Spaces:
Sleeping
Sleeping
| from Prompter import Prompter | |
| from Callback import Stream, Iteratorize | |
| import os | |
| import sys | |
| import gradio as gr | |
| import torch | |
| import transformers | |
| from peft import PeftModel | |
| from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer | |
| import pandas as pd | |
| import numpy as np | |
| if torch.cuda.is_available(): | |
| device = "cuda" | |
| else: | |
| device = "cpu" | |
| try: | |
| if torch.backends.mps.is_available(): | |
| device = "mps" | |
| except: # noqa: E722 | |
| pass | |
| base_model = "openthaigpt/openthaigpt-1.0.0-beta-7b-chat-ckpt-hf" | |
| load_8bit = True | |
| # lora_weights = "PLatonG/openthaigpt-1.0.0-beta-7b-expert-recommendations" | |
| lora_weights = "PLatonG/openthaigpt-1.0.0-beta-7b-expert-recommendation-2.0" | |
| prompter = Prompter("alpaca") | |
| tokenizer = LlamaTokenizer.from_pretrained(base_model) | |
| model = LlamaForCausalLM.from_pretrained( | |
| base_model, | |
| load_in_8bit=load_8bit, | |
| torch_dtype=torch.float16, | |
| device_map="auto", | |
| offload_folder = "./offload" | |
| ) | |
| model = PeftModel.from_pretrained( | |
| model, | |
| lora_weights, | |
| torch_dtype=torch.float16, | |
| offload_folder = "./offload" | |
| ) | |
| # unwind broken decapoda-research config | |
| model.config.pad_token_id = tokenizer.pad_token_id = 0 # unk | |
| model.config.bos_token_id = 1 | |
| model.config.eos_token_id = 2 | |
| if not load_8bit: | |
| model.half() # seems to fix bugs for some users. | |
| model.eval() | |
| if torch.__version__ >= "2" and sys.platform != "win32": | |
| model = torch.compile(model) | |
| def evaluate( | |
| instruction, | |
| input=None, | |
| stream_output=False, | |
| ): | |
| temperature=0.1 | |
| top_p=0.9 | |
| top_k=10 | |
| num_beams=1 | |
| max_new_tokens=380 | |
| prompt = prompter.generate_prompt(instruction, input) | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| input_ids = inputs["input_ids"].to(device) | |
| generation_config = GenerationConfig( | |
| temperature=temperature, | |
| top_p=top_p, | |
| top_k=top_k, | |
| num_beams=num_beams, | |
| do_sample = True, | |
| ) | |
| # generation_config = GenerationConfig( | |
| # do_sample = True, | |
| # num_beams = 4, | |
| # ) | |
| generate_params = { | |
| "input_ids": input_ids, | |
| "generation_config": generation_config, | |
| "return_dict_in_generate": True, | |
| "output_scores": True, | |
| "max_new_tokens": max_new_tokens, | |
| } | |
| if stream_output: | |
| # Stream the reply 1 token at a time. | |
| # This is based on the trick of using 'stopping_criteria' to create an iterator, | |
| # from https://github.com/oobabooga/text-generation-webui/blob/ad37f396fc8bcbab90e11ecf17c56c97bfbd4a9c/modules/text_generation.py#L216-L243. | |
| def generate_with_callback(callback=None, **kwargs): | |
| kwargs.setdefault( | |
| "stopping_criteria", transformers.StoppingCriteriaList() | |
| ) | |
| kwargs["stopping_criteria"].append( | |
| Stream(callback_func=callback) | |
| ) | |
| with torch.no_grad(): | |
| model.generate(**kwargs) | |
| def generate_with_streaming(**kwargs): | |
| return Iteratorize( | |
| generate_with_callback, kwargs, callback=None | |
| ) | |
| with generate_with_streaming(**generate_params) as generator: | |
| for output in generator: | |
| # new_tokens = len(output) - len(input_ids[0]) | |
| decoded_output = tokenizer.decode(output) | |
| if output[-1] in [tokenizer.eos_token_id]: | |
| break | |
| yield prompter.get_response(decoded_output) | |
| return # early return for stream_output | |
| # Without streaming | |
| torch.manual_seed(42) | |
| with torch.no_grad(): | |
| generation_output = model.generate( | |
| input_ids=input_ids, | |
| generation_config=generation_config, | |
| return_dict_in_generate=True, | |
| output_scores=True, | |
| max_new_tokens=max_new_tokens, | |
| # seed = [0, 42], | |
| ) | |
| s = generation_output.sequences[0] | |
| output = tokenizer.decode(s) | |
| yield prompter.get_response(output) | |
| # From SMOTE with 4 neightbor | |
| fourNSMOTE = pd.read_csv("FILTER_GREATERTHANTHREE_FROM_SHEETS_SMOTE_train.csv") | |
| with gr.Blocks(fill_height = True, title="Expert Recommendations") as demo: | |
| gr.Markdown( | |
| """ | |
| # Expert Recommendations | |
| วิธีการใช้งาน | |
| * เลือกค่าที่ต้องการในแต่ละตัวเลือก | |
| * กดปุ่ม 'GENERATE INPUT' จากนั้นจะแสดงผลลัพธ์ในช่อง 'full prompt' | |
| * กดปุ่ม 'GENERATE OUTPUT' จากนั้นรอประมาณ 20 ถึง 60 วินาที ผลลัพธ์จะแสดงในช่อง 'ผลลัพธ์ (output)' | |
| """) | |
| with gr.Row(): | |
| birth_year = gr.components.Number(minimum = 2536, maximum = 2557, value= 2545, | |
| label="ปีเกิด", | |
| info="ต่ำสุด : 2536 สูงสุด : 2557") | |
| nationality_name = gr.components.Dropdown(choices=fourNSMOTE.NATIONALITY_NAME.unique().tolist(), | |
| label="สัญชาติ", | |
| value = fourNSMOTE.NATIONALITY_NAME.unique().tolist()[0]) | |
| religion_name = gr.components.Dropdown(choices=fourNSMOTE.RELIGION_NAME.unique().tolist(), | |
| label="ศาสนา", | |
| value = fourNSMOTE.RELIGION_NAME.unique().tolist()[0]) | |
| with gr.Row(): | |
| sex = gr.components.Dropdown(choices=fourNSMOTE.JVN_SEX.unique().tolist(), | |
| label="เพศ", | |
| value = fourNSMOTE.JVN_SEX.unique().tolist()[0]) | |
| inform_status = gr.components.Dropdown(choices=fourNSMOTE.INFORM_STATUS_TXT.unique().tolist(), | |
| label="เหตุที่นำมาสู่การดำเนินคดี", | |
| value = fourNSMOTE.INFORM_STATUS_TXT.unique().tolist()[0]) | |
| age = gr.components.Number(minimum = 10, maximum = 19, value= 17, | |
| label="อายุตอนกระทำผิด", | |
| info="ต่ำสุด : 10 ปี สูงสุด : 19") | |
| with gr.Row(): | |
| offense_name = gr.components.Dropdown(choices=fourNSMOTE.OFFENSE_NAME.unique().tolist(), | |
| label="คดีที่กระทำผิด", | |
| value = fourNSMOTE.OFFENSE_NAME.unique().tolist()[0]) | |
| ref_value = fourNSMOTE.OFFENSE_NAME.unique().tolist()[0] | |
| allegation_name = gr.components.Dropdown(choices=fourNSMOTE.ALLEGATION_NAME.unique().tolist(), label="ชื่อของข้อกล่าวหา", | |
| value = fourNSMOTE.query("OFFENSE_NAME == @ref_value")["ALLEGATION_NAME"].unique().tolist()[0]) | |
| allegation_desc = gr.components.Dropdown(choices=fourNSMOTE.ALLEGATION_DESC.unique().tolist(), label="รายละเอียดของข้อกล่าวหา", | |
| value = fourNSMOTE.query("OFFENSE_NAME == @ref_value")["ALLEGATION_DESC"].unique().tolist()[0]) | |
| def update_dropDown_allegation(value): | |
| allegation_query = fourNSMOTE.query("OFFENSE_NAME == @value") | |
| data = allegation_query["ALLEGATION_NAME"].unique().tolist() | |
| allegation_name = gr.components.Dropdown(choices=data, value=data[0]) | |
| # allegation_desc = gr.components.Dropdown(choices=query_state["ALLEGATION_DESC"].unique().tolist()) | |
| return allegation_name | |
| def update_dropDown_allegation_desc(offense_name, allegation_name): | |
| allegationDesc_query = fourNSMOTE.query("OFFENSE_NAME == @offense_name and ALLEGATION_NAME == @allegation_name") | |
| data = allegationDesc_query["ALLEGATION_DESC"].unique().tolist() | |
| allegation_desc = gr.components.Dropdown(choices=data, value=data[0]) | |
| # allegation_desc = gr.components.Dropdown(choices=query_state["ALLEGATION_DESC"].unique().tolist()) | |
| return allegation_desc | |
| offense_name.change(fn=update_dropDown_allegation, inputs=offense_name, outputs=[allegation_name]) | |
| offense_name.change(fn=update_dropDown_allegation_desc, inputs=[offense_name, allegation_name], outputs=[allegation_desc]) | |
| allegation_name.change(fn=update_dropDown_allegation_desc, inputs=[offense_name, allegation_name], outputs=[allegation_desc]) | |
| with gr.Row(): | |
| rn1 = gr.components.Radio(choices=["ถูก", "ผิด"], | |
| label="ปรากฎลักษณะนิสัย/พฤติกรรมที่ไม่เหมาะสมของบุคคลในครอบครัวและบุคคลที่เด็ก/เยาวชนอาศัยอยู่", | |
| value="ถูก") | |
| rn2 = gr.components.Radio(choices=["ถูก", "ผิด"], | |
| label="ปรากฎประวัติการกระทำผิดของบุคคลในครอบครัวและบุคคลที่เด็ก/เยาวชนอาศัยอยู่ด้วย", | |
| value = "ถูก") | |
| rn3 = gr.components.Radio(choices=["ถูก", "ผิด"], | |
| label="ปรากฎประวัติการเกี่ยวข้องกับยาเสพติดของบุคคลในครอบครัว", | |
| value = "ถูก") | |
| with gr.Row(): | |
| education = gr.components.Dropdown(choices=fourNSMOTE.RN3_14_HIS_EDU_FLAG.unique().tolist(), | |
| label="สถาณะการศึกษา", | |
| value = fourNSMOTE.RN3_14_HIS_EDU_FLAG.unique().tolist()[0]) | |
| occupation = gr.components.Dropdown(choices=fourNSMOTE.RN3_19_OCCUPATION_STATUS.unique().tolist(), | |
| label="สถาณะการประกอบอาชีพ", | |
| value = fourNSMOTE.RN3_19_OCCUPATION_STATUS.unique().tolist()[0]) | |
| province = gr.components.Dropdown(choices=fourNSMOTE.PROVINCE_NAME.unique().tolist(), | |
| label="จังหวัดที่กระทำผิด", | |
| value = fourNSMOTE.PROVINCE_NAME.unique().tolist()[0]) | |
| def generate_input(birth_year, nationality_name, religion_name, sex, | |
| inform_status, age, offense_name, allegation_name, | |
| allegation_desc, rn1, rn2, rn3, education, occupation, province): | |
| birth_year = f"เกิดเมื่อปี พ.ศ. {int(birth_year)}" | |
| if int(age) >= 10 and int(age) <=15: | |
| age = f"มีอายุอยู่ในช่วง 10 ถึง 15 ปี" | |
| elif int(age) >=16 and int(age) <= 20: | |
| age = f"มีอายุอยู่ในช่วง 16 ถึง 20 ปี" | |
| elif int(age) >=21 and int(age) <= 25: | |
| age = f"มีอายุอยู่ในช่วง 21 ถึง 25 ปี" | |
| elif int(age) >=26: | |
| age = f"มีอายุอยู่ในช่วง 26 ปีขึ้นไป" | |
| if rn1 == "ถูก": | |
| rn1 = "มีลักษณะนิสัย/พฤติกรรมที่ไม่เหมาะสมของบุคคลในครอบครัวและบุคคลที่เด็ก/เยาวชนอาศัยอยู่" | |
| else: | |
| rn1 = "ไม่มีลักษณะนิสัย/พฤติกรรมที่ไม่เหมาะสมของบุคคลในครอบครัวและบุคคลที่เด็ก/เยาวชนอาศัยอยู่" | |
| if rn2 == "ถูก": | |
| rn2 = "มีประวัติการกระทำผิดของบุคคลในครอบครัวและบุคคลที่เด็ก/เยาวชนอาศัยอยู่ด้วย" | |
| else: | |
| rn2 = "ไม่มีประวัติการกระทำผิดของบุคคลในครอบครัวและบุคคลที่เด็ก/เยาวชนอาศัยอยู่ด้วย" | |
| if rn3 == "ถูก": | |
| rn3 = "มีประวัติการเกี่ยวข้องกับยาเสพติดของบุคคลในครอบครัว" | |
| else: | |
| rn3 = "ไม่มีประวัติการเกี่ยวข้องกับยาเสพติดของบุคคลในครอบครัว" | |
| instruciton = "จงสร้างคำแนะนำของผู้เชี่ยวชาญจากปัจจัยดังต่อไปนี้" | |
| input = f"{birth_year} {nationality_name} {religion_name} {sex} {inform_status} {age} {offense_name} {allegation_name} {allegation_desc} {rn1} {rn2} {rn3} {education} {occupation} {province}" | |
| return input | |
| def generate_full_input(inst ,input): | |
| # output = ["True", "false"] | |
| # input = np.random.choice(output) | |
| # input = instruction + " " + input | |
| # first_element = check[0] # user text | |
| # last_element = check[-1] # input | |
| # instruction = check[-2] # instruction | |
| # input = f"{instruction} {last_element}" | |
| return f"{inst} {input}" | |
| def test_fucn(inst, input, stream): | |
| return str(inst) | |
| instruction = gr.Textbox(label = "คำสั่ง", value="จงสร้างคำแนะนำของผู้เชี่ยวชาญจากปัจจัยดังต่อไปนี้", visible=False, interactive=False) | |
| input_compo = gr.Textbox(label = "ข้อมูลเข้า (input)", show_copy_button = True, visible=False) | |
| # stream_output = gr.components.Checkbox(label="Stream output") | |
| full_input = gr.Textbox(label = "full prompt", visible=True, show_copy_button=True) | |
| btn1 = gr.Button("GENERATE INPUT") | |
| # show input text format for user | |
| btn1.click(fn=generate_input, inputs=[birth_year, nationality_name, religion_name, sex, | |
| inform_status, age, offense_name, allegation_name, | |
| allegation_desc, rn1, rn2, rn3, education, occupation, province], | |
| outputs=input_compo) | |
| # btn1.click(fn=generate_simple_output, inputs = [instruction, input_compo], outputs = full_input) | |
| input_compo.change(fn = generate_full_input, inputs=[instruction, input_compo], outputs=full_input) | |
| outputModel = gr.Textbox(label= "ผลลัพธ์ (output)") | |
| btn2 = gr.Button("GENERATE OUTPUT") | |
| btn2.click(fn=evaluate, inputs=[instruction, input_compo], outputs=outputModel) | |
| # input text format for model | |
| # btn.click(fn=generate_text_test2, inputs = [birth_year, nationality_name, religion_name, sex, | |
| # inform_status, age, offense_name, allegation_name, | |
| # allegation_desc, rn1, rn2, rn3, education, occupation, province], | |
| # outputs = input_compo) | |
| demo.launch(debug=True, share=True) |