Create app.py

app.py

@@ -0,0 +1,104 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
+modelname="EleutherAI/gpt-neo-2.7B"
+config = AutoConfig.from_pretrained(modelname)
+tokenizer = AutoTokenizer.from_pretrained(modelname)
+model = AutoModelForCausalLM.from_pretrained(modelname,config=config).to("cuda")
+
+
+def botsay(user_input):
+  prompt = "This is a conversation between Human and AI bot. AI's name is ThatGPT."  
+  new_token_id=None
+  gen_tokens=""
+  new_token=""
+  j =6
+  length=0
+  limit = 128
+  thatid=5562
+  cont = True
+  last_apppended = False
+  cnt=0
+  disable_repeat_length= 5
+  disable_repeat_count = 2
+  tokens=[]
+  while(cont):
+    cnt+=1
+    prob = 1.0
+    input_ids=tokenizer(prompt+user_input+"\nAI:"+gen_tokens,return_tensors="pt").input_ids
+    length=len(input_ids)
+    if length >limit:
+      gen_tokens="⚠️sorry length limit. please reload the browser."
+      return gen_tokens
+    outs=model(input_ids=input_ids.to("cuda"))
+    topk = torch.topk(outs.logits.squeeze()[-1,:],k=j+1).indices
+    if new_token =="that":
+      that_id = 326
+    elif new_token ==" that":
+      that_id = -1
+    elif new_token[-1:] ==" ":
+      that_id = 5562
+    else:
+      that_id = 326
+
+    if ("thatGPT" in gen_tokens[-12:]):
+      that_id = -1
+    if last_apppended:
+      that_id = -1
+    if that_id in topk:
+      new_token_id = that_id
+    else:
+      new_token_id = torch.argmax(outs.logits.squeeze()[-1,:])
+      new_token=tokenizer.decode(new_token_id)
+    new_token=tokenizer.decode(new_token_id)
+    prev_tokens=gen_tokens
+    gen_tokens+=new_token
+    if (cnt>10) and (disable_repeat_count<gen_tokens.count(gen_tokens[-disable_repeat_length:])):
+      gen_tokens=prev_tokens
+      new_token = tokenizer.decode(topk[torch.randint(5, (1,1)).item()])
+      gen_tokens+=new_token
+
+    if new_token_id==50256 or new_token_id==198 or new_token=="<|endoftext|>":
+      if ("that" not in gen_tokens):
+        gen_tokens = gen_tokens.replace("\n","").replace(".","")
+        gen_tokens += " that"
+      else:
+        cont = False
+  return gen_tokens.replace("<br>","").replace("AI:","").replace("\xa0","")
+
+
+    
+
+import gradio as gr
+def add_text(history, text):
+    history = history + [(text, None)]
+    return history, ""
+
+
+def bot(history):
+    serial_history=""
+    for h in history:
+      serial_history+="\nHuman:"+h[0]
+      if h[1]==None:
+        break
+      serial_history+="\nAI:"+h[1].replace("<br>","")
+
+    response = botsay(serial_history)
+    history[-1][1] = response
+    serial_history+="\nAI:"+response
+    return history
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot([], elem_id="chatbot").style(height=750)
+
+    with gr.Row():
+        with gr.Column(scale=0.85):
+            txt = gr.Textbox(
+                show_label=False,
+                placeholder="input text and press enter",
+            ).style(container=False)
+        
+    txt.submit(add_text, [chatbot, txt], [chatbot, txt]).then(
+        bot, chatbot, chatbot
+    )
+
+demo.launch(debug=True,share=True)