sim_case_sug_demo/g_h.py

import json
inset_th=1
#_config=json.load(open("config.json","r"))
_config={
    "sug_based_list":["dispute","plaintiff"],
    "sug_pool_list":["corpus3835","2022~2023"],
    "embedder_list":["ftlf","ftrob"],
    "based_index":1,
    "pool_index":1,
    "emb_index":1,
    "sug_th":20,
    "cluster_epsilon":0.67,
    "similiar_trace_back_th":0.98,
    "back_ground_RGB":[77, 6, 39]
}
emb_dim_lst=[768,1024]
bilstm_len_lst=[19,13]
cnn_len_lst=[32,18]

emb_dim=emb_dim_lst[_config["emb_index"]]
bilstm_len=bilstm_len_lst[_config["based_index"]]
cnn_len=cnn_len_lst[_config["based_index"]]


sug_type=_config["sug_based_list"][_config["based_index"]]
pool_type=_config["sug_pool_list"][_config["pool_index"]]
emb_type=_config["embedder_list"][_config["emb_index"]]

sug_th=_config["sug_th"]

clust_th=_config["cluster_epsilon"]
_th=_config["similiar_trace_back_th"]

bg_rgb=(_config["back_ground_RGB"][0],_config["back_ground_RGB"][1],_config["back_ground_RGB"][2])



import os,sys


#_gpu=(1==1)
#if not _gpu: 
#    os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
import cv2#opencv-python                4.6.0.66
import colorama
from colorama import Fore,Style,Back
import json
import numpy as np
from numpy.linalg import norm
from sentence_transformers import SentenceTransformer
from tqdm import tqdm
import tensorflow as tf
from tensorflow.keras.models import load_model
#---------------------------------------
def logistic(x_r,y_r,x_e,_proba=True):
    from sklearn import linear_model
    from sklearn.inspection import permutation_importance
    model=linear_model.LogisticRegression(max_iter=100000)
    model.fit(x_r,y_r)

    p_e=model.predict(x_e)
    prob_e=model.predict_proba(x_e)
    prob_sum=[i[1] for i in prob_e]
    return (prob_sum if _proba else p_e)

def cos_sim(a,b):
    return np.dot(a,b)/(norm(a)*norm(b))
def replace_all(t,rp_lst,k,_type=0):
    temp=t
    for _e in rp_lst:
        
        if _type==-1:
             temp=temp.replace(_e,k+_e)
        elif _type==1:
             temp=temp.replace(_e,_e+k)
        else:
            temp=temp.replace(_e,k)        
    return temp
def jl(file_path):
    with open(file_path, "r", encoding="utf8") as json_file:
        json_list = list(json_file)
    return [json.loads(json_str) for json_str in json_list]
def lst_2_dict(lst):
    _dict={i["filename"]:[i["p_point"],i["d_point"],i["Controversy"]] for i in lst}
    return _dict
def clust_2_dict(clust):
    _dict={}

    ct=0
    for i in clust:

        if len(clust[i])==1:
            _dict[clust[i][0]]=-1
        else:
            ct+=1
            for _e in clust[i]:

                _dict[_e]=ct
    return _dict
def clust_label(clust):
    _dict={}
    for i in clust:
        for _e in clust[i]:
            if len(clust[i])>1:
                _dict[_e]=i
            else:
                _dict[_e]='-1'
    return _dict
#-----------------------------
def clust_core(clust,vec_lst,id_lst,_type="mean"):
    _dict={}
    for i in clust:
        if _type=="head":
            _dict[i]=vec_lst[id_lst.index(clust[i][0])] 
        elif _type=="central":
            tp_lst=np.array([vec_lst[id_lst.index(_e)] for _e in clust[i]])
            temp=np.average(tp_lst, axis=0)
            cs_lst=[[cos_sim(_e,temp),list(_e)] for _e in tp_lst]
            _dict[i]=max(cs_lst)[-1]
        else:#_type=="mean"
            tp_lst=np.array([vec_lst[id_lst.index(_e)] for _e in clust[i]])
            _dict[i]=np.average(tp_lst, axis=0)
    return _dict

def clust_search(core_dict,target,clust_th=0.65):
    temp=max([[cos_sim(target,core_dict[i]),i] for i in core_dict])
    ot_,label_=temp

    return label_ if ot_>=clust_th else '-1' 

def vec2img(vec_lst1,clust_lst1,vec_lst2,clust_lst2,r):
    tp_lst1=[[vec_lst1[i],clust_lst1[i]] for i in range(len(clust_lst1))]
    tp_lst2=[[vec_lst2[i],clust_lst2[i]] for i in range(len(clust_lst2))]

    lst1=sorted(tp_lst1,key=lambda x:x[1])
    lst2=sorted(tp_lst2,key=lambda x:x[1])

    m_lst=lst1+lst2
    _img=[[255 for _ee in range(len(m_lst))] for _e in range(len(m_lst))]
    for i in range(len(m_lst)):
        for j in range(len(m_lst)):
            if i<j:
                temp=cos_sim(m_lst[i][0],m_lst[j][0])
                _tp=(temp-r)/(1-r)*128+127 if temp>r else temp/r*128

                _tp=int(_tp-1)
                _img[i][j]=_tp
                _img[j][i]=_tp
    return _img
def img_resize(_img,_max_size):
    return cv2.resize(np.array(_img).astype('float32'), (_max_size, _max_size), interpolation=cv2.INTER_AREA).tolist()
def cnn_load(_device="/gpu:0"):
    global cnn_model
    with tf.device(_device):
        cnn_model=load_model("./models/"+sug_type+"_"+emb_type+"_cnn.dat")
        cnn_model.load_weights("./models/"+sug_type+"_"+emb_type+"_cnn_best.hdf5")
def bilstm_load(_device="/gpu:0"):
    global bilstm_model
    with tf.device(_device):
        bilstm_model=load_model("./models/"+sug_type+"_"+emb_type+"_sa.dat")
        bilstm_model.load_weights("./models/"+sug_type+"_"+emb_type+"_sa_best.hdf5")
#---------------------------------------
_tranpose=(1==1)
from colorama import Fore,Style,Back
from pretty_html_table import build_table
import pandas as pd


def html_hl(lst):
    #font_path = "./font/TaipeiSansTCBeta-Regular.ttf"
    #font = ImageFont.truetype(font_path, font_size)

    tp_lst=[]

    for i in lst:
        temp="<mark style=\"background:"+i["background_color"]+";color:"+i["font_color"]+"\">"+i["content"]+"</mark>"
        tp_lst.append(temp)

    return "".join(tp_lst)
def ansi_to_html(_f,file_path,_tranpose=True):

    if _tranpose:
        _dict={"item":["plaintiff","p_point","score"],_f["target"]+"(target)":["plaintiff_anchor2","p_point_anchor2",""],_f["case_id"]:["plaintiff_anchor1","p_point_anchor1","score_anchor"]}
    else:
        _dict={"case_name":[_f["case_id"],_f["target"]+"(target)"],"plaintiff":["plaintiff_anchor1","plaintiff_anchor2"],"p_point":["p_point_anchor1","p_point_anchor2"],"score":["","score_anchor"]}


    p1=html_hl(_f["plaintiff_case1"])
    p2=html_hl(_f["plaintiff_case2"])

    p_point1=html_hl(_f["p_point_case1"])
    p_point2=html_hl(_f["p_point_case2"])
    score_="\n<mark style=\"background:#ffffff;color:"+("green" if _f["ensemble_pred"]>=0.75 else "yellow" if _f["ensemble_pred"]>=0.5 else "red")+"\">"+str(_f["ensemble_pred"])+"</mark>"
    #score_="<mark style=\"color:>"++"\">"+str(_f["ensemble_pred"])+"</mark>"

    df=pd.DataFrame(_dict)
    html_table_blue_light = build_table(df, 'blue_light')
    #print(type(html_table_blue_light))
    injection="<meta charset=\"UTF-8\">"
    #"<td style = \"background-color: #D9E1F2;font-family: Century Gothic, sans-serif;font-size: medium;text-align: left;padding: 0px 20px 0px 0px;width: auto\">"
    html_table_blue_light=html_table_blue_light[:html_table_blue_light.find("<thead>")+7]+injection+html_table_blue_light[html_table_blue_light.find("<thead>")+7:]
    html_table_blue_light=html_table_blue_light.replace("plaintiff_anchor1",p1).replace("plaintiff_anchor2",p2)\
    .replace("p_point_anchor1",p_point1).replace("p_point_anchor2",p_point2)\
    .replace("score_anchor",score_)

    with open(file_path, 'w',) as f:
        f.write(html_table_blue_light)
    return html_table_blue_light
#---------------------------------------
from PIL import Image, ImageDraw, ImageFont



# Dictionary mapping colorama codes to RGB colors
ANSI_BG_COLORS = {
    Fore.BLACK: (0, 0, 0),
    Fore.RED: (255, 0, 0),
    Fore.GREEN: (0, 255, 0),
    Fore.YELLOW: (255, 255, 0),
    Fore.BLUE: (0, 0, 255),
    Fore.MAGENTA: (255, 0, 255),
    Fore.CYAN: (0, 255, 255),
    Fore.WHITE: (255, 255, 255),
    Fore.RESET: (0, 0, 0),  # Reset to black
    Back.BLACK: (0, 0, 0),
    Back.RED: (255, 0, 0),
    Back.GREEN: (0, 255, 0),
    Back.YELLOW: (255, 255, 0),
    Back.BLUE: (0, 0, 255),
    Back.MAGENTA: (255, 0, 255),
    Back.CYAN: (0, 255, 255),
    Back.WHITE: (255, 255, 255),
    '\033[0m': bg_rgb  # Reset to White background
}

ANSI_COLORS={_e:"#"+str(hex(1*256*256*256+ANSI_BG_COLORS[_e][0]*256*256+ANSI_BG_COLORS[_e][1]*256+ANSI_BG_COLORS[_e][2]))[3:] for _e in ANSI_BG_COLORS}
def ansi_to_image(ansi_text, font_size=20, image_path="./test.png"):
    global bg_rgb
    font_path = "./font/TaipeiSansTCBeta-Regular.ttf"
    font = ImageFont.truetype(font_path, font_size)


    # Split the text into lines
    lines = ansi_text.split('\n')

    # Calculate image size
    max_width = 0
    total_height = 0
    line_heights = []
    for line in lines:
        text_width, text_height = font.getsize(line)
        max_width = max(max_width, text_width)
        total_height += text_height
        line_heights.append(text_height)

    # Create a blank image
    image = Image.new('RGB', (max_width, total_height), color=bg_rgb)
    draw = ImageDraw.Draw(image)

    y = 0
    for line, line_height in zip(lines, line_heights):
        x = 0
        segments = line.split('\033')
        anchor_bg_color=(255,255,255)
        for segment in segments:
            #print(segment)
            if segment and  segment[-1]=='m':
                code= segment[:-1]
                anchor_bg_color = ANSI_BG_COLORS.get(f'\033{code}m', anchor_bg_color)
                #text_width, text_height = draw.textsize(text, font=font)
                #draw.rectangle([x, y, x + text_width, y + line_height], fill=(255, 255, 255))
                #draw.text((x, y), text, font=font, fill=anchor_bg_color)
                x += 0
            if 'm' in segment:
                code, text = segment.split('m', 1)
                font_color = ANSI_BG_COLORS.get(f'\033{code}m', anchor_bg_color)
                text_width, text_height = draw.textsize(text, font=font)
                draw.rectangle([x, y, x + text_width, y + line_height], anchor_bg_color)
                draw.text((x, y), text, font=font, fill=font_color)
                x += text_width
            else:

                text = segment
                text_width, text_height = draw.textsize(text, font=font)
                draw.text((x, y), text, font=font, fill=(255,255,255))
                x += text_width
        y += line_height

    # Save the image
    image.save(image_path)
    return image_path

# 示例ANSI文本
#ansi_content = '\033[44m555\033[0m\n111\033[41m555\033[0m'

# 將ANSI轉換為圖像
#image_path = ansi_to_image(ansi_content)
#
#---------------------------------------
def suggesting(the_pool,target_name,case_dict):
    global ANSI_COLORS,_th,c_th,sug_th,corpus_dict,corpus_pd_f,vec_lst,id_lst,sen_lst,corpus_clust_label,_cluster_core_dict,_embedder
    global bilstm_len,cnn_len,emb_dim,inset_th,clust_th
    lst_2=[_e for _e in case_dict["p_point"]][:bilstm_len]

    #for _e in lst2:
    #    temp=_embedder.encode(_e)
    #    vec_lst_2.append()
    vec_lst_2=[_embedder.encode(_e) for _e in lst_2]
    
    clst_2=[clust_search(_cluster_core_dict,_e,clust_th) for _e in vec_lst_2]
    plst_2=replace_all("".join(case_dict["plaintiff"]),key_lst,sp_key,1).split(sp_key)

    v_plst_2=[_embedder.encode(_e) for _e in plst_2]


    print(clst_2)

    rt_lst=[]
    for i in tqdm(the_pool):
        if target_name==i:
            continue
        lst_1=[_e for _e in corpus_dict[i]]
        id_lst_1=[id_lst[sen_lst.index(_e)] for _e in lst_1]
        vec_lst_1=[vec_lst[sen_lst.index(_e)] for _e in lst_1]#[_embedder.encode(_e) for _e in lst_1]
        clst_1=[corpus_clust_label[_e] for _e in id_lst_1]#[clust_search(_cluster_core_dict,_e,0.68) for _e in vec_lst_1]
        #print(clst_1)
        inset=sorted([_e for _e in set(clst_1)&set(clst_2) if _e!=-1])
        temp_ot={}
        if len(inset)>=max(1,inset_th):
            temp_ot["target"]=target_name
            temp_ot["inset"]=inset
            #print(len(inset))
            _img=img_resize(vec2img(vec_lst_1,clst_1,vec_lst_2,clst_2,clust_th),cnn_len)
            cnn_pred=cnn_model.predict(np.array([_img])/255)

            _con1,_con2=[],[]
            for tp_i in range(bilstm_len):
                if len(lst_1)>tp_i:
                    _con1.append(vec_lst_1[tp_i])
                else:
                    _con1.append([0]*emb_dim)
            for tp_i in range(bilstm_len):
                if len(lst_2)>tp_i:
                    _con2.append(vec_lst_2[tp_i])
                else:
                    _con2.append([0]*emb_dim)
            _con1=np.array([_con1])
            _con2=np.array([_con2])
            print(len(_con1),len(_con2),len(_con2[0]))
            #_con1=list(np.array(vec_lst_1).reshape(len(lst_1)*emb_dim))+[0]*(emb_dim*(bilstm_len-len(lst_1))) if len(lst_1)<=bilstm_len else list(np.array(vec_lst_1).reshape(len(lst_1)*emb_dim))[:bilstm_len*emb_dim]
            #_con2=list(np.array(vec_lst_2).reshape(len(lst_2)*emb_dim))+[0]*(emb_dim*(bilstm_len-len(lst_2))) if len(lst_2)<=bilstm_len else list(np.array(vec_lst_2).reshape(len(lst_2)*emb_dim))[:bilstm_len*emb_dim]
            bilstm_pred=bilstm_model.predict([_con1,_con2])
            temp_ot["cnn_pred"]=float(cnn_pred[0][0])
            temp_ot["bilstm_pred"]=float(bilstm_pred[0][0])
            #print(cnn_pred)
            #print(bilstm_pred)
            x_e=[[bilstm_pred[0][0],cnn_pred[0][0]]]
            ensemble_pred=logistic(x_r,y_r,x_e)
            temp_ot["ensemble_pred"]=float(ensemble_pred[0])
            #print(ensemble_pred)

            pre_lst_1=[[color_lst[inset.index(clst_1[_e]) % len(color_lst)],Fore.WHITE,lst_1[_e],Style.RESET_ALL] if clst_1[_e] in inset else [Style.RESET_ALL,lst_1[_e]] for _e in range(len(lst_1))]
            pre_lst_2=[[color_lst[inset.index(clst_2[_e]) % len(color_lst)],Fore.WHITE,lst_2[_e],Style.RESET_ALL] if clst_2[_e] in inset else [Style.RESET_ALL,lst_2[_e]] for _e in range(len(lst_2))]
            
            vlst_1=[[vec_lst_1[_e],pre_lst_1[_e][0]] for _e in range(len(pre_lst_1)) if len(pre_lst_1[_e])==4]
            vlst_2=[[vec_lst_2[_e],pre_lst_2[_e][0]] for _e in range(len(pre_lst_2)) if len(pre_lst_2[_e])==4]
            
            #print(lst_1)

            plst_1=replace_all("".join(corpus_pd_f[i.replace("_",",")][0]),key_lst,sp_key,1).split(sp_key)
            
    
            v_plst_1=[_embedder.encode(_e) for _e in plst_1]
            
     

            cs_p1=[max([[cos_sim(_e,_v[0]),_v[-1]] for _v in vlst_1]) for _e in v_plst_1]

            cs_p2=[max([[cos_sim(_e,_v[0]),_v[-1]] for _v in vlst_2]) for _e in v_plst_2]
 
            pre_lst_p1=[[cs_p1[_e][-1],Fore.WHITE,plst_1[_e],Style.RESET_ALL] if cs_p1[_e][0]>_th else [Style.RESET_ALL,plst_1[_e]] for _e in range(len(cs_p1))]

            
            pre_lst_p2=[[cs_p2[_e][-1],Fore.WHITE,plst_2[_e],Style.RESET_ALL] if cs_p2[_e][0]>_th else [Style.RESET_ALL,plst_2[_e]] for _e in range(len(cs_p2))]

            

            #if max_dp<max([len(plst_1),len(plst_2),len(dlst_1),len(dlst_2)]):
            #   max_dp=max([len(plst_1),len(plst_2),len(dlst_1),len(dlst_2)])
            
            #print(plst_1)
            #print(plst_2)
            #print(dlst_1)
            #print(dlst_2)
            draw_lst_1=["".join(_e) for _e in pre_lst_1]
            draw_lst_2=["".join(_e) for _e in pre_lst_2]

            draw_lst_p1=["".join(_e) for _e in pre_lst_p1]
            draw_lst_p2=["".join(_e) for _e in pre_lst_p2]

            #replace_all(temp_c,key_lst,",",0)

            #print(plst_1)
            tp_str=""

            #print("---------------------")
            #print(Fore.BLUE+str(i)+Style.RESET_ALL)
            temp_ot["case_id"]=i
            temp_ot["plaintiff_case1"]=[{"background_color":ANSI_COLORS[_e[0]],"font_color":ANSI_COLORS[_e[1]],"content":_e[-2]} if len(_e)==4 else {"background_color":ANSI_COLORS[_e[0]],"font_color":ANSI_COLORS[Fore.WHITE],"content":_e[-1]} for _e in pre_lst_p1]
            temp_ot["p_point_case1"]=[{"background_color":ANSI_COLORS[_e[0]],"font_color":ANSI_COLORS[_e[1]],"content":_e[-2]} if len(_e)==4 else {"background_color":ANSI_COLORS[_e[0]],"font_color":ANSI_COLORS[Fore.WHITE],"content":_e[-1]} for _e in pre_lst_1]
            temp_ot["plaintiff_case2"]=[{"background_color":ANSI_COLORS[_e[0]],"font_color":ANSI_COLORS[_e[1]],"content":_e[-2]} if len(_e)==4 else {"background_color":ANSI_COLORS[_e[0]],"font_color":ANSI_COLORS[Fore.WHITE],"content":_e[-1]} for _e in pre_lst_p2]
            temp_ot["p_point_case2"]=[{"background_color":ANSI_COLORS[_e[0]],"font_color":ANSI_COLORS[_e[1]],"content":_e[-2]} if len(_e)==4 else {"background_color":ANSI_COLORS[_e[0]],"font_color":ANSI_COLORS[Fore.WHITE],"content":_e[-1]} for _e in pre_lst_2]

            tp_str+=Fore.BLUE+str(i)+Style.RESET_ALL+"\n"
            tp_str+=(Fore.GREEN if temp_ot["ensemble_pred"]>=0.75 else Fore.YELLOW if temp_ot["ensemble_pred"]>=0.5 else Fore.RED)+str(temp_ot["ensemble_pred"])+Style.RESET_ALL+"\n"
            tp_str+=Fore.MAGENTA+"---plaintiff_case1---"+Style.RESET_ALL+"\n"
            tp_str+="".join(draw_lst_p1)+Style.RESET_ALL+"\n"
            

            tp_str+=Fore.MAGENTA+"---p_point_case1---"+Style.RESET_ALL+"\n"
            tp_str+="".join(draw_lst_1)+Style.RESET_ALL+"\n"
            ###
            tp_str+=Fore.BLUE+"target"+Style.RESET_ALL+"\n"
            
            tp_str+=Fore.MAGENTA+"---plaintiff_case2---"+Style.RESET_ALL+"\n"
            tp_str+="".join(draw_lst_p2)+Style.RESET_ALL+"\n"


            tp_str+=Fore.MAGENTA+"---p_point_case2---"+Style.RESET_ALL+"\n"
            tp_str+="".join(draw_lst_2)+Style.RESET_ALL+"\n"
            
            #tp_str+="---------------------"+"\n"

            
            
            temp_ot["output"]=tp_str
            rt_lst.append(temp_ot)
            print(tp_str)
    ot=sorted(rt_lst,key=lambda x:x["ensemble_pred"],reverse=True)
    ot_lst=[i["output"] for i in ot[:sug_th]]

    for i in ot[:sug_th]:
        file=open("./json_file/"+str(target_name).replace(",","_")+"&"+str(i["case_id"])+".json","w",encoding='utf8')
        json.dump({_e:i[_e] for _e in i if _e!="output"},file,indent=4,ensure_ascii=False)
        file.close()
    return ot_lst,ot[:sug_th]
#---------------------------------------


_dir_lst=["../gpt4_0409_p_3/","../taide_llama3_8b_3/"]
_dir=_dir_lst[0]
sp_key="@"
emb_model="ftrob"
emb_model_path={\
                "lf":"thunlp/Lawformer",\
                "rob":'hfl/chinese-roberta-wwm-ext-large',\
                "ftlf":"./sbert_pretrained_model/training-lawformer-clause_th10_100k_task-bs100-e2-2023-10-28/",
                "ftrob":"./sbert_pretrained_model/training-roberta-clause_th10_100k_task-bs100-e2-2023-10-27",\
                }

color_lst=[Back.BLUE,Back.GREEN,Back.MAGENTA,Back.YELLOW,Back.RED,Back.CYAN]#[Fore.RED,Fore.GREEN,Fore.YELLOW,Fore.BLUE,Fore.MAGENTA,Fore.CYAN]


log_f=json.load(open("./src/plaintiff_logistic_features.json","r"))["BiLSTM_CNN"]
x_r=np.array(log_f)[:,:-1]
y_r=np.array(log_f)[:,-1]



#pd_path,dis_path,s_path,v_path,c_path,t_path,cr_path,br_path=["TAIDE-LX-8B.jsonl","llama3_taide_8b_re_3_o_c.json","sentence.json","vector.json","hdb_cluster.json","hdb_ternary_array.json","hdb_cnn_result.json","hdb_sa_result.json"]

pd_f=corpus_pd_f=json.load(open("./src/corpus3835_raw.json","r"))["claim"]
s_f=json.load(open("./src/plaintiff_corpus3835_sen.json","r"))
v_f=json.load(open("./src/plaintiff_corpus3835_vec.json","r"))#json.load(open(_dir+v_path,"r"))

o_c_f=json.load(open("./src/plaintiff_corpus3835_cluster.json","r"))["clusters"]
c_f=clust_2_dict(o_c_f)
t_f=json.load(open("./src/plaintiff_ter.json","r"))


if pool_type=="corpus3835":
    corpus_clust_label=clust_label(o_c_f)

    vec_lst=v_f["vector"]
    id_lst=v_f["id"]
    sen_lst=s_f["sentence"]

    corpus_dict={}
    for i in range(len(id_lst)):
        fid=id_lst[i].split("@")[0]
        if fid not in corpus_dict:
            corpus_dict[fid]=[sen_lst[i]]
        else:
            corpus_dict[fid].append(sen_lst[i])
    corpus_pd_f=json.load(open("./src/corpus3835_raw.json","r"))["claim"]
else:
    vec_f=json.load(open("./src/plaintiff_2022~2023_vec.json","r"))
    vec_lst=[_e for i in vec_f for _e in vec_f[i]]


    corpus_dict=json.load(open("./src/plaintiff_2022~2023_raw.json","r"))
    corpus_pd_f=json.load(open("./src/2022~2023_raw.json","r"))["claim"]
    corpus_clust_f=json.load(open("./src/plaintiff_2022~2023_clust.json","r"))
    
    sen_lst=[_e for i in corpus_dict for _e in corpus_dict[i]]
    id_lst=[i+"@"+str(_e) for i in corpus_dict for _e in range(len(corpus_dict[i]))]
    corpus_clust_label={_e:corpus_clust_f[_e[:_e.find("@")]][int(_e[_e.find("@")+1:])] for _e in id_lst}

###

new_point_f=lst_2_dict(jl("../law/2022~2023/gpt-4-turbo-0409-0.3-new22_23.jsonl"))
new_pd_f=json.load(open("../law/2022~2023/new22_23_3k3_corpus_raw.json","r"))["claim"]





key_lst=["，","。","？","?","!","！",";",":","；","："]#["。","？","?","!","！",";",":","；","："]


_embedder = SentenceTransformer(emb_model_path[emb_model])
cnn_model =...
bilstm_model =...

"""#fifo
cnn_load()
bilstm_load()
"""
cnn_load("/cpu:0")
bilstm_load("/cpu:0")
#"""



_cluster_core_dict=clust_core(o_c_f,v_f["vector"],v_f["id"],"central")
#---------------------------------------

from colorama import Fore,Style,Back

import gradio as gr

def case_sug(file_name,plaintiff,p_point):
    global new_pd_f,new_point_f,corpus_dict

    print(file_name)
    #print(point_f)
    #print(list(pd_f.keys()).index(file_name))
    if file_name not in new_pd_f:
        print("file not found")
        file_name="user_input"
    else:
        plaintiff=new_pd_f[file_name][0]
        p_point=new_point_f[file_name][0]


    global sug_th

    p_point=p_point.split("。") if type(p_point)==type("111") else p_point
    _pool=[i for i in corpus_dict]
    _case_dict={"plaintiff":plaintiff,"p_point":p_point}
    print(_case_dict,[type(_case_dict[_e]) for _e in _case_dict])
    ot,ot_dict=suggesting(_pool,file_name,_case_dict)



    #ot=[Back.BLUE+dispute+Style.RESET_ALL]*10
    output_list=[]
    print("-----")
    print(len(ot_dict))
    out_path="./out_of_range.html"
    for i in range(sug_th):
        if i<len(ot_dict):
            _path="./html_file/test"+str(i)+".html"
            output_html=ansi_to_html(ot_dict[i],_path)
            #output_image = Image.open(_path)
            output_list.append(_path)
        else:
            output_list.append(out_path)
    return output_list

demo = gr.Interface(fn=case_sug, inputs=["text","text","text"], outputs=[gr.outputs.File() for i in range(sug_th)])
demo.launch(share=True,server_port=4096,show_error=True)