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| from textblob import TextBlob | |
| import gradio as gr | |
| import math | |
| import os | |
| os.system("python -m textblob.download_corpora") | |
| control_json={'control':'0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ','char':'','leng':62} | |
| string_json={'control':'0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMN','char':'OPQRSTUVWXYZ','leng':50} | |
| cont_list=list(string_json['control']) | |
| text=""" | |
| I asked Generative AI Models about their context window. Their response was intriguing. | |
| The context window for a large language model (LLM) like OpenAI’s GPT refers to the maximum amount of text the model can consider at any one time when generating a response. This includes both the prompt provided by the user and the model’s generated text. | |
| In practical terms, the context window limits how much previous dialogue the model can “remember” during an interaction. If the interaction exceeds the context window, the model loses access to the earliest parts of the conversation. This limitation can impact the model’s consistency in long conversations or complex tasks. | |
| I asked Generative AI Models about their context window. Their response was intriguing. | |
| The context window for a large language model (LLM) like OpenAI’s GPT refers to the maximum amount of text the model can consider at any one time when generating a response. This includes both the prompt provided by the user and the model’s generated text. | |
| In practical terms, the context window limits how much previous dialogue the model can “remember” during an interaction. If the interaction exceeds the context window, the model loses access to the earliest parts of the conversation. This limitation can impact the model’s consistency in long conversations or complex tasks. | |
| I asked Generative AI Models about their context window. Their response was intriguing. | |
| The context window for a large language model (LLM) like OpenAI’s GPT refers to the maximum amount of text the model can consider at any one time when generating a response. This includes both the prompt provided by the user and the model’s generated text. | |
| In practical terms, the context window limits how much previous dialogue the model can “remember” during an interaction. If the interaction exceeds the context window, the model loses access to the earliest parts of the conversation. This limitation can impact the model’s consistency in long conversations or complex tasks. | |
| I asked Generative AI Models about their context window. Their response was intriguing. | |
| The context window for a large language model (LLM) like OpenAI’s GPT refers to the maximum amount of text the model can consider at any one time when generating a response. This includes both the prompt provided by the user and the model’s generated text. | |
| In practical terms, the context window limits how much previous dialogue the model can “remember” during an interaction. If the interaction exceeds the context window, the model loses access to the earliest parts of the conversation. This limitation can impact the model’s consistency in long conversations or complex tasks. | |
| I asked Generative AI Models about their context window. Their response was intriguing. | |
| The context window for a large language model (LLM) like OpenAI’s GPT refers to the maximum amount of text the model can consider at any one time when generating a response. This includes both the prompt provided by the user and the model’s generated text. | |
| In practical terms, the context window limits how much previous dialogue the model can “remember” during an interaction. If the interaction exceeds the context window, the model loses access to the earliest parts of the conversation. This limitation can impact the model’s consistency in long conversations or complex tasks. | |
| """ | |
| def assign_val(inp, rng, cnt, limit): | |
| if go: | |
| for ea in range(rng): | |
| if go: | |
| noun_list[str(noun)].append(f'{a}{cont_list[b]}{cont_list[c]}{cont_list[d]}') | |
| if json_object[f'{a}{cont_list[b]}{cont_list[c]}{cont_list[d]}']=='ZNNN': | |
| a="Y" | |
| b=0 | |
| c=0 | |
| d=0 | |
| if cnt == key_cnt-1: | |
| print('done') | |
| go=False | |
| print(list(json_object.keys())[-1]) | |
| else: | |
| cnt+=1 | |
| def get_nouns(text=text,steps=1): | |
| control_len=control_json['leng']-steps | |
| control_char=list(control_json['control'][:control_len]) | |
| control_val=list(control_json['control'][control_len:]) | |
| char_len=len(control_char) | |
| val_len=len(control_val) | |
| print(control_char) | |
| print(control_val) | |
| json_object={} | |
| sen_list=[] | |
| noun_list={} | |
| noun_box=[] | |
| #print(text) | |
| blob = TextBlob(text) | |
| for sentence in blob.sentences: | |
| sen_list.append(str(sentence)) | |
| noun_box=[] | |
| for ea in blob.parse().split(" "): | |
| #print(ea) | |
| n=ea.split("/") | |
| if n[1] == "NN": | |
| noun_box.append(n[0]) | |
| print(sen_list) | |
| key_cnt=len(sen_list) | |
| noun_cnt=len(noun_box) | |
| print(key_cnt) | |
| print(noun_cnt) | |
| big_cnt=0 | |
| cnt=0 | |
| go=True | |
| a="Z" | |
| step_allot=char_len**steps | |
| print(step_allot) | |
| div_raw=(step_allot/noun_cnt) | |
| print(div_raw) | |
| div_steps=int(step_allot/noun_cnt) | |
| print(div_steps) | |
| div_remain=div_raw-div_steps | |
| print(div_remain) | |
| steps_mult=div_remain*char_len | |
| #steps_mult=div_remain*char_len | |
| print(steps_mult) | |
| print(math.ceil(steps_mult)) | |
| step_list=[] | |
| step_control="" | |
| step_cont_box=[] | |
| for ii in range(steps): | |
| print(ii) | |
| step_cont_box.append(0) | |
| #print (step_cont_box) | |
| mod=0 | |
| pos=len(step_cont_box)-1 | |
| if go: | |
| for i, ea in enumerate(noun_box): | |
| if go: | |
| if cnt > char_len+1: | |
| go1=True | |
| for ii,ev in enumerate(step_cont_box): | |
| if go: | |
| if ev >= char_len: | |
| step_cont_box[ii]=0 | |
| if go1==True: | |
| step_cont_box[ii-1]=step_cont_box[ii-1]+1 | |
| go1=False | |
| #pos-=1 | |
| cnt=0 | |
| else: | |
| #step_cont_box[pos]=control_char[cnt] | |
| step_cont_box[pos]=cnt | |
| cnt+=1 | |
| print(step_cont_box) | |
| big_cnt+=1 | |
| if big_cnt==noun_cnt: | |
| print("DONE") | |
| go=False | |
| return json_object,noun_list | |
| def get_nouns_OG(text,steps=1): | |
| control_len=control_json['leng']-steps | |
| control_new=control_json['control'][:control_len] | |
| control_char=control_json['control'][control_len:] | |
| print(control_new) | |
| print(control_char) | |
| json_object={} | |
| sen_list=[] | |
| noun_list={} | |
| noun_box=[] | |
| blob = TextBlob(text) | |
| for sentence in blob.sentences: | |
| sen_list.append(str(sentence)) | |
| key_cnt=len(sen_list) | |
| cnt=0 | |
| go=True | |
| a="Z" | |
| if go: | |
| for ea in range(10): | |
| if go: | |
| for b in range(50): | |
| if go: | |
| for c in range(50): | |
| if go: | |
| for d in range(50): | |
| if go: | |
| blob_n = TextBlob(sen_list[cnt]) | |
| noun_p=blob_n.noun_phrases | |
| noun_box=[] | |
| for ea in blob_n.parse().split(" "): | |
| n=ea.split("/") | |
| if n[1] == "NN": | |
| noun_box.append(n[0]) | |
| json_object[f'{a}{cont_list[b]}{cont_list[c]}{cont_list[d]}']={'sentence':sen_list[cnt],'noun_phrase':noun_p,'nouns':noun_box} | |
| for noun in noun_p: | |
| if noun in list(noun_list.keys()): | |
| noun_list[str(noun)].append(f'{a}{cont_list[b]}{cont_list[c]}{cont_list[d]}') | |
| else: | |
| noun_list[str(noun)]=[f'{a}{cont_list[b]}{cont_list[c]}{cont_list[d]}'] | |
| for nn in noun_box: | |
| if nn in list(noun_list.keys()): | |
| noun_list[str(nn)].append(f'{a}{cont_list[b]}{cont_list[c]}{cont_list[d]}') | |
| else: | |
| noun_list[str(nn)]=[f'{a}{cont_list[b]}{cont_list[c]}{cont_list[d]}'] | |
| if json_object[f'{a}{cont_list[b]}{cont_list[c]}{cont_list[d]}']=='ZNNN': | |
| a="Y" | |
| b=0 | |
| c=0 | |
| d=0 | |
| if json_object[f'{a}{cont_list[b]}{cont_list[c]}{cont_list[d]}']=='YNNN': | |
| a="X" | |
| b=0 | |
| c=0 | |
| d=0 | |
| if cnt == key_cnt-1: | |
| print('done') | |
| go=False | |
| print(list(json_object.keys())[-1]) | |
| else: | |
| cnt+=1 | |
| return json_object,noun_list | |
| def find_query(query,sen,nouns): | |
| blob_f = TextBlob(query) | |
| noun_box={} | |
| noun_list=[] | |
| sen_box=[] | |
| for ea in blob_f.parse().split(" "): | |
| n=ea.split("/") | |
| if n[1] == "NN": | |
| noun_list.append(n[0]) | |
| nouns_l=list(nouns.keys()) | |
| for nn in nouns_l: | |
| for nl in noun_list: | |
| if nl in nn: | |
| if nl in noun_box: | |
| for ea_n in nouns[nn]: | |
| noun_box[str(nl)].append(ea_n) | |
| else: | |
| noun_box[str(nl)]=[] | |
| for ea_n in nouns[nn]: | |
| noun_box[str(nl)].append(ea_n) | |
| for ea in noun_box.values(): | |
| for vals in ea: | |
| sen_box.append(sen[vals]['sentence']) | |
| return noun_box,sen_box | |
| with gr.Blocks() as app: | |
| inp = gr.Textbox(label="Paste Text",value=text,lines=10) | |
| btn = gr.Button("Load Document") | |
| with gr.Row(): | |
| query=gr.Textbox(label="Search query") | |
| search_btn=gr.Button("Search") | |
| steps=gr.Number(value=1) | |
| out_box=gr.Textbox(label="Results") | |
| sen_box=gr.Textbox(label="Sentences") | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| sen=gr.JSON(label="Sentences") | |
| with gr.Column(scale=1): | |
| nouns=gr.JSON(label="Nouns") | |
| search_btn.click(find_query,[query,sen,nouns],[out_box,sen_box]) | |
| btn.click(get_nouns,[inp,steps],[sen,nouns]) | |
| app.launch() | |