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from fastapi import FastAPI
from pydantic import BaseModel
import torch
import torch.nn as nn
import torch
from torchvision import transforms
class Prediction(BaseModel):
prediction: torch.Tensor
app = FastAPI()
# Load the PyTorch model
model = torch.load("best_model-epoch=01-val_loss=3.00.ckpt")
# Define a function to preprocess the input
def preprocess_input(input):
"""Preprocess the input image for the PyTorch image classification model.
Args:
input: A PIL Image object.
Returns:
A PyTorch tensor representing the preprocessed image.
"""
# Resize the image to the expected size.
input = input.resize((224, 224))
# Convert the image to a PyTorch tensor.
input = torch.from_numpy(np.array(input)).float()
# Normalize the image.
input = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(input)
# Return the preprocessed image.
return input
@app.post("/predict", response_model=Prediction)
async def predict_endpoint(input: fastapi.File):
"""Predict the output of the PyTorch image classification model.
Args:
input: A file containing the input image.
Returns:
A JSON object containing the prediction.
"""
# Load the image.
image = await input.read()
image = Image.open(BytesIO(image))
# Preprocess the image.
image = preprocess_input(image)
# Make a prediction.
prediction = model(image.unsqueeze(0))
# Get the top predicted class.
predicted_class = prediction.argmax(1)
# Return the prediction.
return Prediction(prediction=predicted_class)
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
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