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from fastapi import FastAPI
from pydantic import BaseModel
import torch
import base64
from io import BytesIO
from PIL import Image
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
# Initialize FastAPI
app = FastAPI()
# Load the model and processor
#checkpoint = "Qwen/Qwen2.5-VL-3B-Instruct"
#checkpoint = "Qwen/Qwen2.5-VL-7B-Instruct"
checkpoint = "Qwen/Qwen2.5-VL-32B-Instruct"
#checkpoint = "Qwen/Qwen2.5-VL-72B-Instruct"
min_pixels = 256 * 28 * 28
max_pixels = 1280 * 28 * 28
processor = AutoProcessor.from_pretrained(
checkpoint,
min_pixels=min_pixels,
max_pixels=max_pixels,
use_fase=True
)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
checkpoint,
torch_dtype=torch.bfloat16,
device_map="auto",
)
# Define the request schema
class ImageRequest(BaseModel):
image_base64: str # Base64 encoded image
prompt: str # Text prompt
@app.get("/")
def read_root():
return {"message": "API is live. Use the /predict endpoint."}
@app.post("/predict") # Changed from GET to POST
async def predict(request: ImageRequest):
# Decode the base64 image
try:
image_data = base64.b64decode(request.image_base64)
image = Image.open(BytesIO(image_data)).convert("RGB")
except Exception as e:
return {"error": f"Invalid base64 image data: {str(e)}"}
# Create message structure
messages = [
{"role": "system", "content": "You are a helpful assistant with vision abilities."},
{"role": "user", "content": [{"type": "image", "image": image}, {"type": "text", "text": request.prompt}]},
]
# Process inputs
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
).to(model.device)
# Run inference
with torch.no_grad():
generated_ids = model.generate(**inputs, max_new_tokens=4096) # 128
# Process output
generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
output_texts = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
return {"response": output_texts[0]}
class SummaryRequest(BaseModel):
prompt: str # Input text to summarize
@app.post("/summary")
async def summary(request: SummaryRequest):
# Create message structure
messages = [
{"role": "system", "content": "You are a helpful assistant that summarizes text."},
{"role": "user", "content": [{"type": "text", "text": request.prompt}]},
]
# Process inputs (text-only)
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(
text=[text],
padding=True,
return_tensors="pt",
).to(model.device)
# Run inference
with torch.no_grad():
generated_ids = model.generate(**inputs, max_new_tokens=4096) # Adjust max_new_tokens for summary length
# Process output
generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
output_texts = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
return {"response": output_texts[0]} |