app.py

import torch
from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse
from transformers import ConvNextForImageClassification, AutoImageProcessor
from PIL import Image
import io

# Class names (for skin diseases)
class_names = [
    'Acne and Rosacea Photos', 'Actinic Keratosis Basal Cell Carcinoma and other Malignant Lesions', 'Atopic Dermatitis Photos',
    'Bullous Disease Photos', 'Cellulitis Impetigo and other Bacterial Infections', 'Eczema Photos', 'Exanthems and Drug Eruptions', 
    'Hair Loss Photos Alopecia and other Hair Diseases', 'Herpes HPV and other STDs Photos', 'Light Diseases and Disorders of Pigmentation', 
    'Lupus and other Connective Tissue diseases', 'Melanoma Skin Cancer Nevi and Moles', 'Nail Fungus and other Nail Disease', 
    'Poison Ivy Photos and other Contact Dermatitis', 'Psoriasis pictures Lichen Planus and related diseases', 
    'Scabies Lyme Disease and other Infestations and Bites', 'Seborrheic Keratoses and other Benign Tumors', 'Systemic Disease', 
    'Tinea Ringworm Candidiasis and other Fungal Infections', 'Urticaria Hives', 'Vascular Tumors', 'Vasculitis Photos', 
    'Warts Molluscum and other Viral Infections'
]

# Load model and processor
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-base-224")
model.classifier = torch.nn.Linear(in_features=1024, out_features=23)
model.load_state_dict(torch.load("./models/convnext_base_finetuned.pth", map_location="cpu"))
model.eval()

processor = AutoImageProcessor.from_pretrained("facebook/convnext-base-224")

# FastAPI app
app = FastAPI()

# Prediction helper
def predict(image: Image.Image):
    inputs = processor(images=image, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
        predicted_class = torch.argmax(outputs.logits, dim=1).item()
    return predicted_class, class_names[predicted_class]

# Endpoint: /predict
@app.post("/predict/")
async def predict_endpoint(file: UploadFile = File(...)):
    try:
        img_bytes = await file.read()
        img = Image.open(io.BytesIO(img_bytes)).convert("RGB")
        predicted_class, predicted_name = predict(img)
        return JSONResponse(content={
            "predicted_class": predicted_class,
            "predicted_name": predicted_name
        })
    except Exception as e:
        return JSONResponse(content={"error": str(e)}, status_code=500)

# Required for Hugging Face Spaces (do NOT run uvicorn manually)
# Just expose the app
app = app