Create app.py

app.py

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+from sklearn.datasets import fetch_openml
+from sklearn.model_selection import train_test_split
+import numpy as np
+import torch
+from skorch import NeuralNetClassifier
+from torch import nn
+import torch.nn.functional as F
+import matplotlib.pyplot as plt
+mnist = fetch_openml('mnist_784', as_frame=False, cache=False)
+X = mnist.data.astype('float32')
+y = mnist.target.astype('int64')
+X /= 255.0
+
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+XCnn = X.reshape(-1, 1, 28, 28)
+XCnn_train, XCnn_test, y_train, y_test = train_test_split(XCnn, y, test_size=0.25, random_state=42)
+
+from PIL import Image
+import torchvision.transforms as transforms
+class Cnn(nn.Module):
+    def __init__(self, dropout=0.5):
+        super(Cnn, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
+        self.conv2_drop = nn.Dropout2d(p=dropout)
+        self.fc1 = nn.Linear(1600, 100) # 1600 = number channels * width * height
+        self.fc2 = nn.Linear(100, 10)
+        self.fc1_drop = nn.Dropout(p=dropout)
+
+    def forward(self, x):
+        x = torch.relu(F.max_pool2d(self.conv1(x), 2))
+        x = torch.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
+
+        # flatten over channel, height and width = 1600
+        x = x.view(-1, x.size(1) * x.size(2) * x.size(3))
+
+        x = torch.relu(self.fc1_drop(self.fc1(x)))
+        x = torch.softmax(self.fc2(x), dim=-1)
+        return x
+torch.manual_seed(0)
+
+cnn = NeuralNetClassifier(
+    Cnn,
+    max_epochs=10,
+    lr=0.002,
+    optimizer=torch.optim.Adam,
+    device=device,
+)
+cnn.fit(XCnn_train, y_train)
+# Specify the path to save the model weights
+# After training, save the model weights
+import torch
+
+
+
+# After training, save the model weights
+model_weights_path = 'model_weights.pth'
+
+torch.save(cnn.module_.state_dict(), model_weights_path)
+
+
+
+stroke_width = st.sidebar.slider("Stroke width: ", 1, 35, 32)
+stroke_color = st.sidebar.color_picker("Stroke color hex: ")
+bg_color = st.sidebar.color_picker("Background color hex: ", "#eee")
+bg_image = st.sidebar.file_uploader("Background image:", type=["png", "jpg"])
+drawing_mode = st.sidebar.selectbox(
+    "Drawing tool:", ("freedraw", "line", "rect", "circle", "transform", "polygon")
+)
+realtime_update = st.sidebar.checkbox("Update in realtime", True)
+
+# Create a canvas component
+canvas_result = st_canvas(
+    fill_color="rgba(255, 165, 0, 0.3)",  # Fixed fill color with some opacity
+    stroke_width=stroke_width,
+    stroke_color=stroke_color,
+    background_color=bg_color,
+    background_image=Image.open(bg_image) if bg_image else None,
+    update_streamlit=realtime_update,
+    height=300,
+    drawing_mode=drawing_mode,
+    display_toolbar=st.sidebar.checkbox("Display toolbar", True),
+    key="full_app",
+)
+
+# Do something interesting with the image data and paths
+if canvas_result.image_data is not None:
+    #st.image(canvas_result.image_data)
+    image = canvas_result.image_data
+    image1 = image.copy()
+    image1 = image1.astype('uint8')
+    image1 = cv2.cvtColor(image1,cv2.COLOR_BGR2GRAY)
+    image1 = cv2.resize(image1,(28,28))
+    st.image(image1)
+
+    image1.resize(1,28,28,1)
+    st.title(np.argmax(cnn.predict(image1)))
+if canvas_result.json_data is not None:
+    st.dataframe(pd.json_normalize(canvas_result.json_data["objects"]))