app.py

import os
from PIL import Image
import numpy as np
import torch as T
import transformers

# Assuming necessary model and tokenizer are already set up
PATH_LLAVA = '_ckpt/LLaVA-7B-v1'
tokenizer = transformers.AutoTokenizer.from_pretrained(PATH_LLAVA)
model = transformers.AutoModelForCausalLM.from_pretrained(PATH_LLAVA).cuda()

def remove_alter(s):  # Simplify expressive instruction
    if 'ASSISTANT:' in s: s = s[s.index('ASSISTANT:')+10:].strip()
    if '</s>' in s: s = s[:s.index('</s>')].strip()
    if 'alternative' in s.lower(): s = s[:s.lower().index('alternative')]
    if '[IMG0]' in s: s = s[:s.index('[IMG0]')]
    s = '.'.join([s.strip() for s in s.split('.')[:2]])
    if s[-1]!='.': s += '.'
    return s.strip()

def load_image_and_generate_instruction(image_path):
    # Load the image
    img = Image.open(image_path)
    img.show()

    # Example: Generate a simple instruction based on the image
    # This is a placeholder. You would replace this with your own method
    # to analyze the image and generate a textual description or instruction.
    instruction = "Describe what to do with this image."

    # Simplify and generate expressive instruction
    expressive_instruction = remove_alter(instruction)
    print("Expressive Instruction:", expressive_instruction)

# Example usage
image_path = './path/to/your/image.jpg'  # Update this path to your image
load_image_and_generate_instruction(image_path)