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sim_case_sug_demo/g_h.py

@@ -5,7 +5,7 @@ _config={
     "sug_based_list":["dispute","plaintiff"],
     "sug_pool_list":["corpus3835","2022~2023"],
     "embedder_list":["ftlf","ftrob"],
-    "based_index":1,
+    "based_index":0,
     "pool_index":1,
     "emb_index":1,
     "sug_th":20,
@@ -177,6 +177,37 @@ def html_hl(lst):
         tp_lst.append(temp)
 
     return "".join(tp_lst)
+def ansi_to_html_dis(_f,file_path,_tranpose=True):
+
+    if _tranpose:
+        _dict={"item":["plaintiff","defendant","dispute","score"],_f["target"]+"(target)":["plaintiff_anchor2","defendant_anchor2","dispute_anchor2",""],_f["case_id"]:["plaintiff_anchor1","defendant_anchor1","dispute_anchor1","score_anchor"]}
+    else:
+        _dict={"case_name":[_f["case_id"],_f["target"]+"(target)"],"plaintiff":["plaintiff_anchor1","plaintiff_anchor2"],"defendant":["defendant_anchor1","defendant_anchor2"],"dispute":["dispute_anchor1","dispute_anchor2"],"score":["","score_anchor"]}
+
+
+    p1=html_hl(_f["plaintiff_case1"])
+    p2=html_hl(_f["plaintiff_case2"])
+    d1=html_hl(_f["defendant_case1"])
+    d2=html_hl(_f["defendant_case2"])
+    dis1=html_hl(_f["dispute_case1"])
+    dis2=html_hl(_f["dispute_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("defendant_anchor1",d1).replace("defendant_anchor2",d2)\
+    .replace("dispute_anchor1",dis1).replace("dispute_anchor2",dis2)\
+    .replace("score_anchor",score_)
+
+    with open(file_path, 'w',) as f:
+        f.write(html_table_blue_light)
+    return html_table_blue_light
 def ansi_to_html(_f,file_path,_tranpose=True):
 
     if _tranpose:
@@ -297,6 +328,164 @@ def ansi_to_image(ansi_text, font_size=20, image_path="./test.png"):
 #image_path = ansi_to_image(ansi_content)
 #
 #---------------------------------------
+def suggesting_dis(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["dispute"]][: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)
+    dlst_2=replace_all("".join(case_dict["defendant"]),key_lst,sp_key,1).split(sp_key)
+    v_plst_2=[_embedder.encode(_e) for _e in plst_2]
+    v_dlst_2=[_embedder.encode(_e) for _e in dlst_2]
+
+    print(clst_2)
+
+    rt_lst=[]
+    for i in tqdm(the_pool):
+        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)
+            
+            dlst_1=replace_all("".join(corpus_pd_f[i.replace("_",",")][1]),key_lst,sp_key,1).split(sp_key)
+            
+            v_plst_1=[_embedder.encode(_e) for _e in plst_1]
+            
+            v_dlst_1=[_embedder.encode(_e) for _e in dlst_1]
+            
+
+            cs_p1=[max([[cos_sim(_e,_v[0]),_v[-1]] for _v in vlst_1]) for _e in v_plst_1]
+            cs_d1=[max([[cos_sim(_e,_v[0]),_v[-1]] for _v in vlst_1]) for _e in v_dlst_1]
+
+            cs_p2=[max([[cos_sim(_e,_v[0]),_v[-1]] for _v in vlst_2]) for _e in v_plst_2]
+            cs_d2=[max([[cos_sim(_e,_v[0]),_v[-1]] for _v in vlst_2]) for _e in v_dlst_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_d1=[[cs_d1[_e][-1],Fore.WHITE,dlst_1[_e],Style.RESET_ALL] if cs_d1[_e][0]>_th else [Style.RESET_ALL,dlst_1[_e]] for _e in range(len(cs_d1))]
+            
+            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))]
+            pre_lst_d2=[[cs_d2[_e][-1],Fore.WHITE,dlst_2[_e],Style.RESET_ALL] if cs_d2[_e][0]>_th else [Style.RESET_ALL,dlst_2[_e]] for _e in range(len(cs_d2))]
+            
+
+            #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]
+            draw_lst_d1=["".join(_e) for _e in pre_lst_d1]
+            draw_lst_d2=["".join(_e) for _e in pre_lst_d2]
+            #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["defendant_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_d1]
+            temp_ot["dispute_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["defendant_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_d2]
+            temp_ot["dispute_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+"---defendant_case1---"+Style.RESET_ALL+"\n"
+            tp_str+="".join(draw_lst_d1)+Style.RESET_ALL+"\n"
+
+            tp_str+=Fore.MAGENTA+"---dispute_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+"---defendant_case2---"+Style.RESET_ALL+"\n"
+            tp_str+="".join(draw_lst_d2)+Style.RESET_ALL+"\n"
+            
+            tp_str+=Fore.MAGENTA+"---dispute_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]
 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
@@ -461,55 +650,100 @@ emb_model_path={\
 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]
+#log_f=json.load(open("./src/plaintiff_logistic_features.json","r"))["BiLSTM_CNN"]
+
 
 
 
 #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 sug_type=="plaintiff":
+    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_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"))
 
-if pool_type=="corpus3835":
-    corpus_clust_label=clust_label(o_c_f)
+    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"]
+        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={}
+        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}
+        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}
+
+elif sug_type=="dispute":
+    log_f=json.load(open("./src/dispute_logistic_features.json","r"))["BiLSTM_CNN"]
+    x_r=np.array(log_f)[:,:-1]
+    y_r=np.array(log_f)[:,-1]
+    pd_f=corpus_pd_f=json.load(open("./src/corpus3835_raw_dis.json","r"))["claim"]
+    s_f=json.load(open("./src/dispute_corpus3835_sen.json","r"))
+    v_f=json.load(open("./src/dispute_corpus3835_vec.json","r"))#json.load(open(_dir+v_path,"r"))
+
+    o_c_f=json.load(open("./src/dispute_corpus3835_cluster.json","r"))["clusters"]
+    c_f=clust_2_dict(o_c_f)
+    t_f=json.load(open("./src/dispute_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_dis.json","r"))["claim"]
+    else:
+        vec_f=json.load(open("./src/dispute_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/dispute_2022~2023_raw.json","r"))
+        corpus_pd_f=json.load(open("./src/new22_23_3k3_corpus_raw.json","r"))["claim"]
+        corpus_clust_f=json.load(open("./src/dispute_22~23_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("./src/gpt-4-turbo-0409-0.3-new22_23.jsonl"))
+new_pd_f=json.load(open("./src/new22_23_3k3_corpus_raw.json","r"))["claim"]
 ###
 
-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"]
+
 
 
 
@@ -539,6 +773,48 @@ from colorama import Fore,Style,Back
 
 import gradio as gr
 
+def case_sug_dis(file_name,plaintiff,defendant,p_point,d_point,dispute_list):
+    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]
+        defendant=new_pd_f[file_name][1]
+        p_point=new_point_f[file_name][0]
+        d_point=new_point_f[file_name][1]
+        dispute_list=new_point_f[file_name][2]
+
+    global sug_th
+
+
+    p_point="。".split(p_point) if type(p_point)==type("111") else p_point
+    d_point="。".split(d_point) if type(d_point)==type("111") else d_point
+    dispute_list="。".split(dispute_list) if type(dispute_list)==type("111") else dispute_list
+    _pool=[i for i in corpus_dict]
+    _case_dict={"plaintiff":plaintiff,"defendant":defendant,"p_point":p_point,"d_point":d_point,"dispute":dispute_list}
+    ot,ot_dict=suggesting_dis(_pool,file_name,_case_dict)
+
+
+    dispute="\n".join(dispute_list)
+    #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_dis(ot_dict[i],_path)
+            #output_image = Image.open(_path)
+            output_list.append(_path)
+        else:
+            output_list.append(out_path)
+    return output_list
 def case_sug(file_name,plaintiff,p_point):
     global new_pd_f,new_point_f,corpus_dict
 
@@ -577,6 +853,9 @@ def case_sug(file_name,plaintiff,p_point):
         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)   
+if sug_type=="plaintiff":
+    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)
+elif sug_type=="dispute":
+    demo = gr.Interface(fn=case_sug_dis, inputs=["text","text","text","text","text","text"], outputs=[gr.outputs.File() for i in range(sug_th)])
+    demo.launch(share=True,server_port=2048,show_error=True)