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import os
import threading
import torch
from typing import List, Optional, AsyncGenerator
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
from fastapi import HTTPException
from image_utils import filter_valid_images
style_model = None
style_processor = None
model_lock = threading.Lock()
def ensure_model_loaded():
global style_model, style_processor
if style_model is not None:
return
print("Loading model (lazy load on first request)...")
model_id = "Qwen/Qwen2.5-VL-7B-Instruct"
hf_token = os.getenv("HF_TOKEN")
try:
if torch.cuda.is_available():
dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
max_memory = {0: "14GiB", "cpu": "2GiB"}
offload_folder = None
else:
dtype = torch.float16
max_memory = {"cpu": "14GiB"}
offload_folder = "/tmp/model_offload"
os.makedirs(offload_folder, exist_ok=True)
load_kwargs = {
"torch_dtype": dtype,
"device_map": "auto",
"low_cpu_mem_usage": True,
"max_memory": max_memory,
}
if offload_folder:
load_kwargs["offload_folder"] = offload_folder
if hf_token:
load_kwargs["token"] = hf_token
style_model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
model_id,
**load_kwargs
)
style_processor = AutoProcessor.from_pretrained(model_id, token=hf_token)
else:
style_model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
model_id,
**load_kwargs
)
style_processor = AutoProcessor.from_pretrained(model_id)
print(f"Loaded {model_id} with dtype={dtype}, device_map=auto")
except Exception as e:
print(f"Error loading model: {e}")
import traceback
traceback.print_exc()
raise
def generate_chat_response(prompt: str, max_length: int = 512, temperature: float = 0.7, rag_context: Optional[str] = None, system_override: Optional[str] = None, images: Optional[List[str]] = None) -> str:
ensure_model_loaded()
system_message = system_override if system_override else "You are StyleGPT, a friendly and helpful fashion stylist assistant. You give natural, conversational advice about clothing, colors, and outfit combinations. Always be warm, friendly, and advisory in your responses. When asked your name, say you're StyleGPT. When greeted, respond warmly and offer to help with fashion advice."
if rag_context:
system_message += f"\n\n{rag_context}\n\nUse this fashion knowledge to provide accurate and helpful advice. Reference this knowledge naturally in your responses."
user_content = []
if images:
valid_images = filter_valid_images(images)
for pil_image in valid_images:
user_content.append({"type": "image", "image": pil_image})
user_content.append({"type": "text", "text": prompt})
messages = [
{"role": "system", "content": system_message},
{"role": "user", "content": user_content}
]
try:
text = style_processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = style_processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
if hasattr(style_model, 'device'):
device = style_model.device
elif hasattr(style_model, 'hf_device_map'):
device = next(iter(style_model.hf_device_map.values())) if style_model.hf_device_map else torch.device("cpu")
else:
device = torch.device("cpu")
inputs = {k: v.to(device) for k, v in inputs.items()}
temperature = max(0.1, min(1.5, temperature))
with model_lock:
with torch.no_grad():
try:
outputs = style_model.generate(
**inputs,
max_new_tokens=max_length,
temperature=temperature,
top_p=0.95,
top_k=50,
do_sample=True,
eos_token_id=style_processor.tokenizer.eos_token_id,
pad_token_id=style_processor.tokenizer.pad_token_id,
repetition_penalty=1.1,
)
except RuntimeError as e:
if "probability tensor" in str(e) or "inf" in str(e) or "nan" in str(e):
print(f"[GENERATE] Probability error, retrying with greedy decoding")
outputs = style_model.generate(
**inputs,
max_new_tokens=max_length,
do_sample=False,
eos_token_id=style_processor.tokenizer.eos_token_id,
pad_token_id=style_processor.tokenizer.pad_token_id,
repetition_penalty=1.1,
)
else:
raise
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs['input_ids'], outputs)
]
generated_text = style_processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]
generated_text = generated_text.strip()
return generated_text
except Exception as e:
print(f"[GENERATE] Error: {e}")
import traceback
traceback.print_exc()
raise HTTPException(status_code=500, detail=f"Generation error: {str(e)}")
async def generate_chat_response_streaming(prompt: str, max_length: int = 512, temperature: float = 0.7, rag_context: Optional[str] = None, system_override: Optional[str] = None, images: Optional[List[str]] = None) -> AsyncGenerator[str, None]:
ensure_model_loaded()
system_message = system_override if system_override else "You are StyleGPT, a friendly and helpful fashion stylist assistant. You give natural, conversational advice about clothing, colors, and outfit combinations. Always be warm, friendly, and advisory in your responses. When asked your name, say you're StyleGPT. When greeted, respond warmly and offer to help with fashion advice."
if rag_context:
system_message += f"\n\n{rag_context}\n\nUse this fashion knowledge to provide accurate and helpful advice. Reference this knowledge naturally in your responses."
user_content = []
if images:
valid_images = filter_valid_images(images)
for pil_image in valid_images:
user_content.append({"type": "image", "image": pil_image})
user_content.append({"type": "text", "text": prompt})
messages = [
{"role": "system", "content": system_message},
{"role": "user", "content": user_content}
]
try:
text = style_processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = style_processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
if hasattr(style_model, 'device'):
device = style_model.device
elif hasattr(style_model, 'hf_device_map'):
device = next(iter(style_model.hf_device_map.values())) if style_model.hf_device_map else torch.device("cpu")
else:
device = torch.device("cpu")
inputs = {k: v.to(device) for k, v in inputs.items()}
temperature = max(0.1, min(1.5, temperature))
with model_lock:
with torch.no_grad():
try:
outputs = style_model.generate(
**inputs,
max_new_tokens=max_length,
temperature=temperature,
top_p=0.95,
top_k=50,
do_sample=True,
eos_token_id=style_processor.tokenizer.eos_token_id,
pad_token_id=style_processor.tokenizer.pad_token_id,
repetition_penalty=1.1,
)
except RuntimeError as e:
if "probability tensor" in str(e) or "inf" in str(e) or "nan" in str(e):
print(f"[GENERATE STREAM] Probability error, retrying with greedy decoding")
outputs = style_model.generate(
**inputs,
max_new_tokens=max_length,
do_sample=False,
eos_token_id=style_processor.tokenizer.eos_token_id,
pad_token_id=style_processor.tokenizer.pad_token_id,
repetition_penalty=1.1,
)
else:
raise
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs['input_ids'], outputs)
]
generated_text = style_processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]
generated_text = generated_text.strip()
import asyncio
for char in generated_text:
yield char
await asyncio.sleep(0.01)
except Exception as e:
print(f"[GENERATE STREAM] Error: {e}")
import traceback
traceback.print_exc()
error_msg = f"I apologize, but I encountered an error generating a response. Please try again."
import asyncio
for char in error_msg:
yield char
await asyncio.sleep(0.01)
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