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| import streamlit as st | |
| import torch | |
| from PIL import Image | |
| import os | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class Net(nn.Module): | |
| def __init__(self): | |
| super(Net, self).__init__() | |
| self.conv1 = nn.Conv2d(3, 32, 5) | |
| self.conv2 = nn.Conv2d(32, 64, 5) | |
| self.conv3 = nn.Conv2d(64, 128, 5) | |
| self.conv4 = nn.Conv2d(128, 256, 5) | |
| self.conv5 = nn.Conv2d(256, 512, 5) | |
| self.fc1 = None | |
| self.fc2 = nn.Linear(512, 128) | |
| self.fc3 = nn.Linear(128, 64) | |
| self.fc4 = nn.Linear(64, 2) | |
| def forward(self, x): | |
| x = x.float() | |
| """ x = F.relu(self.conv1(x)) | |
| x = F.relu(self.conv2(x)) | |
| x = F.max_pool2d(x, 2) | |
| x = F.relu(self.conv3(x)) | |
| x = F.relu(self.conv4(x)) | |
| x = F.max_pool2d(x, 2) | |
| x = F.relu(self.conv5(x)) | |
| x = F.max_pool2d(x, 2) """ | |
| x = F.max_pool2d(F.relu(self.conv1(x)), 2) | |
| x = F.max_pool2d(F.relu(self.conv2(x)), 2) | |
| x = F.max_pool2d(F.relu(self.conv3(x)), 2) | |
| x = F.max_pool2d(F.relu(self.conv4(x)), 2) | |
| x = F.max_pool2d(F.relu(self.conv5(x)), 2) | |
| #x = x.view(x.size(0), -1) | |
| x = torch.flatten(x, 1) | |
| if self.fc1 is None: | |
| self.fc1 = nn.Linear(x.shape[1], 512).to(x.device) | |
| x = F.relu(self.fc1(x)) | |
| x = F.relu(self.fc2(x)) | |
| x = F.relu(self.fc3(x)) | |
| x = self.fc4(x) | |
| return x | |
| def classify(model, img, trans=None, classes=[], device=torch.device("cpu")): | |
| try: | |
| model = model.eval() | |
| img = img.convert("RGB") | |
| img = trans(img) | |
| img = img.unsqueeze(0) | |
| img = img.to(device) | |
| output = model(img) | |
| _, pred = torch.max(output, 1) | |
| procent = torch.sigmoid(output) | |
| return f"It {classes[pred.item()].replace('_', ' ')}, I'm {procent[0][pred[0]]*100:.2f}% sure" | |
| except Exception: | |
| return "Something went wrong😕, please notify the developer with the following message: " + str(Exception) | |
| st.title("Pizza & Not Pizza") | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| checkpoint = torch.load("best.pth.tar", map_location=device) | |
| model = checkpoint["model"] | |
| classes = checkpoint["classes"] | |
| tran = checkpoint["transform"] | |
| # upload image | |
| uploaded_file = st.file_uploader("Choose an image...", type="jpg") | |
| taking_picture = st.camera_input("Take a picture...") | |
| if uploaded_file is not None: | |
| img = Image.open(uploaded_file) | |
| st.image(img, caption="Uploaded Image.", use_column_width=True) | |
| label = classify(model, img, tran, classes, device) | |
| st.write(label) | |
| elif taking_picture is not None: | |
| img = Image.open(taking_picture) | |
| st.image(img, caption="Uploaded Image.", use_column_width=True) | |
| label = classify(model, img, tran, classes, device) | |
| st.write(label) | |
| else: | |
| pass | |