Delete image_captioning/app.py

image_captioning/app.py

@@ -1,60 +0,0 @@
-import io
-from typing import List
-
-from fastapi import FastAPI, File, UploadFile
-from fastapi.responses import JSONResponse
-from PIL import Image
-import torch
-from torchvision import transforms
-
-from image_captioning.config import TrainingConfig, get_device
-from image_captioning.dataset import IMAGENET_MEAN, IMAGENET_STD, create_tokenizer
-from image_captioning.model import ImageCaptioningModel
-
-app = FastAPI(title="Image Captioning API (HF Space)")
-
-device = get_device()
-training_cfg = TrainingConfig(max_caption_length=50)
-tokenizer = create_tokenizer()
-model = ImageCaptioningModel(training_cfg=training_cfg)
-model.to(device)
-model.eval()
-
-# Load checkpoint from the repo root
-CHECKPOINT_PATH = "best_model.pt"
-state_dict = torch.load(CHECKPOINT_PATH, map_location=device)
-model.load_state_dict(state_dict)
-
-preprocess = transforms.Compose(
-    [
-        transforms.Resize(256),
-        transforms.CenterCrop(224),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
-    ]
-)
-
-
-@app.get("/health")
-async def health() -> dict:
-    return {"status": "ok"}
-
-
-@app.post("/caption")
-async def caption_image(file: UploadFile = File(...)) -> JSONResponse:
-    try:
-        contents = await file.read()
-        image = Image.open(io.BytesIO(contents)).convert("RGB")
-    except Exception as exc:
-        return JSONResponse(status_code=400, content={"error": f"Invalid image: {exc}"})
-
-    tensor = preprocess(image).unsqueeze(0).to(device)
-
-    with torch.no_grad():
-        captions: List[str] = model.generate(
-            images=tensor,
-            max_length=50,
-            num_beams=1,  # deterministic greedy decoding
-        )
-
-    return JSONResponse({"caption": captions[0]})