Update app.py

app.py

@@ -18,6 +18,34 @@ SAMPLE_IDX = []
 RANDOM_POSITION = [(145 + 200 * i + 400 * (i//2), j * 110 + 900) for i in range(4) for j in range(4) ]
 CURRENT_POSITION = []
 
+
+import os 
+import numpy as np
+import torch
+
+
+
+###########
+
+from thinkgpt.llm import ThinkGPT
+
+os.environ['OPENAI_API_KEY']='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'
+llm = ThinkGPT(model_name="gpt-3.5-turbo")
+
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+title = """<h1 align="center"> 🏛️ Courtroom with AI juries</h1>"""
+description = "Building open-domain chatbots is a challenging area for machine learning research."
+examples = [["How are you?"]]
+os.environ['OPENAI_API_KEY']='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'
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
+
+###########
+
 print (NUM_THREADS)
 
 def exception_handler(exception_type, exception, traceback):
@@ -128,7 +156,40 @@ def parse_codeblock(text):
 #         print (f'error found: {e}')
 #     yield [(parse_codeblock(history[i]), parse_codeblock(history[i + 1])) for i in range(0, len(history) - 1, 2) ], history, chat_counter, response, gr.update(interactive=True), gr.update(interactive=True)
 #     print(json.dumps({"chat_counter": chat_counter, "payload": payload, "partial_words": partial_words, "token_counter": token_counter, "counter": counter}))
-                   
+
+
+def predict(input, history=[]):
+    # tokenize the new input sentence
+    new_user_input_ids = tokenizer.encode(
+        input + tokenizer.eos_token, return_tensors="pt"
+    )
+
+    llm.memorize(memorize_list)
+    prediction = llm.predict(question, remember=llm.remember('철수', limit = 30))
+    print(prediction)
+    print('-'*100)
+
+
+    # append the new user input tokens to the chat history
+    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
+
+    # generate a response
+    history = model.generate(
+        bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id
+    ).tolist()
+
+    # convert the tokens to text, and then split the responses into lines
+    response = tokenizer.decode(history[0]).split("<|endoftext|>")
+    # print('decoded_response-->>'+str(response))
+    response = [
+        (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
+    ]  # convert to tuples of list
+    # print('response-->>'+str(response))
+    
+    return response, prediction
+
+    
+    
 
 def reset_textbox():
     return gr.update(value='', interactive=False), gr.update(interactive=False)