Spaces:
Sleeping
Sleeping
File size: 7,559 Bytes
32887b7 932d067 422a1d6 32887b7 422a1d6 32887b7 932d067 32887b7 971be40 32887b7 932d067 32887b7 932d067 32887b7 932d067 32887b7 422a1d6 932d067 32887b7 932d067 32887b7 932d067 32887b7 932d067 32887b7 932d067 422a1d6 971be40 932d067 971be40 932d067 971be40 932d067 971be40 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 |
import sys
import logging
import os
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
handlers=[logging.StreamHandler(sys.stdout)]
)
logger = logging.getLogger(__name__)
# First try to import from llava
try:
from llava.model.builder import load_pretrained_model
from llava.mm_utils import process_images, tokenizer_image_token
logger.info("Successfully imported llava modules")
except ImportError as e:
logger.error(f"Failed to import llava modules: {e}")
sys.exit(1)
# Then import other dependencies
try:
from transformers import AutoTokenizer, AutoConfig
import torch
import requests
from PIL import Image
from io import BytesIO
logger.info("Successfully imported other required modules")
except ImportError as e:
logger.error(f"Failed to import dependency: {e}")
sys.exit(1)
class LLaVAHelper:
def __init__(self, model_name="llava-hf/llava-1.5-7b-hf"):
"""
Initialize the LLaVA model for image-text processing
"""
logger.info(f"Initializing LLaVAHelper with model: {model_name}")
# Create cache directory if it doesn't exist
os.makedirs("./model_cache", exist_ok=True)
logger.info("Created model cache directory")
# Try loading just the config to ensure the model is valid
try:
AutoConfig.from_pretrained(model_name)
logger.info(f"Successfully loaded config for {model_name}")
except Exception as e:
logger.warning(f"Error loading model config: {e}")
# Try a different model version as fallback
model_name = "llava-hf/llava-1.5-13b-hf"
logger.info(f"Trying alternative model: {model_name}")
try:
# Use specific tokenizer class to avoid issues
logger.info("Loading tokenizer...")
self.tokenizer = AutoTokenizer.from_pretrained(
model_name,
cache_dir="./model_cache",
use_fast=False, # Use the Python implementation instead of the Rust one
trust_remote_code=True
)
logger.info("Tokenizer loaded successfully")
# Inspect the load_pretrained_model function to understand its parameters
import inspect
logger.info(f"load_pretrained_model signature: {inspect.signature(load_pretrained_model)}")
# Try loading with different parameter combinations
logger.info("Loading model...")
try:
# First attempt - standard parameter order
self.model, self.image_processor, _ = load_pretrained_model(
model_path=model_name,
model_base=None,
cache_dir="./model_cache",
)
except Exception as e1:
logger.warning(f"First attempt to load model failed: {e1}")
try:
# Second attempt - try with model_name parameter
self.model, self.image_processor, _ = load_pretrained_model(
model_name=model_name,
model_path=model_name,
model_base=None,
cache_dir="./model_cache",
)
except Exception as e2:
logger.warning(f"Second attempt to load model failed: {e2}")
# Third attempt - minimal parameters
self.model, self.image_processor, _ = load_pretrained_model(
model_name,
None,
"./model_cache",
)
logger.info("Model loaded successfully")
self.model.eval()
# Move model to appropriate device
self.device = "cuda" if torch.cuda.is_available() else "cpu"
logger.info(f"Using device: {self.device}")
if self.device == "cpu":
# If using CPU, make sure model is in the right place
self.model = self.model.to(self.device)
logger.info(f"Model successfully loaded on {self.device}")
except Exception as e:
logger.error(f"Detailed initialization error: {e}")
logger.error("Stack trace:", exc_info=True)
raise
self.model.eval()
# Move model to appropriate device
self.device = "cuda" if torch.cuda.is_available() else "cpu"
if self.device == "cpu":
# If using CPU, make sure model is in the right place
self.model = self.model.to(self.device)
print(f"Model loaded on {self.device}")
except Exception as e:
print(f"Detailed initialization error: {e}")
raise
def generate_answer(self, image, question):
"""
Generate a response to a question about an image
Args:
image: PIL Image or path to image
question: String question about the image
Returns:
String response from the model
"""
try:
# Handle image input (either PIL Image or path/URL)
if isinstance(image, str):
if image.startswith(('http://', 'https://')):
response = requests.get(image)
image = Image.open(BytesIO(response.content))
else:
image = Image.open(image)
# Preprocess image
image_tensor = process_images(
[image],
self.image_processor,
self.model.config
)[0].unsqueeze(0).to(self.device)
# Format prompt with question
prompt = f"###Human: <image>\n{question}\n###Assistant:"
# Tokenize prompt
input_ids = tokenizer_image_token(
prompt,
self.tokenizer,
return_tensors="pt"
).to(self.device)
# Generate response
with torch.no_grad():
output_ids = self.model.generate(
input_ids=input_ids.input_ids,
images=image_tensor,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9,
)
# Decode and extract response
output = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)
return output.split("###Assistant:")[-1].strip()
except Exception as e:
return f"Error generating answer: {str(e)}"
# Example usage if __name__ == "__main__":
if __name__ == "__main__":
try:
# Initialize model
llava = LLaVAHelper()
# Example with a local file
# response = llava.generate_answer("path/to/your/image.jpg", "What's in this image?")
# Example with a URL
# image_url = "https://example.com/image.jpg"
# response = llava.generate_answer(image_url, "Describe this image in detail.")
# print(response)
print("LLaVA model initialized successfully. Ready to process images.")
except Exception as e:
print(f"Error initializing LLaVA: {e}") |