app.py

import os
import json
import subprocess
from threading import Thread

import torch
import spaces
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextIteratorStreamer

subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

MODEL_ID = "infly/OpenCoder-8B-Instruct"
CHAT_TEMPLATE =  "ChatML"
MODEL_NAME = MODEL_ID.split("/")[-1]
CONTEXT_LENGTH = 1300
#EMOJI = os.environ.get("EMOJI")
DESCRIPTION = "Infly OpenCoder-8B-Instruct"


@spaces.GPU()
def predict(message, history, system_prompt, temperature, max_new_tokens, top_k, repetition_penalty, top_p):
    # Format history with a given chat template
    if CHAT_TEMPLATE == "ChatML":
        stop_tokens = ["<|endoftext|>", "<|im_end|>", "<|end_of_text|>", "<|eot_id|>", "assistant"]
        instruction = '<|im_start|>system\n' + system_prompt + '\n<|im_end|>\n'
        for human, assistant in history:
            instruction += '<|im_start|>user\n' + human + '\n<|im_end|>\n<|im_start|>assistant\n' + assistant
        instruction += '\n<|im_start|>user\n' + message + '\n<|im_end|>\n<|im_start|>assistant\n'
    elif CHAT_TEMPLATE == "Mistral Instruct":
        stop_tokens = ["</s>", "[INST]", "[INST] ", "<s>", "[/INST]", "[/INST] "]
        instruction = '<s>[INST] ' + system_prompt
        for human, assistant in history:
            instruction += human + ' [/INST] ' + assistant + '</s>[INST]'
        instruction += ' ' + message + ' [/INST]'
    else:
        raise Exception("Incorrect chat template, select 'ChatML' or 'Mistral Instruct'")
    print(instruction)
    
    streamer = TextIteratorStreamer(tokenizer, timeout=90.0, skip_prompt=True, skip_special_tokens=True)
    enc = tokenizer([instruction], return_tensors="pt", padding=True, truncation=True, max_length=CONTEXT_LENGTH)
    input_ids, attention_mask = enc.input_ids, enc.attention_mask

    if input_ids.shape[1] > CONTEXT_LENGTH:
        input_ids = input_ids[:, -CONTEXT_LENGTH:]

    generate_kwargs = dict(
        {"input_ids": input_ids.to(device), "attention_mask": attention_mask.to(device)},
        streamer=streamer,
        do_sample=True,
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_k=top_k,
        repetition_penalty=repetition_penalty,
        top_p=top_p
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()
    outputs = []
    for new_token in streamer:
        outputs.append(new_token)
        if new_token in stop_tokens:
            break
        yield "".join(outputs)



# Load model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    device_map="auto",
    trust_remote_code=True
)

css = """
.message-row {
    justify-content: space-evenly !important;
}
.message-bubble-border {
    border-radius: 6px !important;
}
.message-buttons-bot, .message-buttons-user {
    right: 10px !important;
    left: auto !important;
    bottom: 2px !important;
}
.dark.message-bubble-border {
    border-color: #15172c !important;
}
.dark.user {
    background: #10132c !important;
}
.dark.assistant.dark, .dark.pending.dark {
    background: #020417 !important;
}
"""

# Create Gradio interface
gr.ChatInterface(
    predict,
    title="Infly " + MODEL_NAME,
    description=DESCRIPTION,
    additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False),
    additional_inputs=[
        gr.Textbox("Perform the task to the best of your ability.", label="System prompt"),
        gr.Slider(0, 1, 0.8, label="Temperature"),
        gr.Slider(128, 4096, 512, label="Max new tokens"),
        gr.Slider(1, 80, 40, label="Top K sampling"),
        gr.Slider(0, 2, 1.1, label="Repetition penalty"),
        gr.Slider(0, 1, 0.95, label="Top P sampling"),
    ],
    theme = gr.themes.Ocean(
    secondary_hue="emerald",
    ),
    css=css,
    #retry_btn="Retry",
    #undo_btn="Undo",
    #clear_btn="Clear",
    #submit_btn="Send",
    chatbot=gr.Chatbot(
        scale=1,
        show_copy_button=True
    )
).queue().launch()