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#!/usr/bin/env python
# encoding: utf-8
import spaces
import gradio as gr
from PIL import Image
import traceback
import re
import torch
import argparse
import numpy as np
from transformers import AutoModel, AutoTokenizer
# README, How to run demo on different devices
# For Nvidia GPUs.
# python web_demo_2.5.py --device cuda
# For Mac with MPS (Apple silicon or AMD GPUs).
# PYTORCH_ENABLE_MPS_FALLBACK=1 python web_demo_2.5.py --device mps
# Argparser
# test.py
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer
import bitsandbytes as bnb
import accelerate
model = AutoModel.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5-int4', trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5-int4', trust_remote_code=True)
model.eval()
image = Image.open('xx.jpg').convert('RGB')
question = 'What is in the image?'
msgs = [{'role': 'user', 'content': question}]
res = model.chat(
image=image,
msgs=msgs,
tokenizer=tokenizer,
sampling=True, # if sampling=False, beam_search will be used by default
temperature=0.7,
# system_prompt='' # pass system_prompt if needed
)
print(res)
## if you want to use streaming, please make sure sampling=True and stream=True
## the model.chat will return a generator
res = model.chat(
image=image,
msgs=msgs,
tokenizer=tokenizer,
sampling=True,
temperature=0.7,
stream=True
)
generated_text = ""
for new_text in res:
generated_text += new_text
print(new_text, flush=True, end='')
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