Create app.py

app.py

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+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)