mohhand/camel

1import gradio as gr2from transformers import AutoModelForCausalLM, AutoTokenizer3import torch4 5# Load the model and tokenizer6model_name = "bragour/Camel-7b-chat-awq"7tokenizer = AutoTokenizer.from_pretrained(model_name)8model = AutoModelForCausalLM.from_pretrained(model_name)9 10# Function to generate responses11def generate_response(user_input, chat_history=[]):12    new_user_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')13    bot_input_ids = torch.cat([torch.LongTensor(chat_history), new_user_input_ids], dim=-1) if chat_history else new_user_input_ids14    15    chat_history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)16    17    response = tokenizer.decode(chat_history[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)18    return response, chat_history.tolist()19 20# Gradio interface21def chat(user_input, history=[]):22    response, history = generate_response(user_input, history)23    return response, history24 25iface = gr.Interface(26    fn=chat, 27    inputs=[gr.inputs.Textbox(lines=7, label="Input Text"), gr.inputs.State()],28    outputs=[gr.outputs.Textbox(label="Response"), gr.outputs.State()],29    title="ChatBot",30    description="A simple chatbot using a pre-trained Camel-7b-chat model."31)32 33iface.launch()34