163 lines
6.4 KiB
Python
163 lines
6.4 KiB
Python
from email.mime import image
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from cv2 import grabCut
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import flask
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import requests
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import json
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import openai
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import re
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from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
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import torch
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import argparse
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ps = argparse.ArgumentParser()
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ps.add_argument("--config", default="config.json", help="Configuration file")
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args = ps.parse_args()
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with open(args.config) as f:
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config_json = json.load(f)
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class GlobalData:
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# OPENAI_ORGID = config_json[""]
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OPENAI_APIKEY = config_json["OpenAI-API-Key"]
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OPENAI_MODEL = config_json["GPT-Model"]
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OPENAI_MODEL_TEMPERATURE = 0.66
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OPENAI_MODEL_MAXTOKENS = 2048
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context_for_users = {}
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context_for_groups = {}
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GENERATE_PICTURE_ARG_PAT = re.compile("(\(|（)([0-9]+)[ \n\t]+([0-9]+)[ \n\t]+([0-9]+)(\)|）)")
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GENERATE_PICTURE_ARG_PAT2 = re.compile("(\(|（)([0-9]+)[ \n\t]+([0-9]+)[ \n\t]+([0-9]+)[ \n\t]+([0-9]+)(\)|）)")
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GENERATE_PICTURE_NEG_PROMPT_DELIMETER = re.compile("\n+")
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GENERATE_PICTURE_MAX_ITS = 200 #最大迭代次数
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app = flask.Flask(__name__)
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sd_pipe = StableDiffusionPipeline.from_pretrained(config_json["Diffusion-Model"], torch_dtype=torch.float32)
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sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
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if torch.backends.mps.is_available():
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sd_pipe = sd_pipe.to("mps")
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elif torch.cuda.is_available():
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sd_pipe = sd_pipe.to("cuda")
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def send_text_to_user(user_id : str, text : str):
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requests.post(url="http://localhost:11110/send_text",
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data=json.dumps({"user_id" : user_id, "text" : text, "in_group" : False}))
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def call_gpt(prompt : str):
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try:
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res = openai.Completion.create(
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model=GlobalData.OPENAI_MODEL,
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prompt=prompt,
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max_tokens=GlobalData.OPENAI_MODEL_MAXTOKENS,
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temperature=GlobalData.OPENAI_MODEL_TEMPERATURE)
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if len(res["choices"]) > 0:
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return res["choices"][0]["text"].strip()
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else:
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return ""
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except:
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return "上下文长度超出模型限制，请对我说\“重置上下文\"，然后再试一次"
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@app.route("/chat_clear", methods=['POST'])
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def app_chat_clear():
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data = json.loads(flask.globals.request.get_data())
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GlobalData.context_for_users[data["user_id"]] = ""
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print(f"Cleared context for {data['user_id']}")
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return ""
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@app.route("/chat", methods=['POST'])
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def app_chat():
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data = json.loads(flask.globals.request.get_data())
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#print(data)
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prompt = GlobalData.context_for_users.get(data["user_id"], "")
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if not data["text"][-1] in ['?', '？', '.', '。', ',', '，', '!', '！']:
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data["text"] += "。"
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prompt += "\n" + data["text"]
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if len(prompt) > 4000:
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prompt = prompt[:4000]
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resp = call_gpt(prompt=prompt)
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GlobalData.context_for_users[data["user_id"]] = (prompt + resp)
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print(f"Prompt = {prompt}\nResponse = {resp}")
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return json.dumps({"user_id" : data["user_id"], "text" : resp, "in_group" : False})
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@app.route("/draw", methods=['POST'])
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def app_draw():
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data = json.loads(flask.globals.request.get_data())
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prompt = data["prompt"]
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i = 0
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for i in range(len(prompt)):
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if prompt[i] == ':' or prompt[i] == '：':
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break
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if i == len(prompt):
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return json.dumps({"user_name" : data["user_name"], "filenames" : [], "error" : True, "error_msg" : "格式不对，正确的格式是：生成图片：Prompt 或者 生成图片(宽 高 迭代次数 [图片最大数量(缺省1)])：Prompt"})
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match_args = re.match(GlobalData.GENERATE_PICTURE_ARG_PAT2, prompt[:i])
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if not match_args is None:
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W = int(match_args.group(2))
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H = int(match_args.group(3))
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ITS = int(match_args.group(4))
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NUM_PIC = int(match_args.group(5))
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else:
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match_args = re.match(GlobalData.GENERATE_PICTURE_ARG_PAT, prompt[:i])
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if not match_args is None:
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W = int(match_args.group(2))
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H = int(match_args.group(3))
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ITS = int(match_args.group(4))
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NUM_PIC = 1
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else:
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if len(prompt[:i].strip()) != 0:
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return json.dumps({"user_name" : data["user_name"], "filenames" : [], "error" : True, "error_msg" : "格式不对，正确的格式是：生成图片：Prompt 或者 生成图片(宽 高 迭代次数 [图片最大数量(缺省1)])：Prompt"})
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else:
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W = 768
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H = 768
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ITS = 50
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NUM_PIC = 1
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if W > 2500 or H > 2500:
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return json.dumps({"user_name" : data["user_name"], "filenames" : [], "error" : True, "error_msg" : "你要求的图片太大了，我不干了～"})
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if ITS > GlobalData.GENERATE_PICTURE_MAX_ITS:
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return json.dumps({"user_name" : data["user_name"], "filenames" : [], "error" : True, "error_msg" : f"迭代次数太多了，不要超过{GlobalData.GENERATE_PICTURE_MAX_ITS}次"})
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prompt = prompt[(i+1):].strip()
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prompts = re.split(GlobalData.GENERATE_PICTURE_NEG_PROMPT_DELIMETER, prompt)
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prompt = prompts[0]
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neg_prompt = None
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if len(prompts) > 1:
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neg_prompt = prompts[1]
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print(f"Generating {NUM_PIC} picture(s) with prompt = {prompt} , negative prompt = {neg_prompt}")
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try:
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if NUM_PIC > 1 and torch.backends.mps.is_available(): #Apple silicon上的bug：https://github.com/huggingface/diffusers/issues/363
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return json.dumps({"user_name" : data["user_name"], "filenames" : [], "error" : True,
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"error_msg" : "单prompt生成多张图像在Apple silicon上无法实现，相关讨论参考https://github.com/huggingface/diffusers/issues/363"})
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images = sd_pipe(prompt=prompt, negative_prompt=neg_prompt, width=W, height=H, num_inference_steps=ITS, num_images_per_prompt=NUM_PIC).images[:NUM_PIC]
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if len(images) == 0:
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return json.dumps({"user_name" : data["user_name"], "filenames" : [], "error" : True, "error_msg" : "没有产生任何图像"})
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filenames = []
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for i, img in enumerate(images):
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img.save(f"latest-{i}.png")
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filenames.append(f"latest-{i}.png")
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return json.dumps({"user_name" : data["user_name"], "filenames" : filenames, "error" : False, "error_msg" : ""})
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except Exception as e:
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return json.dumps({"user_name" : data["user_name"], "filenames" : [], "error" : True, "error_msg" : str(e)})
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if __name__ == "__main__":
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#openai.organization = GlobalData.OPENAI_ORGID
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openai.api_key = GlobalData.OPENAI_APIKEY
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app.run(host="0.0.0.0", port=11111) |