├── .gitignore
├── LICENSE
├── README.md
├── README_model.md
├── main.py
├── main_without_openai.py
├── package.json
├── requirements.txt
├── resources
├── frontend
│ ├── Roboto-Light.ttf
│ ├── canvas.html
│ ├── canvas_double.html
│ ├── index.js
│ └── tmp.html
├── readme
│ ├── overview.png
│ ├── qq.JPG
│ ├── qq2.jpg
│ └── result_allinone_small.png
├── test
│ └── test.jpg
└── workflow
│ ├── nobel_workflow.json
│ └── nobel_workflow_for_install.json
└── src
├── html_modify.py
├── server.py
└── slogan_agent.py
/.gitignore:
--------------------------------------------------------------------------------
1 | input
2 | output
3 | node_modules
4 | **/__pycache__
5 |
6 | .env
7 | package-lock.json
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
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/README.md:
--------------------------------------------------------------------------------
1 |
2 |
3 |
4 |
🏆 EveryoneNobel
5 |
6 |
9 |
10 | | **[Overview](#overview)** | **[News](#news)** | **[Requirements](#requirements)** | **[Quick Start](#quick-start)** | **[Contributors](#contributors)** |
11 |
12 |
13 | ## 💡 Overview
14 |
15 | EveryoneNobel aims to generate **Nobel Prize images for everyone**. We utilizes ComfyUI for image generation and HTML templates to display text on the images. This project serves not only as a process for generating nobel images but also as **a potential universal framework**. This framework transforms the ComfyUI-generated visuals into final products, offering a structured approach for further applications and customization.
16 |
17 | We share how we build the entire app and sell the product in 30 hours in this blog [here](https://mp.weixin.qq.com/s/t3v-h1MzpFKuh0RCMRmjEg).
18 |
19 | You could generate the picture without text [here](https://civitai.com/models/875184?modelVersionId=979771).
20 |
21 |
22 |
23 |
33 |
34 |
97 |
98 |  |
99 |  |
100 |  |
101 |  |
102 |  |
103 |
104 |
105 |
106 | ## 🏄 Star History
107 |
108 | [](https://star-history.com/#16131zzzzzzzz/EveryoneNobel&Date)
109 |
--------------------------------------------------------------------------------
/README_model.md:
--------------------------------------------------------------------------------
1 | # ComfyUI Models
2 | ## 1. Download Models
3 | Download all models below and put it into ComfyUI's `models` folder.
4 | ### SD model
5 | link: https://civitai.com/models/112902/dreamshaper-xl
6 |
7 | path: `models/checkpoints`
8 |
9 | ### Lora model
10 | link: https://civitai.com/models/875184?modelVersionId=979771
11 |
12 | path: `models/loras`
13 |
14 | ### Sam model
15 | link：https://pan.quark.cn/s/f6422ed31f96
16 |
17 | path: `models/sams`
18 |
19 | ### Ground-Dino model
20 | link：https://pan.quark.cn/s/269e85ea0785
21 |
22 | path: `models/grounding-dino`
23 |
24 | ### ControlNet model 1 (Left in the workflow for lineart)
25 | link：https://pan.quark.cn/s/bab5376acd72
26 |
27 | path: `models/controlnet`
28 |
29 | ### ControlNet model 2 (Right in the workflow for pose)
30 | link：https://pan.quark.cn/s/3accad712c54
31 |
32 | path: `models/controlnet`
33 |
34 | ### VAE model
35 | link：https://pan.quark.cn/s/da810e21331e
36 |
37 | path: `models/vae`
38 |
39 | ## 2. Adapt the model paths
40 | Adapt the model paths in the workflow nodes below to the your own path.
41 |
42 | [](https://www.picgo.net/image/1280X1280-%281%29.oqa7JG)
43 |
44 | [](https://www.picgo.net/image/7fc490b0-5923-4dbc-a650-a3d31374050e.oqahXk)
45 |
46 | [](https://www.picgo.net/image/7787472d-c1b0-4947-896b-928146f3aabb.oqaOlw)
47 |
48 | [](https://www.picgo.net/image/15113787-baab-4a02-99ee-eccdd0011c11.oqaF5l)
49 |
50 | ## 3. Save as API format
51 | Run ComfyUI locally in your browser. !!!Delete all preview image node except the final output preview image!!!. Once it’s running, go to the settings and enable the "Dev Mode Options". You can export the workflow in API format as a JSON file. Use this exported JSON to replace the original resources/workflow/nobel_workflow.json (don't change the json name).
52 |
53 | [](https://www.picgo.net/image/PixPin-2024-10-27-13-48-00.oqmTsi)
54 |
55 | [](https://www.picgo.net/image/PixPin-2024-10-27-13-48-22.oqmKLf)
56 |
--------------------------------------------------------------------------------
/main.py:
--------------------------------------------------------------------------------
1 | import sys
2 | import os
3 | import threading
4 | import json
5 | import argparse
6 | from dotenv import load_dotenv
7 |
8 | from src.slogan_agent import get_slogan, prompt_dict
9 | from src.html_modify import modifier_html
10 | from src.server import prompt_image_to_image
11 |
12 | def main(name, subject, content, image_path, comfy_server_address):
13 | load_dotenv()
14 | api_key = os.getenv("API_KEY")
15 | proj_dir = os.path.dirname(os.path.abspath(__file__))
16 |
17 | name_safe = name.replace(" ", "_")
18 | output_path = os.path.join(proj_dir, "output", name_safe)
19 | os.makedirs(output_path, exist_ok=True)
20 | html_template_path = "resources/frontend/canvas.html"
21 |
22 | output_pic_path = f"nobel_{name_safe}_{(image_path.split('/')[-1]).split('.')[0]}.jpg"
23 | output_pic_path = os.path.join(output_path, output_pic_path)
24 | output_html_path = os.path.join(proj_dir, "resources/frontend/tmp.html")
25 |
26 | # get slogan
27 | print("start get slogan")
28 | subject_content, name, contribution_content = get_slogan(
29 | content, name, prompt_dict, api_key
30 | )
31 |
32 | # save config
33 | info = {
34 | "name": name,
35 | "content": subject_content,
36 | "image_path": image_path,
37 | "contribution": contribution_content,
38 | "output_image_path": output_pic_path,
39 | }
40 | print(info)
41 | json_file_path = os.path.join(
42 | output_path, f"info_{name}_{(image_path.split('/')[-1]).split('.')[0]}.json"
43 | )
44 | with open(json_file_path, "w", encoding="utf-8") as json_file:
45 | json.dump(info, json_file, ensure_ascii=False, indent=4)
46 |
47 | # comfy
48 | print("start comfy")
49 | output_filename = prompt_image_to_image(
50 | "resources/workflow/nobel_workflow.json",
51 | image_path,
52 | comfy_server_address,
53 | output_path=output_path,
54 | save_previews=True,
55 | )
56 | print("comfy image written into: ", output_filename)
57 |
58 | # template
59 | html_modifier = modifier_html(
60 | html_template_path, subject_content, name, contribution_content, output_filename
61 | )
62 |
63 | # save html
64 | html_modifier.save_changes(output_html_path)
65 | print("end generate image html")
66 |
67 | # start npm run dev thread
68 | def start_npm_dev(input_html_path, output_pic_path):
69 | os.system(f"npm run dev -- {input_html_path} {output_pic_path}")
70 |
71 | threading.Thread(target=start_npm_dev, args=(output_html_path, output_pic_path)).start()
72 |
73 | if __name__ == "__main__":
74 | parser = argparse.ArgumentParser(description='Generate Nobel Prize Slogan and Image.')
75 | parser.add_argument('--name', required=True, help='Name of the individual')
76 | parser.add_argument('--subject', required=True, help='Subject of the prize')
77 | parser.add_argument('--content', required=True, help='Contribution description')
78 | parser.add_argument('--image_path', required=True, help='Path to the input image')
79 | parser.add_argument('--comfy_server_address', default="127.0.0.1:6006", help='Address of the Comfy server')
80 |
81 | args = parser.parse_args()
82 |
83 | main(args.name, args.subject, args.content, args.image_path, args.comfy_server_address)
84 |
--------------------------------------------------------------------------------
/main_without_openai.py:
--------------------------------------------------------------------------------
1 | import os
2 | import threading
3 | import json
4 | import argparse
5 |
6 | from src.html_modify import modifier_html
7 | from src.server import prompt_image_to_image
8 |
9 | def main(name, subject, content, image_path, comfy_server_address):
10 | proj_dir = os.path.dirname(os.path.abspath(__file__))
11 |
12 | name_safe = name.replace(" ", "_")
13 | output_path = os.path.join(proj_dir, "output", name_safe)
14 | os.makedirs(output_path, exist_ok=True)
15 | html_template_path = "resources/frontend/canvas.html"
16 |
17 | output_pic_path = f"nobel_{name_safe}_{(image_path.split('/')[-1]).split('.')[0]}.jpg"
18 | output_pic_path = os.path.join(output_path, output_pic_path)
19 | output_html_path = os.path.join(proj_dir, "resources/frontend/tmp.html")
20 |
21 | subject_content = subject
22 | contribution_content = content
23 |
24 | # save config
25 | info = {
26 | "name": name,
27 | "content": subject_content,
28 | "image_path": image_path,
29 | "contribution": contribution_content,
30 | "output_image_path": output_pic_path,
31 | }
32 | print(info)
33 | json_file_path = os.path.join(
34 | output_path, f"info_{name}_{(image_path.split('/')[-1]).split('.')[0]}.json"
35 | )
36 | with open(json_file_path, "w", encoding="utf-8") as json_file:
37 | json.dump(info, json_file, ensure_ascii=False, indent=4)
38 |
39 | # comfy
40 | print("start comfy")
41 | output_filename = prompt_image_to_image(
42 | "resources/workflow/nobel_workflow.json",
43 | image_path,
44 | comfy_server_address,
45 | output_path=output_path,
46 | save_previews=True,
47 | )
48 | print("comfy image written into: ", output_filename)
49 |
50 | # template
51 | html_modifier = modifier_html(
52 | html_template_path, subject_content, name, contribution_content, output_filename
53 | )
54 |
55 | # save html
56 | html_modifier.save_changes(output_html_path)
57 | print("end generate image html")
58 |
59 | # start npm run dev thread
60 | def start_npm_dev(input_html_path, output_pic_path):
61 | os.system(f"npm run dev -- {input_html_path} {output_pic_path}")
62 |
63 | threading.Thread(target=start_npm_dev, args=(output_html_path, output_pic_path)).start()
64 |
65 | if __name__ == "__main__":
66 | parser = argparse.ArgumentParser(description='Generate Nobel Prize Slogan and Image.')
67 | parser.add_argument('--name', required=True, help='Name of the individual')
68 | parser.add_argument('--subject', required=True, help='Subject of the prize')
69 | parser.add_argument('--content', required=True, help='Contribution description')
70 | parser.add_argument('--image_path', required=True, help='Path to the input image')
71 | parser.add_argument('--comfy_server_address', default="127.0.0.1:6006", help='Address of the Comfy server')
72 |
73 | args = parser.parse_args()
74 |
75 | main(args.name, args.subject, args.content, args.image_path, args.comfy_server_address)
76 |
--------------------------------------------------------------------------------
/package.json:
--------------------------------------------------------------------------------
1 | {
2 | "name": "convert-test",
3 | "version": "1.0.0",
4 | "description": "",
5 | "main": "resources/frontend/index.js",
6 | "scripts": {
7 | "dev": "node resources/frontend/index.js"
8 | },
9 | "keywords": [],
10 | "author": "",
11 | "license": "ISC",
12 | "dependencies": {
13 | "puppeteer": "^23.5.1"
14 | }
15 | }
16 |
--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
1 | pypinyin==0.53.0
2 | openai==1.40.3
3 | beautifulsoup4==4.12.3
4 | websocket-client==0.58.0
5 | python-dotenv
--------------------------------------------------------------------------------
/resources/frontend/Roboto-Light.ttf:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/16131zzzzzzzz/EveryoneNobel/aeaddcabc12aa7443d9fececb8fbe7e1b269b051/resources/frontend/Roboto-Light.ttf
--------------------------------------------------------------------------------
/resources/frontend/canvas.html:
--------------------------------------------------------------------------------
1 |
2 |
3 |
4 |
5 |
6 |
7 | Canvas with Colored Bottom and Text Overlay
8 |
75 |
76 |
77 |
78 |
79 |
80 |
81 |
82 |
THE NOBEL PRIZE IN PHYSICS 2024
83 |
Iron Man
84 |
”哥们发现了新元素，你呢？“
85 |
86 |
86 |
87 |
88 |
89 |
90 |
91 |
THE NOBEL PRIZE
IN MAGA 2024
92 |
Donald Trump
93 |
Elon Musk
94 |
”MAKE AMERICAN GREAT AGAIN“
95 |
96 |
78 |
79 |
80 |
81 |
82 |
THE NOBEL PRIZE IN DO NOTHING 2024
83 |
somebody
84 |
”无所事事却能评出最有趣的作品，真是懒人界的奇迹“
85 |
86 |
")[-1]
100 | subject_content_font = int(30 * min(60 / len(subject_content), 1))
101 |
102 | contribution_content_font = int(20 * min(1, 120 / len(contribution_content)))
103 | # 修改内容
104 | html_modifier.update_subject(subject_content)
105 | html_modifier.update_name(name)
106 | html_modifier.update_contribution(contribution_content)
107 |
108 | html_modifier.update_image_source(img_path)
109 | html_modifier.update_image_source2(img_path2)
110 | html_modifier.update_name2(name2)
111 | html_modifier.update_style(
112 | "subject", "font-size", f"{subject_content_font}px"
113 | ) # 更新名字字体大小
114 | html_modifier.update_style(
115 | "contribution", "font-size", f"{contribution_content_font}px"
116 | ) # 更新名字字体大小
117 | return html_modifier
118 |
119 |
120 | def modifier_html(
121 | html_template_path, subject_content, name, contribution_content, img_path
122 | ):
123 | # 使用示例
124 | html_modifier = HtmlModifier(html_template_path)
125 |
126 | subject_content_sencond_row = subject_content.split("
")[-1]
127 | subject_content_font = int(30 * min(60 / len(subject_content), 1))
128 |
129 | contribution_content_font = int(20 * min(1, 120 / len(contribution_content)))
130 | # 修改内容
131 | html_modifier.update_subject(subject_content)
132 | html_modifier.update_name(name)
133 | html_modifier.update_contribution(contribution_content)
134 |
135 | html_modifier.update_image_source(img_path)
136 | html_modifier.update_style(
137 | "subject", "font-size", f"{subject_content_font}px"
138 | ) # 更新名字字体大小
139 | html_modifier.update_style(
140 | "contribution", "font-size", f"{contribution_content_font}px"
141 | ) # 更新名字字体大小
142 | return html_modifier
143 |
144 |
145 | if __name__ == "__main__":
146 | html_template_path = "canvas.html"
147 | new_path = "1.html"
148 | subject_content = "THE NOBEL PRIZE IN COMPUTER SCIENCE 2024"
149 | name = "Zhihong Zhu"
150 | contribution_content = "”玩原神玩的“"
151 | img_path = "zhu.png"
152 | img_path2 = "zhu.png"
153 | name2 = "原神"
154 | html_modifier = modifier_html(
155 | html_template_path, subject_content, name, contribution_content, img_path
156 | )
157 | # html_modifier = modifier_html_double(html_template_path,subject_content,name,name2,contribution_content,img_path,img_path2)
158 | html_modifier.save_changes(new_path)
159 |
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/src/server.py:
--------------------------------------------------------------------------------
1 | import json
2 | import websocket # NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
3 | import uuid
4 | import urllib.request
5 | from requests_toolbelt import MultipartEncoder
6 | import urllib.parse
7 | import random
8 | import os
9 | import io
10 | from PIL import Image
11 |
12 |
13 | def open_websocket_connection(comfy_server_address):
14 | server_address = comfy_server_address
15 | client_id = str(uuid.uuid4())
16 | ws = websocket.WebSocket()
17 | ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
18 | return ws, server_address, client_id
19 |
20 |
21 | def queue_prompt(prompt, client_id, server_address):
22 | p = {"prompt": prompt, "client_id": client_id}
23 | headers = {"Content-Type": "application/json"}
24 | data = json.dumps(p).encode("utf-8")
25 | req = urllib.request.Request(
26 | "http://{}/prompt".format(server_address), data=data, headers=headers
27 | )
28 | return json.loads(urllib.request.urlopen(req).read())
29 |
30 |
31 | def get_history(prompt_id, server_address):
32 | with urllib.request.urlopen(
33 | "http://{}/history/{}".format(server_address, prompt_id)
34 | ) as response:
35 | return json.loads(response.read())
36 |
37 |
38 | def get_image(filename, subfolder, folder_type, server_address):
39 | data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
40 | url_values = urllib.parse.urlencode(data)
41 | with urllib.request.urlopen(
42 | "http://{}/view?{}".format(server_address, url_values)
43 | ) as response:
44 | return response.read()
45 |
46 |
47 | def upload_image(input_path, name, server_address, image_type="input", overwrite=True):
48 | with open(input_path, "rb") as file:
49 | multipart_data = MultipartEncoder(
50 | fields={
51 | "image": (name, file, "image/png"),
52 | "type": image_type,
53 | "overwrite": str(overwrite).lower(),
54 | }
55 | )
56 |
57 | data = multipart_data
58 | headers = {"Content-Type": multipart_data.content_type}
59 | request = urllib.request.Request(
60 | "http://{}/upload/image".format(server_address), data=data, headers=headers
61 | )
62 | with urllib.request.urlopen(request) as response:
63 | return response.read()
64 |
65 |
66 | def load_workflow(workflow_path):
67 | try:
68 | with open(workflow_path, "r") as file:
69 | workflow = json.load(file)
70 | return json.dumps(workflow)
71 | except FileNotFoundError:
72 | print(f"The file {workflow_path} was not found.")
73 | return None
74 | except json.JSONDecodeError:
75 | print(f"The file {workflow_path} contains invalid JSON.")
76 | return None
77 |
78 |
79 | def prompt_image_to_image(
80 | workflow_path, input_path, comfy_server_address, output_path="./output/", save_previews=False
81 | ):
82 | with open(workflow_path, "r", encoding="utf-8") as workflow_api_txt2gif_file:
83 | prompt = json.load(workflow_api_txt2gif_file)
84 | filename = input_path.split("/")[-1]
85 | prompt.get("104")["inputs"]["image"] = filename
86 | file_name = generate_image_by_prompt_and_image(
87 | prompt, output_path, input_path, filename, comfy_server_address, save_previews
88 | )
89 | return file_name
90 |
91 |
92 | def generate_image_by_prompt_and_image(
93 | prompt, output_path, input_path, filename, comfy_server_address, save_previews=False
94 | ):
95 | try:
96 | ws, server_address, client_id = open_websocket_connection(comfy_server_address)
97 | upload_image(input_path, filename, server_address)
98 | prompt_id = queue_prompt(prompt, client_id, server_address)["prompt_id"]
99 | track_progress(prompt, ws, prompt_id)
100 | images = get_images(prompt_id, server_address, save_previews)
101 | save_image(images, output_path, save_previews)
102 | finally:
103 | ws.close()
104 | return os.path.join(output_path, images[-1]["file_name"])
105 |
106 |
107 | def track_progress(prompt, ws, prompt_id):
108 | node_ids = list(prompt.keys())
109 | finished_nodes = []
110 |
111 | while True:
112 | out = ws.recv()
113 | if isinstance(out, str):
114 | message = json.loads(out)
115 | if message["type"] == "progress":
116 | data = message["data"]
117 | current_step = data["value"]
118 | print("In K-Sampler -> Step: ", current_step, " of: ", data["max"])
119 | if message["type"] == "execution_cached":
120 | data = message["data"]
121 | for itm in data["nodes"]:
122 | if itm not in finished_nodes:
123 | finished_nodes.append(itm)
124 | print(
125 | "Progess: ",
126 | len(finished_nodes),
127 | "/",
128 | len(node_ids),
129 | " Tasks done",
130 | )
131 | if message["type"] == "executing":
132 | data = message["data"]
133 | if data["node"] not in finished_nodes:
134 | finished_nodes.append(data["node"])
135 | print(
136 | "Progess: ",
137 | len(finished_nodes),
138 | "/",
139 | len(node_ids),
140 | " Tasks done",
141 | )
142 |
143 | if data["node"] is None and data["prompt_id"] == prompt_id:
144 | break # Execution is done
145 | else:
146 | continue
147 | return
148 |
149 |
150 | # 从history通过prompt_id找到图片
151 | # TODO: (warning!!!) prompt_id要保证每次都不一样
152 | # TODO: (warning!!!) 不知道是否大规模运行，history多了后会导致卡顿，可能需要清理机制
153 | # TODO: 固定模板应该可以不用遍历 节省时间
154 | def get_images(prompt_id, server_address, allow_preview=False):
155 | output_images = []
156 |
157 | history = get_history(prompt_id, server_address)[prompt_id]
158 | for node_id in history["outputs"]:
159 | node_output = history["outputs"][node_id]
160 | if "images" in node_output:
161 | output_data = {}
162 | for image in node_output["images"]:
163 | if allow_preview and image["type"] == "temp":
164 | preview_data = get_image(
165 | image["filename"],
166 | image["subfolder"],
167 | image["type"],
168 | server_address,
169 | )
170 | output_data["image_data"] = preview_data
171 | if image["type"] == "output":
172 | image_data = get_image(
173 | image["filename"],
174 | image["subfolder"],
175 | image["type"],
176 | server_address,
177 | )
178 | output_data["image_data"] = image_data
179 | output_data["file_name"] = image["filename"]
180 | output_data["type"] = image["type"]
181 | output_images.append(output_data)
182 |
183 | return output_images
184 |
185 |
186 | def save_image(images, output_path, save_previews):
187 | # for itm in images:
188 | # directory = os.path.join(output_path, 'temp/') if itm['type'] == 'temp' and save_previews else output_path
189 | # os.makedirs(directory, exist_ok=True)
190 | # try:
191 | # image = Image.open(io.BytesIO(itm['image_data']))
192 | # image.save(os.path.join(directory, itm['file_name']))
193 | # except Exception as e:
194 | # print(f"Failed to save image {itm['file_name']}: {e}")
195 | # TODO: 这个逻辑不怎么好看 但应该不会造成隐患
196 | itm = images[-1]
197 | os.makedirs(output_path, exist_ok=True)
198 | try:
199 | image = Image.open(io.BytesIO(itm["image_data"]))
200 | image.save(os.path.join(output_path, itm["file_name"]))
201 | except Exception as e:
202 | print(f"Failed to save image {itm['file_name']}: {e}")
203 |
204 |
205 | if __name__ == "__main__":
206 | IMAGE_PATH = "/root/autodl-tmp/comfy_api/image_tmp/images (2).jpeg"
207 | OUTPUT_PATH = "./output/"
208 | output_filename = prompt_image_to_image(
209 | "nobel_slight_workflow_api.json",
210 | IMAGE_PATH,
211 | output_path=OUTPUT_PATH,
212 | save_previews=True,
213 | )
214 | print(output_filename)
215 |
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/src/slogan_agent.py:
--------------------------------------------------------------------------------
1 | from openai import OpenAI
2 | import re
3 | from pypinyin import pinyin, Style
4 |
5 |
6 | def call_llm(api_key: str, prompt: str, sys_prompt: str) -> dict:
7 | client = OpenAI(
8 | api_key=api_key,
9 | base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
10 | )
11 | completion = client.chat.completions.create(
12 | model="qwen-plus",
13 | messages=[
14 | {
15 | "role": "system",
16 | "content": sys_prompt,
17 | },
18 | {"role": "user", "content": prompt},
19 | ],
20 | temperature=0.8,
21 | )
22 | response = completion.choices[0].message.content
23 | return response
24 |
25 |
26 | prompt_dict = {
27 | "subject_prompt": {
28 | "sys_prompt": "Only one English word or phrase is returned",
29 | "prompt": "Use the above text to sort the person's description by topic and return just one English word or phrase:",
30 | },
31 | "contribution_prompt": {
32 | "sys_prompt": "你是评审人员，擅长评审他人的工作",
33 | "prompt": " 结合以上人物介绍，不要出现人名，不要主语，最后不要句号。用夸张且诙谐的一句中文不超过20个词不带主语的话总结他的主要贡献，可以非常主观: ",
34 | },
35 | }
36 |
37 | prompt_dict_en = {
38 | "subject_prompt": {
39 | "sys_prompt": "Only one English word or phrase is returned",
40 | "prompt": "Use the above text to sort the person's description by topic and return just one English word or phrase:",
41 | },
42 | "contribution_prompt": {
43 | "sys_prompt": "You're a reviewer, and you're good at reviewing other people's work. Only return English",
44 | "prompt": "Combined with the above character introduction, summarize his main contributions in a one-sentence short play without subject or punctuation. Exaggerate to emphasize the great contribution. Only return English And no more than 20 words: ",
45 | },
46 | }
47 |
48 |
49 | def is_chinese(char):
50 | """判断一个字符是否为中文"""
51 | return "\u4e00" <= char <= "\u9fff"
52 |
53 |
54 | def standardize_name(name):
55 | """将输入的名字转化为学术英语的标准格式"""
56 | if any(is_chinese(char) for char in name):
57 | # 使用pypinyin库将中文名转为拼音
58 | parts = pinyin(name, style=Style.NORMAL)
59 |
60 | parts = [part[0] for part in parts] # 不进行大写转换，直接连成字符串
61 | # 假设最后一个部分是姓，其他是名
62 | first_name = "".join(parts[1:]).capitalize() # 连接并首字母大写
63 | last_name = parts[0].capitalize() # 姓的首字母大写
64 | # 转换为首字母大写的格式，并调整顺序
65 | return f"{first_name} {last_name}"
66 | else:
67 | # 如果名字不含中文，直接返回
68 | return name
69 |
70 |
71 | def get_slogan(content, name, prompt_dict, api_key):
72 |
73 | name = standardize_name(name)
74 |
75 | subject_prompt = prompt_dict["subject_prompt"]
76 | sys_prompt = subject_prompt["sys_prompt"]
77 | prompt = content + subject_prompt["prompt"]
78 | subject = call_llm(api_key, prompt, sys_prompt)
79 | subject_content = f"THE NOBEL PRIZE IN {subject.upper()} 2024"
80 |
81 | contribution_prompt = prompt_dict["contribution_prompt"]
82 | sys_prompt = contribution_prompt["sys_prompt"]
83 | prompt = content + contribution_prompt["prompt"] + content
84 | contribution_content = "”" + call_llm(api_key, prompt, sys_prompt).strip() + "“"
85 |
86 | return subject_content, name, contribution_content
87 |
88 |
89 | if __name__ == "__main__":
90 | api_key = ""
91 | name = "somebody"
92 | content = "Do nothing"
93 | prompt_dict = prompt_dict
94 | subject_content, name, contribution_content = get_slogan(
95 | content, name, prompt_dict, api_key
96 | )
97 | print(subject_content)
98 | print(name)
99 | print(contribution_content)
100 |
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