Spaces:
Runtime error
Runtime error
File size: 4,191 Bytes
24ff201 |
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 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
import sys
import time
import warnings
from pathlib import Path
# 配置hugface环境
from huggingface_hub import hf_hub_download
import gradio as gr
import os
import glob
import json
# os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
# torch.set_float32_matmul_precision("high")
def instruct_generate(
img_path: str = " ",
prompt: str = "What food do lamas eat?",
input: str = "",
max_new_tokens: int = 100,
top_k: int = 200,
temperature: float = 0.8,
) -> None:
"""Generates a response based on a given instruction and an optional input.
This script will only work with checkpoints from the instruction-tuned LLaMA-Adapter model.
See `finetune_adapter.py`.
Args:
prompt: The prompt/instruction (Alpaca style).
adapter_path: Path to the checkpoint with trained adapter weights, which are the output of
`finetune_adapter.py`.
input: Optional input (Alpaca style).
pretrained_path: The path to the checkpoint with pretrained LLaMA weights.
tokenizer_path: The tokenizer path to load.
quantize: Whether to quantize the model and using which method:
``"llm.int8"``: LLM.int8() mode,
``"gptq.int4"``: GPTQ 4-bit mode.
max_new_tokens: The number of generation steps to take.
top_k: The number of top most probable tokens to consider in the sampling process.
temperature: A value controlling the randomness of the sampling process. Higher values result in more random
"""
output = [prompt, input, max_new_tokens, top_k, temperature]
print(output)
return output
# 配置具体参数
example_path = "example.json"
# 1024如果不够, 调整为512
max_seq_len = 1024
max_batch_size = 1
with open(example_path, 'r') as f:
content = f.read()
example_dict = json.loads(content)
def create_instruct_demo():
with gr.Blocks() as instruct_demo:
with gr.Row():
with gr.Column():
scene_img = gr.Image(label='Scene', type='filepath')
object_list = gr.Textbox(
lines=2, label="Input")
instruction = gr.Textbox(
lines=2, label="Instruction")
max_len = gr.Slider(minimum=1, maximum=512,
value=128, label="Max length")
with gr.Accordion(label='Advanced options', open=False):
temp = gr.Slider(minimum=0, maximum=1,
value=0.8, label="Temperature")
top_k = gr.Slider(minimum=100, maximum=300,
value=200, label="Top k")
run_botton = gr.Button("Run")
with gr.Column():
outputs = gr.Textbox(lines=10, label="Output")
inputs = [instruction, object_list, max_len, top_k, temp]
# 接下来设定具体的example格式
examples_img_list = glob.glob("caption_demo/*.png")
examples = []
for example_img_one in examples_img_list:
scene_name = os.path.basename(example_img_one).split(".")[0]
example_object_list = example_dict[scene_name]["input"]
example_instruction = example_dict[scene_name]["instruction"]
example_one = [example_img_one, example_object_list, example_instruction, 512, 0.8, 200]
examples.append(example_one)
gr.Examples(
examples=examples,
inputs=inputs,
outputs=outputs,
fn=instruct_generate,
cache_examples=os.getenv('SYSTEM') == 'spaces'
)
run_botton.click(fn=instruct_generate, inputs=inputs, outputs=outputs)
return instruct_demo
# Please refer to our [arXiv paper](https://arxiv.org/abs/2303.16199) and [github](https://github.com/ZrrSkywalker/LLaMA-Adapter) for more details.
description = """
# TaPA
The official demo for **Embodied Task Planning with Large Language Models**.
"""
with gr.Blocks(css='style.css') as demo:
gr.Markdown(description)
with gr.TabItem("Instruction-Following"):
create_instruct_demo()
demo.queue(api_open=True, concurrency_count=1).launch()
|