Update app.py

app.py

@@ -7,26 +7,6 @@ control_json={'control':'0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRS
 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 get_sen_list(text):
     sen_list=[]
     blob = TextBlob(text)
@@ -42,7 +22,7 @@ def proc_sen(sen_list,cnt):
         n=ea.split("/")
         if n[1] == "NN":
             noun_box1.append(n[0])
-    json_object={'sentence':sen_list[cnt],'noun_phrase':noun_p,'nouns':noun_box1}    
+    json_object={'sen_num':cnt,'sentence':sen_list[cnt],'noun_phrase':noun_p,'nouns':noun_box1}    
     return json_object
 
 def proc_nouns(sen_list):
@@ -158,7 +138,7 @@ def find_query(query,sen,nouns):
                         noun_box[str(nl)].append(ea_n)
     for ea in noun_box.values():
         for vals in ea:
-            sen_box.append(sen[vals]['sentence'])
+            sen_box.append({'sen_num':sen[vals]['sen_num'],'sentence':sen[vals]['sentence']})
     return noun_box,sen_box
 
 with gr.Blocks() as app:
@@ -168,7 +148,7 @@ with gr.Blocks() as app:
         query=gr.Textbox(label="Search query")
         search_btn=gr.Button("Search")
     out_box=gr.Textbox(label="Results")
-    sen_box=gr.Textbox(label="Sentences")
+    sen_box=gr.JSON(label="Sentences")
     with gr.Row():
         with gr.Column(scale=2):
             sen=gr.JSON(label="Sentences")