import argparse import base64 import copy import io import json import logging import os import random import re import threading import time import traceback import uuid from io import BytesIO from queue import Queue import requests import tiktoken import yaml from diffusers.utils import load_image from huggingface_hub.inference_api import InferenceApi from PIL import Image, ImageDraw from pydub import AudioSegment #tokenizations encodings = { "gpt-4": tiktoken.get_encoding("cl100k_base"), "gpt-4-32k": tiktoken.get_encoding("cl100k_base"), "gpt-3.5-turbo": tiktoken.get_encoding("cl100k_base"), "gpt-3.5-turbo-0301": tiktoken.get_encoding("cl100k_base"), "text-davinci-003": tiktoken.get_encoding("p50k_base"), "text-davinci-002": tiktoken.get_encoding("p50k_base"), "text-davinci-001": tiktoken.get_encoding("r50k_base"), "text-curie-001": tiktoken.get_encoding("r50k_base"), "text-babbage-001": tiktoken.get_encoding("r50k_base"), "text-ada-001": tiktoken.get_encoding("r50k_base"), "davinci": tiktoken.get_encoding("r50k_base"), "curie": tiktoken.get_encoding("r50k_base"), "babbage": tiktoken.get_encoding("r50k_base"), "ada": tiktoken.get_encoding("r50k_base"), } max_length = { "gpt-4": 8192, "gpt-4-32k": 32768, "gpt-3.5-turbo": 4096, "gpt-3.5-turbo-0301": 4096, "text-davinci-003": 4096, "text-davinci-002": 4096, "text-davinci-001": 2049, "text-curie-001": 2049, "text-babbage-001": 2049, "text-ada-001": 2049, "davinci": 2049, "curie": 2049, "babbage": 2049, "ada": 2049 } def count_tokens(model_name, text): return len(encodings[model_name].encode(text)) def get_max_context_length(model_name): return max_length[model_name] def get_token_ids_for_task_parsing(model_name): text = '''{"task": "text-classification", "token-classification", "text2text-generation", "summarization", "translation", "question-answering", "conversational", "text-generation", "sentence-similarity", "tabular-classification", "object-detection", "image-classification", "image-to-image", "image-to-text", "text-to-image", "visual-question-answering", "document-question-answering", "image-segmentation", "text-to-speech", "text-to-video", "automatic-speech-recognition", "audio-to-audio", "audio-classification", "canny-control", "hed-control", "mlsd-control", "normal-control", "openpose-control", "canny-text-to-image", "depth-text-to-image", "hed-text-to-image", "mlsd-text-to-image", "normal-text-to-image", "openpose-text-to-image", "seg-text-to-image", "args", "text", "path", "dep", "id", "-"}''' res = encodings[model_name].encode(text) res = list(set(res)) return res def get_token_ids_for_choose_model(model_name): text = '''{"id": "reason"}''' res = encodings[model_name].encode(text) res = list(set(res)) return res ######### parser = argparse.ArgumentParser() parser.add_argument("--config", type=str, default="swarms/agents/workers/multi_modal_workers/omni_agent/config.yml") parser.add_argument("--mode", type=str, default="cli") args = parser.parse_args() if __name__ != "__main__": args.config = "swarms/agents/workers/multi_modal_workers/omni_agent/config.yml" args.mode = "gradio" config = yaml.load(open(args.config, "r"), Loader=yaml.FullLoader) os.makedirs("logs", exist_ok=True) os.makedirs("public/images", exist_ok=True) os.makedirs("public/audios", exist_ok=True) os.makedirs("public/videos", exist_ok=True) 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.CRITICAL) 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 config["dev"] and 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" API_TYPE = None # priority: local > azure > openai if "dev" in config and config["dev"]: API_TYPE = "local" elif "azure" in config: API_TYPE = "azure" elif "openai" in config: API_TYPE = "openai" else: logger.warning(f"No endpoint specified in {args.config}. The endpoint will be set dynamically according to the client.") if args.mode in ["test", "cli"]: assert API_TYPE, "Only server mode supports dynamic endpoint." API_KEY = None API_ENDPOINT = None if API_TYPE == "local": API_ENDPOINT = f"{config['local']['endpoint']}/v1/{api_name}" elif API_TYPE == "azure": API_ENDPOINT = f"{config['azure']['base_url']}/openai/deployments/{config['azure']['deployment_name']}/{api_name}?api-version={config['azure']['api_version']}" API_KEY = config["azure"]["api_key"] elif API_TYPE == "openai": API_ENDPOINT = f"https://api.openai.com/v1/{api_name}" if config["openai"]["api_key"].startswith("sk-"): # Check for valid OpenAI key in config file API_KEY = config["openai"]["api_key"] elif "OPENAI_API_KEY" in os.environ and os.getenv("OPENAI_API_KEY").startswith("sk-"): # Check for environment variable OPENAI_API_KEY API_KEY = os.getenv("OPENAI_API_KEY") else: raise ValueError(f"Incorrect OpenAI key. Please check your {args.config} file.") PROXY = None if config["proxy"]: PROXY = { "https": config["proxy"], } inference_mode = config["inference_mode"] # check the local_inference_endpoint Model_Server = None if inference_mode!="huggingface": Model_Server = "http://" + config["local_inference_endpoint"]["host"] + ":" + str(config["local_inference_endpoint"]["port"]) message = f"The server of local inference endpoints is not running, please start it first. (or using `inference_mode: huggingface` in {args.config} for a feature-limited experience)" try: r = requests.get(Model_Server + "/running") if r.status_code != 200: raise ValueError(message) except: raise ValueError(message) 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 HUGGINGFACE_HEADERS = {} if config["huggingface"]["token"] and config["huggingface"]["token"].startswith("hf_"): # Check for valid huggingface token in config file HUGGINGFACE_HEADERS = { "Authorization": f"Bearer {config['huggingface']['token']}", } elif "HUGGINGFACE_ACCESS_TOKEN" in os.environ and os.getenv("HUGGINGFACE_ACCESS_TOKEN").startswith("hf_"): # Check for environment variable HUGGINGFACE_ACCESS_TOKEN HUGGINGFACE_HEADERS = { "Authorization": f"Bearer {os.getenv('HUGGINGFACE_ACCESS_TOKEN')}", } else: raise ValueError(f"Incorrect HuggingFace token. Please check your {args.config} file.") 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 += (""+ "user" + "\n" + message['content'] + "\n") elif message['role'] == "assistant": final_prompt += (""+ "assistant" + "\n" + message['content'] + "\n") else: final_prompt += (""+ "system" + "\n" + message['content'] + "\n") final_prompt = tprompt + final_prompt final_prompt = final_prompt + "assistant" data["prompt"] = final_prompt data['stop'] = data.get('stop', [""]) 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): api_key = data.pop("api_key") api_type = data.pop("api_type") api_endpoint = data.pop("api_endpoint") if use_completion: data = convert_chat_to_completion(data) if api_type == "openai": HEADER = { "Authorization": f"Bearer {api_key}" } elif api_type == "azure": HEADER = { "api-key": api_key, "Content-Type": "application/json" } else: HEADER = None response = requests.post(api_endpoint, json=data, headers=HEADER, proxies=PROXY) if "error" in response.json(): return response.json() logger.debug(response.text.strip()) 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() 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 "" 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 "" 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 "" 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, api_key, api_type, api_endpoint): data = { "model": LLM, "messages": messages, "api_key": api_key, "api_type": api_type, "api_endpoint": api_endpoint } return send_request(data) def parse_task(context, input, api_key, api_type, api_endpoint): 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 = "\nuser".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}, "api_key": api_key, "api_type": api_type, "api_endpoint": api_endpoint } return send_request(data) def choose_model(input, task, metas, api_key, api_type, api_endpoint): 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 "api_key": api_key, "api_type": api_type, "api_endpoint": api_endpoint } return send_request(data) def response_results(input, results, api_key, api_type, api_endpoint): 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, "api_key": api_key, "api_type": api_type, "api_endpoint": api_endpoint } return send_request(data) def huggingface_model_inference(model_id, data, task): 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=config["huggingface"]["token"]) # 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"] = 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"] = 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, proxies=PROXY) 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): task_url = f"{Model_Server}/models/{model_id}" # contronlet if model_id.startswith("lllyasviel/sd-controlnet-"): img_url = data["image"] text = data["text"] response = requests.post(task_url, json={"img_url": img_url, "text": text}) results = response.json() if "path" in results: results["generated image"] = results.pop("path") return results if model_id.endswith("-control"): img_url = data["image"] response = requests.post(task_url, json={"img_url": img_url}) results = response.json() if "path" in results: results["generated image"] = results.pop("path") return results if task == "text-to-video": response = requests.post(task_url, json=data) results = response.json() if "path" in results: results["generated video"] = results.pop("path") return results # NLP tasks if task == "question-answering" or task == "sentence-similarity": response = requests.post(task_url, json=data) return response.json() if task in ["text-classification", "token-classification", "text2text-generation", "summarization", "translation", "conversational", "text-generation"]: response = requests.post(task_url, json=data) return response.json() # CV tasks if task == "depth-estimation": img_url = data["image"] response = requests.post(task_url, json={"img_url": img_url}) results = response.json() if "path" in results: results["generated image"] = results.pop("path") return results if task == "image-segmentation": img_url = data["image"] response = requests.post(task_url, json={"img_url": img_url}) results = response.json() results["generated image"] = results.pop("path") return results if task == "image-to-image": img_url = data["image"] response = requests.post(task_url, json={"img_url": img_url}) results = response.json() if "path" in results: results["generated image"] = results.pop("path") return results if task == "text-to-image": response = requests.post(task_url, json=data) results = response.json() if "path" in results: results["generated image"] = results.pop("path") return results if task == "object-detection": img_url = data["image"] response = requests.post(task_url, json={"img_url": img_url}) predicted = response.json() 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"] = 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"] response = requests.post(task_url, json={"img_url": img_url, "text": text}) results = response.json() return results # AUDIO tasks if task == "text-to-speech": response = requests.post(task_url, json=data) results = response.json() 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"] response = requests.post(task_url, json={"audio_url": audio_url}) return response.json() def model_inference(model_id, data, hosted_on, task): if hosted_on == "unknown": localStatusUrl = f"{Model_Server}/status/{model_id}" r = requests.get(localStatusUrl) logger.debug("Local Server Status: " + str(r.json())) if r.status_code == 200 and "loaded" in r.json() and r.json()["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 r.status_code == 200 and "loaded" in r.json() and r.json()["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) 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 = requests.get(url) if r.status_code == 200 and "loaded" in r.json() and r.json()["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=5): all_available_models = {"local": [], "huggingface": []} threads = [] result_queue = Queue() 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": localStatusUrl = f"{Model_Server}/status/{model_id}" thread = threading.Thread(target=get_model_status, args=(model_id, localStatusUrl, {}, 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, api_key, api_type, api_endpoint): 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 "-" 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 "-" 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 "-" 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 "" in args["image"]: if image: args["image"] = image if "audio" in args and "" in args["audio"]: if audio: args["audio"] = audio if "text" in args and "" 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": "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}. Now you are a {command['task']} system, the arguments are {command['args']}. Just help me do {command['task']} and give me the result. The result must be in text form without any urls." }] response = chitchat(messages, api_key, api_type, api_endpoint) 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][:10] all_avaliable_models = get_avaliable_models(candidates, config["num_candidate_models"]) 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("meta").get("language") if model.get("meta") else None, "tags": model.get("meta").get("tags") if model.get("meta") else None, } for model in candidates if model["id"] in all_avaliable_model_ids ] choose_str = choose_model(input, command, cand_models_info, api_key, api_type, api_endpoint) 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: 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']) 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, api_key, api_type, api_endpoint, 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, api_key, api_type, api_endpoint) if "error" in task_str: record_case(success=False, **{"input": input, "task": task_str, "reason": f"task parsing error: {task_str['error']['message']}", "op":"report message"}) return {"message": task_str["error"]["message"]} task_str = task_str.strip() logger.info(task_str) try: tasks = json.loads(task_str) except Exception as e: logger.debug(e) response = chitchat(messages, api_key, api_type, api_endpoint) record_case(success=False, **{"input": input, "task": task_str, "reason": "task parsing fail", "op":"chitchat"}) return {"message": 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, api_key, api_type, api_endpoint) return {"message": 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": "chitchat tasks", "op": "chitchat"}) response = chitchat(messages, api_key, api_type, api_endpoint) return {"message": 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_thread = len(threads) for task in tasks: # logger.debug(f"d.keys(): {d.keys()}, dep: {dep}") for dep_id in task["dep"]: if dep_id >= task["id"]: task["dep"] = [-1] break dep = task["dep"] if dep[0] == -1 or len(list(set(dep).intersection(d.keys()))) == len(dep): tasks.remove(task) thread = threading.Thread(target=run_task, args=(input, task, d, api_key, api_type, api_endpoint)) thread.start() threads.append(thread) if num_thread == 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, api_key, api_type, api_endpoint).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 answer def test(): # single round examples inputs = [ "Given a collection of image A: /examples/a.jpg, B: /examples/b.jpg, C: /examples/c.jpg, please tell me how many zebras in these picture?" "Can you give me a picture of a small bird flying in the sky with trees and clouds. Generate a high definition image if possible.", "Please answer all the named entities in the sentence: Iron Man is a superhero appearing in American comic books published by Marvel Comics. The character was co-created by writer and editor Stan Lee, developed by scripter Larry Lieber, and designed by artists Don Heck and Jack Kirby.", "please dub for me: 'Iron Man is a superhero appearing in American comic books published by Marvel Comics. The character was co-created by writer and editor Stan Lee, developed by scripter Larry Lieber, and designed by artists Don Heck and Jack Kirby.'" "Given an image: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg, please answer the question: What is on top of the building?", "Please generate a canny image based on /examples/f.jpg" ] for input in inputs: messages = [{"role": "user", "content": input}] chat_huggingface(messages, API_KEY, API_TYPE, API_ENDPOINT, return_planning = False, return_results = False) # multi rounds example messages = [ {"role": "user", "content": "Please generate a canny image based on /examples/f.jpg"}, {"role": "assistant", "content": """Sure. I understand your request. Based on the inference results of the models, I have generated a canny image for you. The workflow I used is as follows: First, I used the image-to-text model (nlpconnect/vit-gpt2-image-captioning) to convert the image /examples/f.jpg to text. The generated text is "a herd of giraffes and zebras grazing in a field". Second, I used the canny-control model (canny-control) to generate a canny image from the text. Unfortunately, the model failed to generate the canny image. Finally, I used the canny-text-to-image model (lllyasviel/sd-controlnet-canny) to generate a canny image from the text. The generated image is located at /images/f16d.png. I hope this answers your request. Is there anything else I can help you with?"""}, {"role": "user", "content": """then based on the above canny image and a prompt "a photo of a zoo", generate a new image."""}, ] chat_huggingface(messages, API_KEY, API_TYPE, API_ENDPOINT, return_planning = False, return_results = False) def cli(): messages = [] print("Welcome to Jarvis! A collaborative system that consists of an LLM as the controller and numerous expert models as collaborative executors. Jarvis can plan tasks, schedule Hugging Face models, generate friendly responses based on your requests, and help you with many things. Please enter your request (`exit` to exit).") while True: message = input("[ User ]: ") if message == "exit": break messages.append({"role": "user", "content": message}) answer = chat_huggingface(messages, API_KEY, API_TYPE, API_ENDPOINT, return_planning=False, return_results=False) print("[ Jarvis ]: ", answer["message"]) messages.append({"role": "assistant", "content": answer["message"]}) # def server(): # http_listen = config["http_listen"] # host = http_listen["host"] # port = http_listen["port"] # app = flask.Flask(__name__, static_folder="public", static_url_path="/") # app.config['DEBUG'] = False # CORS(app) # @cross_origin() # @app.route('/tasks', methods=['POST']) # def tasks(): # data = request.get_json() # messages = data["messages"] # api_key = data.get("api_key", API_KEY) # api_endpoint = data.get("api_endpoint", API_ENDPOINT) # api_type = data.get("api_type", API_TYPE) # if api_key is None or api_type is None or api_endpoint is None: # return jsonify({"error": "Please provide api_key, api_type and api_endpoint"}) # response = chat_huggingface(messages, api_key, api_type, api_endpoint, return_planning=True) # return jsonify(response) # @cross_origin() # @app.route('/results', methods=['POST']) # def results(): # data = request.get_json() # messages = data["messages"] # api_key = data.get("api_key", API_KEY) # api_endpoint = data.get("api_endpoint", API_ENDPOINT) # api_type = data.get("api_type", API_TYPE) # if api_key is None or api_type is None or api_endpoint is None: # return jsonify({"error": "Please provide api_key, api_type and api_endpoint"}) # response = chat_huggingface(messages, api_key, api_type, api_endpoint, return_results=True) # return jsonify(response) # @cross_origin() # @app.route('/hugginggpt', methods=['POST']) # def chat(): # data = request.get_json() # messages = data["messages"] # api_key = data.get("api_key", API_KEY) # api_endpoint = data.get("api_endpoint", API_ENDPOINT) # api_type = data.get("api_type", API_TYPE) # if api_key is None or api_type is None or api_endpoint is None: # return jsonify({"error": "Please provide api_key, api_type and api_endpoint"}) # response = chat_huggingface(messages, api_key, api_type, api_endpoint) # return jsonify(response) # print("server running...") # waitress.serve(app, host=host, port=port) # if __name__ == "__main__": # if args.mode == "test": # test() # elif args.mode == "server": # server() # elif args.mode == "cli": # cli()