File size: 1,303 Bytes
cb2d051 8a0c957 4ab6b9d 8a0c957 cb2d051 8a0c957 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load the model and tokenizer
model_name = "bragour/Camel-7b-chat-awq"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Function to generate responses
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()
# Gradio interface
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()
|