Update app.py

app.py

@@ -2,9 +2,86 @@ from textblob import TextBlob
 import gradio as gr
 import os 
 os.system("python -m textblob.download_corpora")
-string_json={'control':'0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMN'}
+control_json={'control':'0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ','char':'','leng':62}
 cont_list=list(string_json['control'])
-def get_nouns(text):
+
+
+
+
+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.
+"""
+
+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:-1])
+    char_len=len(control_char)
+    val_len=len(control_val)
+    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 i,ea in enumerate(range(steps)):
+            
+            if go:     
+                for ii,sent in enumerate(sen_list):
+                    
+                    #for iii in  
+                    noun_list[f'{control_val[i]}{control_char[ii]}']=sent
+                
+                if cnt == key_cnt-1:
+                    print('done')
+                    go=False
+                    print(list(json_object.keys())[-1])
+                else:
+                    cnt+=1
+
+
+
+
+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:-1]
+    print(control_new)
+    print(control_char)
     json_object={}
     sen_list=[]
     noun_list={}