"]:
text = dep_task["task"]["args"]["text"]
logger.debug("Detect the text of dependency task (from args): " + text)
if "generated image" in dep_task["inference result"]:
image = dep_task["inference result"]["generated image"]
logger.debug("Detect the generated image of dependency task (from results): " + image)
elif "image" in dep_task["task"]["args"]:
image = dep_task["task"]["args"]["image"]
logger.debug("Detect the image of dependency task (from args): " + image)
if "generated audio" in dep_task["inference result"]:
audio = dep_task["inference result"]["generated audio"]
logger.debug("Detect the generated audio of dependency task (from results): " + audio)
elif "audio" in dep_task["task"]["args"]:
audio = dep_task["task"]["args"]["audio"]
logger.debug("Detect the audio of dependency task (from args): " + audio)
if "image" in args and "<GENERATED>" in args["image"]:
if image:
args["image"] = image
if "audio" in args and "<GENERATED>" in args["audio"]:
if audio:
args["audio"] = audio
if "text" in args and "<GENERATED>" in args["text"]:
if text:
args["text"] = text
for resource in ["image", "audio"]:
if resource in args and not args[resource].startswith("public/") and len(args[resource]) > 0 and not args[resource].startswith("http"):
args[resource] = f"public/{args[resource]}"
if "-text-to-image" in command['task'] and "text" not in args:
logger.debug("control-text-to-image task, but text is empty, so we use control-generation instead.")
control = task.split("-")[0]
if control == "seg":
task = "image-segmentation"
command['task'] = task
elif control == "depth":
task = "depth-estimation"
command['task'] = task
else:
task = f"{control}-control"
command["args"] = args
logger.debug(f"parsed task: {command}")
if task.endswith("-text-to-image") or task.endswith("-control"):
if inference_mode != "huggingface":
if task.endswith("-text-to-image"):
control = task.split("-")[0]
best_model_id = f"lllyasviel/sd-controlnet-{control}"
else:
best_model_id = task
hosted_on = "local"
reason = "ControlNet is the best model for this task."
choose = {"id": best_model_id, "reason": reason}
logger.debug(f"chosen model: {choose}")
else:
logger.warning(f"Task {command['task']} is not available. ControlNet need to be deployed locally.")
record_case(success=False, **{"input": input, "task": command, "reason": f"Task {command['task']} is not available. ControlNet need to be deployed locally.", "op":"message"})
inference_result = {"error": f"service related to ControlNet is not available."}
results[id] = collect_result(command, "", inference_result)
return False
elif task in ["summarization", "translation", "conversational", "text-generation", "text2text-generation"]: # ChatGPT Can do
best_model_id = "ChatGPT"
reason = "ChatGPT performs well on some NLP tasks as well."
choose = {"id": best_model_id, "reason": reason}
messages = [{
"role": "user",
"content": f"[ {input} ] contains a task in JSON format {command}, 'task' indicates the task type and 'args' indicates the arguments required for the task. Don't explain the task to me, just help me do it and give me the result. The result must be in text form without any urls."
}]
response = chitchat(messages, openaikey)
results[id] = collect_result(command, choose, {"response": response})
return True
else:
if task not in MODELS_MAP:
logger.warning(f"no available models on {task} task.")
record_case(success=False, **{"input": input, "task": command, "reason": f"task not support: {command['task']}", "op":"message"})
inference_result = {"error": f"{command['task']} not found in available tasks."}
results[id] = collect_result(command, "", inference_result)
return False
candidates = MODELS_MAP[task][:20]
all_avaliable_models = get_avaliable_models(candidates, config["num_candidate_models"], huggingfacetoken)
all_avaliable_model_ids = all_avaliable_models["local"] + all_avaliable_models["huggingface"]
logger.debug(f"avaliable models on {command['task']}: {all_avaliable_models}")
if len(all_avaliable_model_ids) == 0:
logger.warning(f"no available models on {command['task']}")
record_case(success=False, **{"input": input, "task": command, "reason": f"no available models: {command['task']}", "op":"message"})
inference_result = {"error": f"no available models on {command['task']} task."}
results[id] = collect_result(command, "", inference_result)
return False
if len(all_avaliable_model_ids) == 1:
best_model_id = all_avaliable_model_ids[0]
hosted_on = "local" if best_model_id in all_avaliable_models["local"] else "huggingface"
reason = "Only one model available."
choose = {"id": best_model_id, "reason": reason}
logger.debug(f"chosen model: {choose}")
else:
cand_models_info = [
{
"id": model["id"],
"inference endpoint": all_avaliable_models.get(
"local" if model["id"] in all_avaliable_models["local"] else "huggingface"
),
"likes": model.get("likes"),
"description": model.get("description", "")[:config["max_description_length"]],
"language": model.get("language"),
"tags": model.get("tags"),
}
for model in candidates
if model["id"] in all_avaliable_model_ids
]
choose_str = choose_model(input, command, cand_models_info, openaikey)
logger.debug(f"chosen model: {choose_str}")
try:
choose = json.loads(choose_str)
reason = choose["reason"]
best_model_id = choose["id"]
hosted_on = "local" if best_model_id in all_avaliable_models["local"] else "huggingface"
except Exception as e:
logger.warning(f"the response [ {choose_str} ] is not a valid JSON, try to find the model id and reason in the response.")
choose_str = find_json(choose_str)
best_model_id, reason, choose = get_id_reason(choose_str)
hosted_on = "local" if best_model_id in all_avaliable_models["local"] else "huggingface"
inference_result = model_inference(best_model_id, args, hosted_on, command['task'], huggingfacetoken)
if "error" in inference_result:
logger.warning(f"Inference error: {inference_result['error']}")
record_case(success=False, **{"input": input, "task": command, "reason": f"inference error: {inference_result['error']}", "op":"message"})
results[id] = collect_result(command, choose, inference_result)
return False
results[id] = collect_result(command, choose, inference_result)
return True
def chat_huggingface(messages, openaikey = None, huggingfacetoken = None, return_planning = False, return_results = False):
start = time.time()
context = messages[:-1]
input = messages[-1]["content"]
logger.info("*"*80)
logger.info(f"input: {input}")
task_str = parse_task(context, input, openaikey)
logger.info(task_str)
if "error" in task_str:
return str(task_str), {}
else:
task_str = task_str.strip()
try:
tasks = json.loads(task_str)
except Exception as e:
logger.debug(e)
response = chitchat(messages, openaikey)
record_case(success=False, **{"input": input, "task": task_str, "reason": "task parsing fail", "op":"chitchat"})
return response, {}
if task_str == "[]": # using LLM response for empty task
record_case(success=False, **{"input": input, "task": [], "reason": "task parsing fail: empty", "op": "chitchat"})
response = chitchat(messages, openaikey)
return response, {}
if len(tasks)==1 and tasks[0]["task"] in ["summarization", "translation", "conversational", "text-generation", "text2text-generation"]:
record_case(success=True, **{"input": input, "task": tasks, "reason": "task parsing fail: empty", "op": "chitchat"})
response = chitchat(messages, openaikey)
best_model_id = "ChatGPT"
reason = "ChatGPT performs well on some NLP tasks as well."
choose = {"id": best_model_id, "reason": reason}
return response, collect_result(tasks[0], choose, {"response": response})
tasks = unfold(tasks)
tasks = fix_dep(tasks)
logger.debug(tasks)
if return_planning:
return tasks
results = {}
threads = []
tasks = tasks[:]
d = dict()
retry = 0
while True:
num_threads = len(threads)
for task in tasks:
dep = task["dep"]
# logger.debug(f"d.keys(): {d.keys()}, dep: {dep}")
for dep_id in dep:
if dep_id >= task["id"]:
task["dep"] = [-1]
dep = [-1]
break
if len(list(set(dep).intersection(d.keys()))) == len(dep) or dep[0] == -1:
tasks.remove(task)
thread = threading.Thread(target=run_task, args=(input, task, d, openaikey, huggingfacetoken))
thread.start()
threads.append(thread)
if num_threads == len(threads):
time.sleep(0.5)
retry += 1
if retry > 160:
logger.debug("User has waited too long, Loop break.")
break
if len(tasks) == 0:
break
for thread in threads:
thread.join()
results = d.copy()
logger.debug(results)
if return_results:
return results
response = response_results(input, results, openaikey).strip()
end = time.time()
during = end - start
answer = {"message": response}
record_case(success=True, **{"input": input, "task": task_str, "results": results, "response": response, "during": during, "op":"response"})
logger.info(f"response: {response}")
return response, results