main.py

import io
import torch
from PIL import Image
from fastapi import FastAPI, File, UploadFile, HTTPException
from transformers import AutoImageProcessor, AutoModelForImageClassification

# Log model loading
print("🚀 Starting model download...")
model_id = "sheikh987/Skin_Cancer-Image_Classification"
processor = AutoImageProcessor.from_pretrained(model_id)
model = AutoModelForImageClassification.from_pretrained(model_id)
print("✅ Model loaded successfully.")

# FastAPI app
app = FastAPI(title="Skin Cancer Classifier API")

# Health check endpoint
@app.get("/status")
def status():
    return {"status": "ok", "model": model_id}

# Image classification endpoint
@app.post("/predict/")
async def predict(file: UploadFile = File(...)):
    if not file.content_type.startswith("image/"):
        raise HTTPException(status_code=400, detail="Invalid image file")

    try:
        image_bytes = await file.read()
        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
    except Exception:
        raise HTTPException(status_code=400, detail="Could not decode image")

    inputs = processor(images=image, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)

    logits = outputs.logits
    idx = logits.argmax(-1).item()
    label = model.config.id2label[idx]
    confidence = torch.nn.functional.softmax(logits, dim=-1)[0][idx].item()

    return {"label": label, "confidence": round(confidence, 4)}