app.py

#!/usr/bin/env python

import os
from threading import Thread
from typing import Iterator

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

DESCRIPTION = "# Mistral-7B"

if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"

MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 256
MAX_INPUT_TOKEN_LENGTH = 4096

if torch.cuda.is_available():
    model_id = "codys12/MergeLlama-7b"
    model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.float16, device_map=0, cache_dir="/data")
    tokenizer = AutoTokenizer.from_pretrained("codellama/CodeLlama-7b-hf", trust_remote_code=True)
    tokenizer.pad_token = tokenizer.eos_token
    tokenizer.padding_side = "right"


@spaces.GPU
def generate(
    message: str,
    chat_history: list[tuple[str, str]],
    max_new_tokens: int = 1024,
    #temperature: float = 0.6,
    #top_p: float = 0.9,
    #top_k: int = 50,
    #repetition_penalty: float = 1.2,
) -> Iterator[str]:
    conversation = []
    current_input = ""
    for user, assistant in chat_history:
        current_input += user
        current_input += assistant

    history = current_input
    current_input += message
    
    device = "cuda:0"
    print(current_input)
    input_ids = tokenizer(current_input, return_tensors="pt").input_ids.to(device)
    
    outputs = model.generate(input_ids, max_new_tokens=100)

    print(tokenizer.decode(outputs[0], skip_special_tokens=False))


    if len(input_ids) > MAX_INPUT_TOKEN_LENGTH:
        input_ids = input_ids[-MAX_INPUT_TOKEN_LENGTH:]
        gr.Warning("Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")

    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        {"input_ids": input_ids},
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        #do_sample=True,
        #top_p=top_p,
        #top_k=top_k,
        #temperature=temperature,
        #num_beams=1,
        #repetition_penalty=repetition_penalty,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    outputs = []
    for text in streamer:
        outputs.append(text)
        yield "".join(outputs)


chat_interface = gr.ChatInterface(
    fn=generate,
    additional_inputs=[
        gr.Slider(
            label="Max new tokens",
            minimum=1,
            maximum=MAX_MAX_NEW_TOKENS,
            step=1,
            value=DEFAULT_MAX_NEW_TOKENS,
        ),
        # gr.Slider(
        #     label="Temperature",
        #     minimum=0.1,
        #     maximum=4.0,
        #     step=0.1,
        #     value=0.6,
        # ),
        # gr.Slider(
        #     label="Top-p (nucleus sampling)",
        #     minimum=0.05,
        #     maximum=1.0,
        #     step=0.05,
        #     value=0.9,
        # ),
        # gr.Slider(
        #     label="Top-k",
        #     minimum=1,
        #     maximum=1000,
        #     step=1,
        #     value=50,
        # ),
        # gr.Slider(
        #     label="Repetition penalty",
        #     minimum=0.1,
        #     maximum=2.0,
        #     step=0.05,
        #     value=1.2,
        # ),
    ],
    stop_btn=None,
    examples=[
        ["<<<<<<<\nimport org.apache.flink.api.java.tuple.Tuple2;\n\n=======\n\nimport org.apache.commons.collections.MapUtils;\nimport org.apache.flink.api.common.functions.RuntimeContext;\n\n>>>>>>>"],
        ["<<<<<<<\n  // Simple check for whether our target app uses Recoil\n  if (window[`$recoilDebugStates`]) {\n    isRecoil = true;\n  }\n\n=======\n\n    if (\n      memoizedState &&\n      (tag === 0 || tag === 1 || tag === 2 || tag === 10) &&\n      isRecoil === true\n    ) {\n      if (memoizedState.queue) {\n        // Hooks states are stored as a linked list using memoizedState.next,\n        // so we must traverse through the list and get the states.\n        // We then store them along with the corresponding memoizedState.queue,\n        // which includes the dispatch() function we use to change their state.\n        const hooksStates = traverseRecoilHooks(memoizedState);\n        hooksStates.forEach((state, i) => {\n\n          hooksIndex = componentActionsRecord.saveNew(\n            state.state,\n            state.component\n          );\n          componentData.hooksIndex = hooksIndex;\n          if (newState && newState.hooksState) {\n            newState.push(state.state);\n          } else if (newState) {\n            newState = [state.state];\n          } else {\n            newState.push(state.state);\n          }\n          componentFound = true;\n        });\n      }\n    }\n\n>>>>>>>"],
        ["Explain the plot of Cinderella in a sentence."],
        ["How many hours does it take a man to eat a Helicopter?"],
        ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
    ],
)

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(
        value="Duplicate Space for private use",
        elem_id="duplicate-button",
        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
    )
    chat_interface.render()

if __name__ == "__main__":
    demo.queue(max_size=20).launch()