app.py

from fastai.vision.all import *
from fastapi import FastAPI
from fastapi.responses import HTMLResponse

app = FastAPI()

def get_x(i):
    # Convert NumPy array to a single-channel PIL image with inverted colors
    return PILImageBW.create(all_noise[i])

def get_y(i):
    return all_thresh[i].astype(np.float32)

def get_items(_):
    return range(len(all_noise))

# Load the model
learn = load_learner('model.pkl')

@app.get("/")
def read_root():
    html_content = "<p>This is a model inference point for the <a href='https://huggingface.co/spaces/vishalbakshi/isitadigit'>isitadigit</a> space</p>"
    return HTMLResponse(content=html_content)

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)


# from fastapi import FastAPI, HTTPException
# from pydantic import BaseModel
# from PIL import Image
# import io
# import base64
# import uvicorn



# # Load the model
# learn = load_learner('model.pkl')

# app = FastAPI()

# class ImageData(BaseModel):
#     image: str

# def predict_image(img):
#     img = img.convert("L")
#     img = img.resize((28, 28))
#     img = np.array(img)
#     return f"{learn.predict(img)[0][0]:.2f}"

# @app.get("/")
# def read_root():
#     return {"message": "Hello World"}

# @app.post("/predict")
# async def predict(data: ImageData):
#     try:
#         image_data = base64.b64decode(data.image)
#         img = Image.open(io.BytesIO(image_data))
#         probability = predict_image(img)
#         return {"probability": probability}
#     except Exception as e:
#         raise HTTPException(status_code=400, detail=str(e))
    
# if __name__ == "__main__":
#     import uvicorn
#     uvicorn.run(app, host="0.0.0.0", port=7860)