import fastapi
from fastapi import FastAPI
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from PIL import Image
import io
import base64

app = FastAPI()

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(6 * 14 * 14, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 3)

    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = x.view(-1, 6 * 14 * 14)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

model = CNN()
model.load_state_dict(torch.load("best_model.pt", map_location=torch.device("cpu")))
model.eval()

data_transforms = transforms.Compose([
    transforms.Resize(32),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

@app.post("/predict")
async def predict(image_base64: str):
    image_bytes = base64.b64decode(image_base64)
    image = Image.open(io.BytesIO(image_bytes))
    image = data_transforms(image)
    image = image.unsqueeze(0)
    output = model(image)
    _, predicted = torch.max(output, 1)
    confidence = torch.nn.functional.softmax(output, dim=1)
    return {
        "predicted_class": predicted.item(),
        "confidence": confidence.tolist()[0]
    }

@app.get("/health")
async def health():
    return {"status": "ok"}

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)