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| import gradio as gr | |
| import os | |
| import sys | |
| import json | |
| import requests | |
| import glob | |
| from PIL import Image | |
| from io import BytesIO | |
| import random | |
| import numpy as np | |
| MODEL = "gpt-3.5-turbo" | |
| API_URL = os.getenv("API_URL") | |
| DISABLED = os.getenv("DISABLED") == 'True' | |
| OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") | |
| NUM_THREADS = int(16) | |
| TOTAL_K = 5 | |
| SAMPLE_IDX = [] | |
| RANDOM_POSITION = [(145 + 200 * i + 400 * (i//2), j * 110 + 900) for i in range(4) for j in range(4) ] | |
| CURRENT_POSITION = [] | |
| import os | |
| import numpy as np | |
| import torch | |
| ########### | |
| from thinkgpt.llm import ThinkGPT | |
| os.environ['OPENAI_API_KEY']='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' | |
| llm = ThinkGPT(model_name="gpt-3.5-turbo") | |
| import gradio as gr | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| title = """<h1 align="center"> 🏛️ Courtroom with AI juries</h1>""" | |
| description = "Building open-domain chatbots is a challenging area for machine learning research." | |
| examples = [["How are you?"]] | |
| os.environ['OPENAI_API_KEY']='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' | |
| tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large") | |
| model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large") | |
| ########### | |
| print (NUM_THREADS) | |
| def exception_handler(exception_type, exception, traceback): | |
| print("%s: %s" % (exception_type.__name__, exception)) | |
| sys.excepthook = exception_handler | |
| sys.tracebacklimit = 0 | |
| def show_image_from_link(image_link): | |
| response = requests.get(image_link) | |
| img = Image.open(BytesIO(response.content)) | |
| return img | |
| #https://github.com/gradio-app/gradio/issues/3531#issuecomment-1484029099 | |
| def parse_codeblock(text): | |
| lines = text.split("\n") | |
| for i, line in enumerate(lines): | |
| if "```" in line: | |
| if line != "```": | |
| lines[i] = f'<pre><code class="{lines[i][3:]}">' | |
| else: | |
| lines[i] = '</code></pre>' | |
| else: | |
| if i > 0: | |
| lines[i] = "<br/>" + line.replace("<", "<").replace(">", ">") | |
| return "".join(lines) | |
| # def predict(inputs, top_p, temperature, chat_counter, chatbot, history, request:gr.Request): | |
| # payload = { | |
| # "model": MODEL, | |
| # "messages": [{"role": "user", "content": f"{inputs}"}], | |
| # "temperature" : 1.0, | |
| # "top_p":1.0, | |
| # "n" : 1, | |
| # "stream": True, | |
| # "presence_penalty":0, | |
| # "frequency_penalty":0, | |
| # } | |
| # headers = { | |
| # "Content-Type": "application/json", | |
| # "Authorization": f"Bearer {OPENAI_API_KEY}", | |
| # "Headers": f"{request.kwargs['headers']}" | |
| # } | |
| # # print(f"chat_counter - {chat_counter}") | |
| # if chat_counter != 0 : | |
| # messages = [] | |
| # for i, data in enumerate(history): | |
| # if i % 2 == 0: | |
| # role = 'user' | |
| # else: | |
| # role = 'assistant' | |
| # message = {} | |
| # message["role"] = role | |
| # message["content"] = data | |
| # messages.append(message) | |
| # message = {} | |
| # message["role"] = "user" | |
| # message["content"] = inputs | |
| # messages.append(message) | |
| # payload = { | |
| # "model": MODEL, | |
| # "messages": messages, | |
| # "temperature" : temperature, | |
| # "top_p": top_p, | |
| # "n" : 1, | |
| # "stream": True, | |
| # "presence_penalty":0, | |
| # "frequency_penalty":0, | |
| # } | |
| # chat_counter += 1 | |
| # history.append(inputs) | |
| # token_counter = 0 | |
| # partial_words = "" | |
| # counter = 0 | |
| # try: | |
| # # make a POST request to the API endpoint using the requests.post method, passing in stream=True | |
| # response = requests.post(API_URL, headers=headers, json=payload, stream=True) | |
| # response_code = f"{response}" | |
| # #if response_code.strip() != "<Response [200]>": | |
| # # #print(f"response code - {response}") | |
| # # raise Exception(f"Sorry, hitting rate limit. Please try again later. {response}") | |
| # for chunk in response.iter_lines(): | |
| # #Skipping first chunk | |
| # if counter == 0: | |
| # counter += 1 | |
| # continue | |
| # #counter+=1 | |
| # # check whether each line is non-empty | |
| # if chunk.decode() : | |
| # chunk = chunk.decode() | |
| # # decode each line as response data is in bytes | |
| # if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']: | |
| # partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"] | |
| # if token_counter == 0: | |
| # history.append(" " + partial_words) | |
| # else: | |
| # history[-1] = partial_words | |
| # token_counter += 1 | |
| # yield [(parse_codeblock(history[i]), parse_codeblock(history[i + 1])) for i in range(0, len(history) - 1, 2) ], history, chat_counter, response, gr.update(interactive=False), gr.update(interactive=False) # resembles {chatbot: chat, state: history} | |
| # except Exception as e: | |
| # print (f'error found: {e}') | |
| # yield [(parse_codeblock(history[i]), parse_codeblock(history[i + 1])) for i in range(0, len(history) - 1, 2) ], history, chat_counter, response, gr.update(interactive=True), gr.update(interactive=True) | |
| # print(json.dumps({"chat_counter": chat_counter, "payload": payload, "partial_words": partial_words, "token_counter": token_counter, "counter": counter})) | |
| def predict(input, history=[]): | |
| # tokenize the new input sentence | |
| new_user_input_ids = tokenizer.encode( | |
| input + tokenizer.eos_token, return_tensors="pt" | |
| ) | |
| llm.memorize(memorize_list) | |
| prediction = llm.predict(question, remember=llm.remember('철수', limit = 30)) | |
| print(prediction) | |
| print('-'*100) | |
| # append the new user input tokens to the chat history | |
| bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1) | |
| # generate a response | |
| history = model.generate( | |
| bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id | |
| ).tolist() | |
| # convert the tokens to text, and then split the responses into lines | |
| response = tokenizer.decode(history[0]).split("<|endoftext|>") | |
| # print('decoded_response-->>'+str(response)) | |
| response = [ | |
| (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2) | |
| ] # convert to tuples of list | |
| # print('response-->>'+str(response)) | |
| return response, prediction | |
| def reset_textbox(): | |
| return gr.update(value='', interactive=False), gr.update(interactive=False) | |
| def random_sample(): | |
| SAMPLE_IDX = random.choices(range(25), k=TOTAL_K) | |
| image = np.array(Image.open(os.path.join(os.path.dirname(__file__), "data/Court.jpeg"))) | |
| random_characters = [Image.open(os.path.join(os.path.dirname(__file__), f'data/characters_{idx:02}.png')).resize((64, 64)) for idx in SAMPLE_IDX] | |
| current_position = random.choices(RANDOM_POSITION, k = TOTAL_K) | |
| new_image = Image.fromarray(image).convert('RGBA') | |
| for k in range(TOTAL_K): | |
| new_image.paste(random_characters[k], current_position[k], random_characters[k]) | |
| return np.array(new_image), current_position | |
| def reset_sample(): | |
| image = np.array(Image.open(os.path.join(os.path.dirname(__file__), "data/Court.jpeg"))) | |
| return image | |
| title = """<h1 align="center">Courtroom with AI juries</h1>""" | |
| if DISABLED: | |
| title = """<h1 align="center" style="color:red">This app has reached OpenAI's usage limit. We are currently requesting an increase in our quota. Please check back in a few days.</h1>""" | |
| description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form: | |
| ``` | |
| User: <utterance> | |
| Assistant: <utterance> | |
| User: <utterance> | |
| Assistant: <utterance> | |
| ... | |
| ``` | |
| In this app, you can explore the outputs of a gpt-3.5 LLM. | |
| """ | |
| def sumbit(image): | |
| pass | |
| theme = gr.themes.Default(primary_hue="green") | |
| agents = [] | |
| with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;} | |
| #chatbot {height: 520px; overflow: auto;}""", | |
| theme=theme) as demo: | |
| gr.HTML(title) | |
| #gr.HTML('''<center><a href="https://huggingface.co/spaces/yuntian-deng/ChatGPT?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''') | |
| with gr.Column(elem_id = "col_container", visible=False) as main_block: | |
| with gr.Row(): | |
| with gr.Column(scale=7): | |
| image = gr.Image(os.path.join(os.path.dirname(__file__), "data/Court.jpeg"), height = 800, width = 1600) | |
| with gr.Column(scale=3): | |
| for idx in range(TOTAL_K): | |
| text = gr.Textbox(label=f"Random Sample - {idx+1}") | |
| agents.append(text) | |
| # chatbot = gr.Chatbot(elem_id='chatbot') #c | |
| with gr.Row(): | |
| with gr.Column(scale=7): | |
| inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter") #t | |
| with gr.Column(scale=3): | |
| b1 = gr.Button('Submit', visible=not DISABLED) | |
| with gr.Row(): | |
| with gr.Column(scale=7): | |
| b0 = gr.Button('Random Sample', visible=not DISABLED) | |
| with gr.Column(scale=3): | |
| b2 = gr.Button('Reset', visible=not DISABLED) | |
| #inputs, top_p, temperature, top_k, repetition_penalty | |
| with gr.Accordion("Parameters", open=False): | |
| top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",) | |
| temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",) | |
| #top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",) | |
| #repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", ) | |
| chat_counter = gr.Number(value=0, visible=False, precision=0) | |
| with gr.Column(elem_id = "user_consent_container") as user_consent_block: | |
| # Get user consent | |
| accept_checkbox = gr.Checkbox(visible=False) | |
| js = "(x) => confirm('By clicking \"OK\", I agree that my data may be published or shared.')" | |
| with gr.Accordion("User Consent for Data Collection, Use, and Sharing", open=True): | |
| gr.HTML(""" | |
| <div> | |
| <p>By using our app, which is powered by OpenAI's API, you acknowledge and agree to the following terms regarding the data you provide:</p> | |
| <ol> | |
| <li><strong>Collection:</strong> We may collect information, including the inputs you type into our app, the outputs generated by OpenAI's API, and certain technical details about your device and connection (such as browser type, operating system, and IP address) provided by your device's request headers.</li> | |
| <li><strong>Use:</strong> We may use the collected data for research purposes, to improve our services, and to develop new products or services, including commercial applications, and for security purposes, such as protecting against unauthorized access and attacks.</li> | |
| <li><strong>Sharing and Publication:</strong> Your data, including the technical details collected from your device's request headers, may be published, shared with third parties, or used for analysis and reporting purposes.</li> | |
| <li><strong>Data Retention:</strong> We may retain your data, including the technical details collected from your device's request headers, for as long as necessary.</li> | |
| </ol> | |
| <p>By continuing to use our app, you provide your explicit consent to the collection, use, and potential sharing of your data as described above. If you do not agree with our data collection, use, and sharing practices, please do not use our app.</p> | |
| </div> | |
| """) | |
| accept_button = gr.Button("I Agree") | |
| def enable_inputs(): | |
| return user_consent_block.update(visible=False), main_block.update(visible=True) | |
| accept_button.click(None, None, accept_checkbox, _js=js, queue=False) | |
| accept_checkbox.change(fn=enable_inputs, inputs=[], outputs=[user_consent_block, main_block], queue=False) | |
| b0.click(random_sample, inputs = [], outputs = [image, CURRENT_POSITION]) | |
| b2.click(reset_sample, inputs = [], outputs = [image]) | |
| # inputs.submit(reset_textbox, [], [inputs, b1], queue=False) | |
| # inputs.submit(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code, inputs, b1],) #openai_api_key | |
| # b1.click(reset_textbox, [], [inputs, b1], queue=False) | |
| # b1.click(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code, inputs, b1],) #openai_api_key | |
| demo.queue(max_size=20, concurrency_count=NUM_THREADS, api_open=False).launch(share=False) |