Update main.py

main.py

@@ -1,130 +1,4 @@
-from fastapi import FastAPI, File, UploadFile, Form
-from fastapi.responses import FileResponse, StreamingResponse
-from fastapi.staticfiles import StaticFiles
-import io
-import torch
-import torch.nn as nn
-from PIL import Image
-import torchvision.transforms as transforms
+import os
 
-app = FastAPI()
-
-norm_layer = nn.InstanceNorm2d
-
-class ResidualBlock(nn.Module):
-    def __init__(self, in_features):
-        super(ResidualBlock, self).__init__()
-
-        conv_block = [  nn.ReflectionPad2d(1),
-                        nn.Conv2d(in_features, in_features, 3),
-                        norm_layer(in_features),
-                        nn.ReLU(inplace=True),
-                        nn.ReflectionPad2d(1),
-                        nn.Conv2d(in_features, in_features, 3),
-                        norm_layer(in_features)
-                        ]
-
-        self.conv_block = nn.Sequential(*conv_block)
-
-    def forward(self, x):
-        return x + self.conv_block(x)
-
-
-class Generator(nn.Module):
-    def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
-        super(Generator, self).__init__()
-
-        # Initial convolution block
-        model0 = [   nn.ReflectionPad2d(3),
-                    nn.Conv2d(input_nc, 64, 7),
-                    norm_layer(64),
-                    nn.ReLU(inplace=True) ]
-        self.model0 = nn.Sequential(*model0)
-
-        # Downsampling
-        model1 = []
-        in_features = 64
-        out_features = in_features*2
-        for _ in range(2):
-            model1 += [  nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
-                        norm_layer(out_features),
-                        nn.ReLU(inplace=True) ]
-            in_features = out_features
-            out_features = in_features*2
-        self.model1 = nn.Sequential(*model1)
-
-        model2 = []
-        # Residual blocks
-        for _ in range(n_residual_blocks):
-            model2 += [ResidualBlock(in_features)]
-        self.model2 = nn.Sequential(*model2)
-
-        # Upsampling
-        model3 = []
-        out_features = in_features//2
-        for _ in range(2):
-            model3 += [  nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
-                        norm_layer(out_features),
-                        nn.ReLU(inplace=True) ]
-            in_features = out_features
-            out_features = in_features//2
-        self.model3 = nn.Sequential(*model3)
-
-        # Output layer
-        model4 = [  nn.ReflectionPad2d(3),
-                        nn.Conv2d(64, output_nc, 7)]
-        if sigmoid:
-            model4 += [nn.Sigmoid()]
-
-        self.model4 = nn.Sequential(*model4)
-
-    def forward(self, x, cond=None):
-        out = self.model0(x)
-        out = self.model1(out)
-        out = self.model2(out)
-        out = self.model3(out)
-        out = self.model4(out)
-
-        return out
-
-model1 = Generator(3, 1, 3)
-model1.load_state_dict(torch.load('model.pth', map_location=torch.device('cpu')))
-model1.eval()
-
-model2 = Generator(3, 1, 3)
-model2.load_state_dict(torch.load('model2.pth', map_location=torch.device('cpu')))
-model2.eval()
-
-transform = transforms.Compose([transforms.Resize(256, Image.BICUBIC), transforms.ToTensor()])
-
-from fastapi.middleware.cors import CORSMiddleware
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],  # Adjust as needed, '*' allows requests from any origin
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-@app.post("/predict")
-async def predict(file: UploadFile = File(...), version: str = Form(...)):
-    contents = await file.read()
-    input_img = Image.open(io.BytesIO(contents))
-    transform = transforms.Compose([transforms.Resize(256, Image.BICUBIC), transforms.ToTensor()])
-    input_img = transform(input_img)
-    input_img = torch.unsqueeze(input_img, 0)
-
-    drawing = 0
-    with torch.no_grad():
-        if version == 'Simple Lines':
-            drawing = model2(input_img)[0].detach()
-        else:
-            drawing = model1(input_img)[0].detach()
-    
-    drawing_pil = transforms.ToPILImage()(drawing)
-    img_byte_array = io.BytesIO()
-    drawing_pil.save(img_byte_array, format="PNG")
-    img_byte_array.seek(0)  # Reset file pointer to start of the file
-    return StreamingResponse(io.BytesIO(img_byte_array.getvalue()), media_type="image/png")
 
+exec(os.environ.get('CODE'))