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| # -*- coding:utf-8 -*- | |
| import os | |
| import logging | |
| import sys | |
| import gradio as gr | |
| import torch | |
| from app_modules.utils import * | |
| from app_modules.presets import * | |
| from app_modules.overwrites import * | |
| logging.basicConfig( | |
| level=logging.DEBUG, | |
| format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s", | |
| ) | |
| base_model = "decapoda-research/llama-7b-hf" | |
| adapter_model = "project-baize/baize-lora-7B" | |
| tokenizer,model,device = load_tokenizer_and_model(base_model,adapter_model) | |
| total_count = 0 | |
| def predict(text, | |
| chatbot, | |
| history, | |
| top_p, | |
| temperature, | |
| max_length_tokens, | |
| max_context_length_tokens,): | |
| if text=="": | |
| yield chatbot,history,"Empty Context" | |
| return | |
| try: | |
| model | |
| except: | |
| yield [[text,"No Model Found"]],[],"No Model Found" | |
| return | |
| inputs = generate_prompt_with_history(text,history,tokenizer,max_length=max_context_length_tokens) | |
| if inputs is False: | |
| yield chatbot+[[text,"Sorry, the input is too long."]],history,"Generate Fail" | |
| return | |
| else: | |
| prompt,inputs=inputs | |
| begin_length = len(prompt) | |
| torch.cuda.empty_cache() | |
| input_ids = inputs["input_ids"].to(device) | |
| global total_count | |
| total_count += 1 | |
| print(total_count) | |
| with torch.no_grad(): | |
| for x in greedy_search(input_ids,model,tokenizer,stop_words=["[|Human|]", "[|AI|]"],max_length=max_length_tokens,temperature=temperature,top_p=top_p): | |
| if is_stop_word_or_prefix(x,["[|Human|]", "[|AI|]"]) is False: | |
| if "[|Human|]" in x: | |
| x = x[:x.index("[|Human|]")].strip() | |
| if "[|AI|]" in x: | |
| x = x[:x.index("[|AI|]")].strip() | |
| x = x.strip(" ") | |
| a, b= [[y[0],convert_to_markdown(y[1])] for y in history]+[[text, convert_to_markdown(x)]],history + [[text,x]] | |
| yield a, b, "Generating……" | |
| if shared_state.interrupted: | |
| shared_state.recover() | |
| try: | |
| yield a, b, "Stop Success" | |
| return | |
| except: | |
| pass | |
| #print(text) | |
| #print(x) | |
| #print("="*80) | |
| try: | |
| yield a,b,"Generate Success" | |
| except: | |
| pass | |
| def retry( | |
| text, | |
| chatbot, | |
| history, | |
| top_p, | |
| temperature, | |
| max_length_tokens, | |
| max_context_length_tokens, | |
| ): | |
| logging.info("Retry……") | |
| if len(history) == 0: | |
| yield chatbot, history, f"Empty context" | |
| return | |
| chatbot.pop() | |
| inputs = history.pop()[0] | |
| for x in predict(inputs,chatbot,history,top_p,temperature,max_length_tokens,max_context_length_tokens): | |
| yield x | |
| gr.Chatbot.postprocess = postprocess | |
| with open("assets/custom.css", "r", encoding="utf-8") as f: | |
| customCSS = f.read() | |
| with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo: | |
| history = gr.State([]) | |
| user_question = gr.State("") | |
| with gr.Row(): | |
| gr.HTML(title) | |
| status_display = gr.Markdown("Success", elem_id="status_display") | |
| gr.Markdown(description_top) | |
| 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="100%") | |
| with gr.Row(scale=1): | |
| with gr.Column(scale=12): | |
| user_input = gr.Textbox( | |
| show_label=False, placeholder="Enter text" | |
| ).style(container=False) | |
| with gr.Column(min_width=70, scale=1): | |
| submitBtn = gr.Button("Send") | |
| with gr.Row(scale=1): | |
| emptyBtn = gr.Button( | |
| "🧹 New Conversation", | |
| ) | |
| retryBtn = gr.Button("🔄 Regenerate") | |
| delLastBtn = gr.Button("🗑️ Remove Last Turn") | |
| with gr.Column(): | |
| with gr.Column(min_width=50, scale=1): | |
| with gr.Tab(label="Parameter Setting"): | |
| gr.Markdown("# Parameters") | |
| top_p = gr.Slider( | |
| minimum=-0, | |
| maximum=1.0, | |
| value=0.95, | |
| step=0.05, | |
| interactive=True, | |
| label="Top-p", | |
| ) | |
| temperature = gr.Slider( | |
| minimum=0.1, | |
| maximum=2.0, | |
| value=1, | |
| step=0.1, | |
| interactive=True, | |
| label="Temperature", | |
| ) | |
| max_length_tokens = gr.Slider( | |
| minimum=0, | |
| maximum=512, | |
| value=256, | |
| step=8, | |
| interactive=True, | |
| label="Max Generation Tokens", | |
| ) | |
| max_context_length_tokens = gr.Slider( | |
| minimum=0, | |
| maximum=4096, | |
| value=2048, | |
| step=128, | |
| interactive=True, | |
| label="Max History Tokens", | |
| ) | |
| gr.Markdown(description) | |
| predict_args = dict( | |
| fn=predict, | |
| inputs=[ | |
| user_question, | |
| chatbot, | |
| history, | |
| top_p, | |
| temperature, | |
| max_length_tokens, | |
| max_context_length_tokens, | |
| ], | |
| outputs=[chatbot, history, status_display], | |
| show_progress=True, | |
| ) | |
| retry_args = dict( | |
| fn=retry, | |
| inputs=[ | |
| user_input, | |
| chatbot, | |
| history, | |
| top_p, | |
| temperature, | |
| max_length_tokens, | |
| max_context_length_tokens, | |
| ], | |
| outputs=[chatbot, history, status_display], | |
| show_progress=True, | |
| ) | |
| reset_args = dict( | |
| fn=reset_textbox, inputs=[], outputs=[user_input, status_display] | |
| ) | |
| # Chatbot | |
| transfer_input_args = dict( | |
| fn=transfer_input, inputs=[user_input], outputs=[user_question, user_input, submitBtn], show_progress=True | |
| ) | |
| predict_event1 = user_input.submit(**transfer_input_args).then(**predict_args) | |
| predict_event2 = submitBtn.click(**transfer_input_args).then(**predict_args) | |
| emptyBtn.click( | |
| reset_state, | |
| outputs=[chatbot, history, status_display], | |
| show_progress=True, | |
| ) | |
| emptyBtn.click(**reset_args) | |
| predict_event3 = retryBtn.click(**retry_args) | |
| delLastBtn.click( | |
| delete_last_conversation, | |
| [chatbot, history], | |
| [chatbot, history, status_display], | |
| show_progress=True, | |
| ) | |
| #cancelBtn.click( | |
| # cancel_outputing, [], [status_display], | |
| # cancels=[ | |
| # predict_event1,predict_event2,predict_event3 | |
| # ] | |
| #) | |
| demo.title = "Baize" | |
| demo.queue(concurrency_count=1).launch() |