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5ae5072 | 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 | import os
from typing import Any, Dict, List, Optional, Union
import torch
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from services.retrieval_service import RetrievalServiceVisual
from utils.image_utils import image_to_base64
from utils.utils import load_yaml
app = FastAPI(title="Retrieval Server")
class SearchRequest(BaseModel):
query: str
top_k: int = 5
class SearchResponse(BaseModel):
images: List[str]
image_paths: List[str]
scores: List[float]
success: bool
message: str
class ProcessApplyFeedbackRequest(BaseModel):
query: str
top_k: int
relevant_image_paths: List[str]
annotator_json_boxes_list: List[Any]
fuse_initial_query: bool = False
class ProcessApplyFeedbackResponse(BaseModel):
images: List[str]
image_paths: List[str]
scores: List[float]
success: bool
message: str
retrieval_service: Optional[RetrievalServiceVisual] = None
@app.on_event("startup")
async def startup_event():
global retrieval_service
config_path = os.getenv("CONFIG_PATH", "configs/demo/coco_clip_large.yaml")
config = load_yaml(config_path)
device = "cuda" if torch.cuda.is_available() else "cpu"
retrieval_service = RetrievalServiceVisual(
config=config,
device=device,
)
@app.post("/search", response_model=SearchResponse)
async def search_images(request: SearchRequest):
try:
images, scores, image_paths = retrieval_service.search_images(request.query, request.top_k)
images = [image_to_base64(img) for img in images]
return SearchResponse(
images=images,
image_paths=image_paths,
scores=scores,
success=True,
message="Search completed successfully"
)
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/apply_feedback", response_model=ProcessApplyFeedbackResponse)
async def apply_feedback(request: ProcessApplyFeedbackRequest):
try:
images, scores, image_paths = retrieval_service.process_and_apply_feedback(
query=request.query,
top_k=request.top_k,
relevant_image_paths=request.relevant_image_paths,
annotator_json_boxes_list=request.annotator_json_boxes_list,
fuse_initial_query=request.fuse_initial_query
)
images = [image_to_base64(img) for img in images]
return ProcessApplyFeedbackResponse(
images=images,
image_paths=image_paths,
scores=scores,
success=True,
message="Feedback applied successfully"
)
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/health")
async def health_check():
return {"status": "healthy", "gpu_available": torch.cuda.is_available()}
if __name__ == "__main__":
import argparse
import uvicorn
parser = argparse.ArgumentParser()
parser.add_argument("--port", type=int, default=8000)
args = parser.parse_args()
port = args.port
uvicorn.run(app, host="0.0.0.0", port=port)
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