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import torch
import gradio as gr
from transformers import AutoModel, pipeline, AutoTokenizer
import spaces
import subprocess
# from issue: https://discuss.huggingface.co/t/how-to-install-flash-attention-on-hf-gradio-space/70698/2
# InternVL2 needs flash_attn
subprocess.run(
"pip install flash-attn --no-build-isolation",
env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
shell=True,
)
try:
model_name = "OpenGVLab/InternVL2-8B"
# model: <class 'transformers_modules.OpenGVLab.InternVL2-8B.0e6d592d957d9739b6df0f4b90be4cb0826756b9.modeling_internvl_chat.InternVLChatModel'>
model = (
AutoModel.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
# low_cpu_mem_usage=True,
trust_remote_code=True,
)
.cuda()
.eval()
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
# pipeline: <class 'transformers.pipelines.visual_question_answering.VisualQuestionAnsweringPipeline'>
inference = pipeline(
task="visual-question-answering", model=model, tokenizer=tokenizer
)
except Exception as error:
raise gr.Error("👌" + str(error), duration=30)
@spaces.GPU
def predict(input_img, questions):
try:
gr.Info("pipeline: " + str(type(inference)))
gr.Info("model: " + str(type(model)))
predictions = inference(question=questions, image=input_img)
return str(predictions)
except Exception as e:
error_message = "❌" + str(e)
raise gr.Error(error_message, duration=25)
gradio_app = gr.Interface(
predict,
inputs=[
gr.Image(label="Select A Image", sources=["upload", "webcam"], type="pil"),
"text",
],
outputs="text",
title='ask me anything',
)
if __name__ == "__main__":
gradio_app.launch(show_error=True, debug=True)
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