Update app.py

app.py

@@ -16,5 +16,26 @@ The bot is trained on Rick and Morty dialogues Kaggle Dataset using DialoGPT.
 """
 article = "<p style='text-align: center'><a href='https://medium.com/geekculture/discord-bot-using-dailogpt-and-huggingface-api-c71983422701' target='_blank'>Complete Tutorial</a></p><p style='text-align: center'><a href='https://dagshub.com/kingabzpro/DailoGPT-RickBot' target='_blank'>Project is Available at DAGsHub</a></p></center></p>"
 
-gr.Interface.load("huggingface/kingabzpro/DialoGPT-small-Rick-Bot","textbox","chatbot",theme ="grass", title = title, flagging_callback=hf_writer,description = description, article = article).launch(enable_queue=True)  # customizes the input component
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+
+def predict(input, history=[]):
+    # tokenize the new input sentence
+    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
+
+    # 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=1000, 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]).replace("<|endoftext|>", "\n")
+    
+    return response, history
+
+gr.Interface(predict,"textbox","chatbot",theme ="grass", title = title, flagging_callback=hf_writer,description = description, article = article).launch(enable_queue=True)  # customizes the input component