| import gradio as gr |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import torch |
|
|
| |
| model_name = "bragour/Camel-7b-chat-awq" |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModelForCausalLM.from_pretrained(model_name) |
|
|
| |
| def generate_response(user_input, chat_history=[]): |
| new_user_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt') |
| bot_input_ids = torch.cat([torch.LongTensor(chat_history), new_user_input_ids], dim=-1) if chat_history else new_user_input_ids |
| |
| chat_history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id) |
| |
| response = tokenizer.decode(chat_history[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True) |
| return response, chat_history.tolist() |
|
|
| |
| def chat(user_input, history=[]): |
| response, history = generate_response(user_input, history) |
| return response, history |
|
|
| iface = gr.Interface( |
| fn=chat, |
| inputs=[gr.inputs.Textbox(lines=7, label="Input Text"), gr.inputs.State()], |
| outputs=[gr.outputs.Textbox(label="Response"), gr.outputs.State()], |
| title="ChatBot", |
| description="A simple chatbot using a pre-trained Camel-7b-chat model." |
| ) |
|
|
| iface.launch() |
|
|
