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README.md

@@ -1,13 +1,12 @@
----
-title: Classification
-emoji: 🏆
-colorFrom: purple
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.36.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Mnist Classification
+emoji: 🌖
+colorFrom: blue
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.17.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import time
+from PIL import Image, ImageOps
+from torch import nn
+import torchvision.transforms as T
+import torch
+import cv2
+import numpy as np
+import streamlit as st
+
+st.set_page_config(layout="wide", page_title="Digit Recognition")
+
+class Network(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=(5, 5), stride=(1, 1), padding=(0, 0))
+        self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=(5, 5), stride=(1, 1), padding=(0, 0))
+        self.conv3 = nn.Conv2d(in_channels=16, out_channels=120, kernel_size=(4, 4), stride=(1, 1), padding=(0, 0))
+
+        self.fully_connected1 = nn.Linear(in_features=120, out_features=84)
+        self.fully_connected2 = nn.Linear(in_features=84, out_features=10)
+
+        self.pooling_layer = nn.AvgPool2d(kernel_size=(2, 2), stride=(2, 2))
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout(0.25)
+
+    def forward(self, x):
+        # Convolution Layer 1
+        x = self.conv1(x)
+        x = self.relu(x)
+        x = self.pooling_layer(x)
+
+        # Convolution Layer 2
+        x = self.conv2(x)
+        x = self.relu(x)
+        x = self.pooling_layer(x)
+        x = self.dropout(x)
+
+        # Convolution Layer 3
+        x = self.conv3(x)
+        x = self.relu(x)
+
+        # flatten x
+        x = x.view(-1, 120)
+
+        # Fully connected layer 1
+        x = self.fully_connected1(x)
+        x = self.relu(x)
+
+        # Fully connected layer 2
+        x = self.fully_connected2(x)
+
+        return x
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = Network()
+model.load_state_dict(torch.load('mnist_model.pth', map_location=torch.device(device)))
+
+st.title("MNIST Image Classification")
+st.subheader("This is a simple image classification web application to predict handwritten digits")
+
+st.sidebar.write('## Please upload an image file :camera:', unsafe_allow_html=True)
+file = st.sidebar.file_uploader("## Upload", type=["png"])
+
+if file is None:
+    imagefile = './0.png'
+else:
+    imagefile = file
+
+img = Image.open(imagefile)
+img_copy = img
+img = cv2.cvtColor(np.array(img), cv2.COLOR_BGR2GRAY)
+transform = T.Compose([
+    T.ToTensor(),
+    T.Resize((28, 28))
+])
+img = transform(img)
+st.image(img_copy, width=150)
+model.eval()
+results = model(img)
+category = torch.argmax(results)
+print(category.numpy())
+st.write('<hr font-size: 30px;>The image is digit </hr>', str(category.numpy()), unsafe_allow_html=True)

mnist_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b9f58974b27543b4f1eb5b7a3c4609d0c2aca5fae2bde76a11b07e721e7b20e1
+size 180807

requirements.txt

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+torch==1.12.1
+torchaudio==0.12.1
+torchvision==0.13.1
+streamlit==1.20.0
+pandas==1.4.2
+opencv-python==4.6.0.66
+numpy==1.21.5
+matplotlib==3.5.1
+Pillow==9.0.1
+streamlit-image-select==0.5.1