Spaces:
Runtime error
Runtime error
| import transformers | |
| import gradio as gr | |
| import torch | |
| from transformers import GPT2LMHeadModel, GPT2Tokenizer | |
| tokenizer = GPT2Tokenizer.from_pretrained("tareknaous/dialogpt-daily-dialog") | |
| model = GPT2LMHeadModel.from_pretrained("tareknaous/dialogpt-daily-dialog") | |
| model.eval() | |
| def chat(message, history): | |
| history = history or [] | |
| new_user_input_ids = tokenizer.encode(message + tokenizer.eos_token, return_tensors='pt') | |
| if len(history) > 0 and len(history) < 2: | |
| for i in range(0,len(history)): | |
| encoded_message = tokenizer.encode(history[i][0] + tokenizer.eos_token, return_tensors='pt') | |
| encoded_response = tokenizer.encode(history[i][1] + tokenizer.eos_token, return_tensors='pt') | |
| if i == 0: | |
| chat_history_ids = encoded_message | |
| chat_history_ids = torch.cat([chat_history_ids,encoded_response], dim=-1) | |
| else: | |
| chat_history_ids = torch.cat([chat_history_ids,encoded_message], dim=-1) | |
| chat_history_ids = torch.cat([chat_history_ids,encoded_response], dim=-1) | |
| bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) | |
| elif len(history) >= 2: | |
| for i in range(len(history)-1, len(history)): | |
| encoded_message = tokenizer.encode(history[i][0] + tokenizer.eos_token, return_tensors='pt') | |
| encoded_response = tokenizer.encode(history[i][1] + tokenizer.eos_token, return_tensors='pt') | |
| if i == (len(history)-1): | |
| chat_history_ids = encoded_message | |
| chat_history_ids = torch.cat([chat_history_ids,encoded_response], dim=-1) | |
| else: | |
| chat_history_ids = torch.cat([chat_history_ids,encoded_message], dim=-1) | |
| chat_history_ids = torch.cat([chat_history_ids,encoded_response], dim=-1) | |
| bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) | |
| elif len(history) == 0: | |
| bot_input_ids = new_user_input_ids | |
| chat_history_ids = model.generate(bot_input_ids, max_length=1000, do_sample=True, top_p=0.9, temperature=0.8, pad_token_id=tokenizer.eos_token_id) | |
| response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True) | |
| history.append((message, response)) | |
| return history, history | |
| title = "DialoGPT fine-tuned on DailyDialog" | |
| description = "Gradio demo for open-domain dialog using DialoGPT. Model was fine-tuned on the DailyDialog multi-turn dataset" | |
| iface = gr.Interface( | |
| chat, | |
| ["text", "state"], | |
| ["chatbot", "state"], | |
| allow_screenshot=False, | |
| allow_flagging="never", | |
| title=title, | |
| description=description | |
| ) | |
| iface.launch(debug=True) |