File size: 2,076 Bytes
aeb005f |
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 |
# File: app/main.py
from fastapi import FastAPI, UploadFile, File, Query
from fastapi.responses import JSONResponse, StreamingResponse
from PIL import Image
import io
import numpy as np
import traceback
# Import the model utilities
from app.model import predict, gradcam, CLASS_NAMES
app = FastAPI(title="Brain Tumor MRI Classifier (InceptionV3 + Grad-CAM)")
@app.post("/predict")
async def predict_image(file: UploadFile = File(...)):
try:
contents = await file.read()
pil_img = Image.open(io.BytesIO(contents)).convert("RGB")
label, confidence, probs = predict(pil_img)
return JSONResponse({
"predicted_label": label,
"confidence": round(confidence, 3),
"probabilities": {k: round(v, 6) for k, v in probs.items()}
})
except Exception as e:
tb = traceback.format_exc()
return JSONResponse({"error": str(e), "trace": tb}, status_code=500)
@app.post("/gradcam")
async def gradcam_image(file: UploadFile = File(...), interpolant: float = Query(0.5, ge=0.0, le=1.0)):
"""
Returns a PNG image (overlay) produced by gradcam().
`interpolant` controls mixing (0..1).
"""
try:
contents = await file.read()
pil_img = Image.open(io.BytesIO(contents)).convert("RGB")
# Compute overlay (this calls the optimized gradcam in model.py)
overlay = gradcam(pil_img, interpolant=float(interpolant))
# Ensure correct dtype and shape
overlay = np.asarray(overlay).astype("uint8")
if overlay.ndim == 2:
overlay = np.stack([overlay] * 3, axis=-1)
# Convert to PNG bytes
buf = io.BytesIO()
Image.fromarray(overlay).save(buf, format="PNG")
buf.seek(0)
return StreamingResponse(buf, media_type="image/png")
except Exception as e:
tb = traceback.format_exc()
return JSONResponse({"error": str(e), "trace": tb}, status_code=500)
# Optional health endpoint
@app.get("/health")
async def health():
return {"status": "ok", "classes": CLASS_NAMES} |