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| import json | |
| import os | |
| import logging | |
| import sys | |
| import torch | |
| import gradio as gr | |
| from huggingface_hub import Repository | |
| from text_generation import Client | |
| from app_modules.utils import convert_to_markdown | |
| # from dialogues import DialogueTemplate | |
| from share_btn import (community_icon_html, loading_icon_html, share_btn_css, | |
| share_js) | |
| HF_TOKEN = os.environ.get("HF_TOKEN", None) | |
| API_TOKEN = os.environ.get("API_TOKEN", None) | |
| # API_TOKEN = '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' | |
| API_URL = os.environ.get("API_URL", None) | |
| API_URL = "https://api-inference.huggingface.co/models/timdettmers/guanaco-33b-merged" | |
| client = Client( | |
| API_URL, | |
| headers={"Authorization": f"Bearer {API_TOKEN}"}, | |
| ) | |
| repo = None | |
| logging.basicConfig( | |
| format="%(asctime)s [%(levelname)s] [%(name)s] %(message)s", | |
| datefmt="%Y-%m-%dT%H:%M:%SZ", | |
| ) | |
| logger = logging.getLogger(__name__) | |
| logger.setLevel(logging.DEBUG) | |
| examples = [ | |
| "Describe the advantages and disadvantages of Incremental Sheet Forming.", | |
| "Describe the applications of Incremental Sheet Forming.", | |
| "Describe the process parameters included in Incremental Sheet Forming in dot points." | |
| ] | |
| def get_total_inputs(inputs, chatbot, preprompt, user_name, assistant_name, sep): | |
| past = [] | |
| for data in chatbot: | |
| user_data, model_data = data | |
| if not user_data.startswith(user_name): | |
| user_data = user_name + user_data | |
| if not model_data.startswith(sep + assistant_name): | |
| model_data = sep + assistant_name + model_data | |
| past.append(user_data + model_data.rstrip() + sep) | |
| if not inputs.startswith(user_name): | |
| inputs = user_name + inputs | |
| total_inputs = preprompt + "".join(past) + inputs + sep + assistant_name.rstrip() | |
| return total_inputs | |
| def has_no_history(chatbot, history): | |
| return not chatbot and not history | |
| header = "A chat between a curious human and an artificial intelligence assistant about Incremental Sheet Forming (ISF). " \ | |
| "The assistant gives helpful, detailed, and polite answers to the user's questions." | |
| prompt_template = "### Human: {query}\n### Assistant:{response}" | |
| def generate( | |
| user_message, | |
| chatbot, | |
| history, | |
| temperature, | |
| top_p, | |
| top_k, | |
| max_new_tokens, | |
| repetition_penalty, | |
| ): | |
| # Don't return meaningless message when the input is empty | |
| if not user_message: | |
| print("Empty input") | |
| history.append(user_message) | |
| past_messages = [] | |
| for data in chatbot: | |
| user_data, model_data = data | |
| past_messages.extend( | |
| [{"role": "user", "content": user_data}, {"role": "assistant", "content": model_data.rstrip()}] | |
| ) | |
| if len(past_messages) < 1: | |
| prompt = header + prompt_template.format(query=user_message, response="") | |
| else: | |
| prompt = header | |
| for i in range(0, len(past_messages), 2): | |
| intermediate_prompt = prompt_template.format(query=past_messages[i]["content"], | |
| response=past_messages[i + 1]["content"]) | |
| print("intermediate: ", intermediate_prompt) | |
| prompt = prompt + '\n' + intermediate_prompt | |
| prompt = prompt + prompt_template.format(query=user_message, response="") | |
| temperature = float(temperature) | |
| if temperature < 1e-2: | |
| temperature = 1e-2 | |
| top_p = float(top_p) | |
| generate_kwargs = dict( | |
| temperature=temperature, | |
| max_new_tokens=max_new_tokens, | |
| top_p=top_p, | |
| top_k=top_k, | |
| repetition_penalty=repetition_penalty, | |
| do_sample=True, | |
| truncate=999, | |
| seed=42, | |
| ) | |
| stream = client.generate_stream( | |
| prompt, | |
| **generate_kwargs, | |
| ) | |
| output = "" | |
| for idx, response in enumerate(stream): | |
| if response.token.text == '': | |
| break | |
| if response.token.special: | |
| continue | |
| output += response.token.text | |
| if idx == 0: | |
| history.append(" " + output) | |
| else: | |
| history[-1] = output | |
| chat = [(convert_to_markdown(history[i].strip()), convert_to_markdown(history[i + 1].strip())) for i in range(0, len(history) - 1, 2)] | |
| yield chat, history, user_message, "" | |
| return chat, history, user_message, "" | |
| def clear_chat(): | |
| return [], [] | |
| def save( | |
| history, | |
| temperature=0.7, | |
| top_p=0.9, | |
| top_k=50, | |
| max_new_tokens=512, | |
| repetition_penalty=1.2, | |
| max_memory=1024, | |
| ): | |
| history = [] if history is None else history | |
| data_point = {'history': history, 'generation_parameter': { | |
| "temperature": temperature, | |
| "top_p": top_p, | |
| "top_k": top_k, | |
| "max_new_tokens": max_new_tokens, | |
| "repetition_penalty": repetition_penalty, | |
| "max_memory": max_memory, | |
| }} | |
| print(data_point) | |
| file_name = "history.jsonl" | |
| with open(file_name, 'a') as f: | |
| for line in [data_point]: | |
| f.write(json.dumps(line, ensure_ascii=False) + '\n') | |
| def process_example(args): | |
| for [x, y] in generate(args): | |
| pass | |
| return [x, y] | |
| title = """<h1 align="center">ISF Alpaca 💬</h1>""" | |
| custom_css = """ | |
| #banner-image { | |
| display: block; | |
| margin-left: auto; | |
| margin-right: auto; | |
| } | |
| #chat-message { | |
| font-size: 14px; | |
| min-height: 300px; | |
| } | |
| """ | |
| with gr.Blocks(analytics_enabled=False, | |
| theme=gr.themes.Soft(), | |
| css=".disclaimer {font-variant-caps: all-small-caps;}") as demo: | |
| gr.HTML(title) | |
| # status_display = gr.Markdown("Success", elem_id="status_display") | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown( | |
| """ | |
| 🏭 The fine-tuned model primarily emphasizes **Knowledge Augmentation** in the Manufacturing domain, | |
| with **Incremental Sheet Forming (ISF)** serving as a use case. | |
| """ | |
| ) | |
| history = gr.components.State() | |
| with gr.Row(scale=1).style(equal_height=True): | |
| with gr.Column(scale=5): | |
| with gr.Row(scale=1): | |
| chatbot = gr.Chatbot(elem_id="chuanhu_chatbot").style(height=476) | |
| with gr.Row(scale=1): | |
| with gr.Column(scale=12): | |
| user_message = gr.Textbox( | |
| show_label=False, placeholder="Enter text" | |
| ).style(container=False) | |
| with gr.Column(min_width=70, scale=1): | |
| submit_btn = gr.Button("Send") | |
| with gr.Column(min_width=70, scale=1): | |
| stop_btn = gr.Button("Stop") | |
| with gr.Row(): | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[user_message], | |
| cache_examples=False, | |
| outputs=[chatbot, history], | |
| ) | |
| with gr.Row(scale=1): | |
| clear_history = gr.Button( | |
| "🧹 New Conversation", | |
| ) | |
| reset_btn = gr.Button("🔄 Reset Parameter") | |
| save_btn = gr.Button("📥 Save Chat") | |
| with gr.Column(): | |
| input_component_column = gr.Column(min_width=50, scale=1) | |
| with input_component_column: | |
| with gr.Tab(label="Parameter Setting"): | |
| gr.Markdown("# Parameters") | |
| temperature = gr.components.Slider(minimum=0, maximum=1, value=0.7, label="Temperature") | |
| top_p = gr.components.Slider(minimum=0, maximum=1, value=0.9, label="Top p") | |
| top_k = gr.components.Slider(minimum=0, maximum=100, step=1, value=30, label="Top k") | |
| max_new_tokens = gr.components.Slider(minimum=1, maximum=2048, step=1, value=512, | |
| label="Max New Tokens") | |
| repetition_penalty = gr.components.Slider(minimum=0.1, maximum=10.0, step=0.1, value=1.2, | |
| label="Repetition Penalty") | |
| max_memory = gr.components.Slider(minimum=0, maximum=2048, step=1, value=2048, label="Max Memory") | |
| history = gr.State([]) | |
| last_user_message = gr.State("") | |
| user_message.submit( | |
| generate, | |
| inputs=[ | |
| user_message, | |
| chatbot, | |
| history, | |
| temperature, | |
| top_p, | |
| top_k, | |
| max_new_tokens, | |
| repetition_penalty, | |
| ], | |
| outputs=[chatbot, history, last_user_message, user_message], | |
| ) | |
| submit_event = submit_btn.click( | |
| generate, | |
| inputs=[ | |
| user_message, | |
| chatbot, | |
| history, | |
| temperature, | |
| top_p, | |
| top_k, | |
| max_new_tokens, | |
| repetition_penalty, | |
| ], | |
| outputs=[chatbot, history, last_user_message, user_message], | |
| ) | |
| # submit_btn.click( | |
| # lambda: ( | |
| # submit_btn.update(visible=False), | |
| # stop_btn.update(visible=True), | |
| # ), | |
| # inputs=None, | |
| # outputs=[submit_btn, stop_btn], | |
| # queue=False, | |
| # ) | |
| stop_btn.click( | |
| lambda: ( | |
| submit_btn.update(visible=True), | |
| stop_btn.update(visible=True), | |
| ), | |
| inputs=None, | |
| outputs=[submit_btn, stop_btn], | |
| cancels=[submit_event], | |
| queue=False, | |
| ) | |
| clear_history.click(clear_chat, outputs=[chatbot, history]) | |
| save_btn.click( | |
| save, | |
| inputs=[user_message, chatbot, history, temperature, top_p, top_k, max_new_tokens, repetition_penalty], | |
| outputs=None, | |
| ) | |
| input_components_except_states = [user_message, chatbot, history, temperature, top_p, top_k, max_new_tokens, | |
| repetition_penalty] | |
| reset_btn.click( | |
| None, | |
| [], | |
| (input_components_except_states + [input_component_column]), # type: ignore | |
| _js=f"""() => {json.dumps([getattr(component, "cleared_value", None) for component in input_components_except_states] | |
| + ([gr.Column.update(visible=True)]) | |
| + ([]) | |
| )} | |
| """, | |
| ) | |
| demo.queue(concurrency_count=16).launch(debug=True) | |