api.py

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

app = FastAPI(title="Skin Disease Classifier API")

print("Loading model... This may take a while the first time as it downloads from Hugging Face.")
model_name = "HotJellyBean/skin-disease-classifier"

# Load the processor and model
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)
print("Model loaded successfully!")

@app.get("/")
async def root():
    return {"message": "Skin Disease Classifier API is running. Send a POST request with an image to /predict."}

@app.post("/predict")
async def predict_skin_disease(file: UploadFile = File(...)):
    if not file.content_type.startswith('image/'):
        return JSONResponse(content={"success": False, "error": "File must be an image"}, status_code=400)
    
    try:
        contents = await file.read()
        image = Image.open(io.BytesIO(contents)).convert("RGB")
        
        # Preprocess the image
        inputs = processor(images=image, return_tensors="pt")
        
        # Make prediction
        with torch.no_grad():
            outputs = model(**inputs)
            
        logits = outputs.logits
        predicted_class_idx = logits.argmax(-1).item()
        
        # Get class labels from the model
        labels = model.config.id2label
        predicted_class = labels[predicted_class_idx]
        
        # Calculate confidence probabilities using softmax
        probabilities = torch.nn.functional.softmax(logits, dim=-1)[0]
        
        # Map probabilities to class names
        all_probs = {labels[i]: float(prob) for i, prob in enumerate(probabilities)}
        
        # Sort probabilities from highest to lowest
        sorted_probs = dict(sorted(all_probs.items(), key=lambda item: item[1], reverse=True))
        
        return JSONResponse(content={
            "success": True,
            "prediction": predicted_class,
            "confidence": float(probabilities[predicted_class_idx]),
            "details": sorted_probs
        })
        
    except Exception as e:
        return JSONResponse(content={"success": False, "error": str(e)}, status_code=500)

if __name__ == "__main__":
    import uvicorn
    # Run the API on Hugging Face default port 7860
    uvicorn.run(app, host="0.0.0.0", port=7860)