Upload 4 files

app.py

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+import torch
+import numpy as np
+import gradio as gr
+import matplotlib.pyplot as plt
+from model import Generator
+
+# Load the model
+device = torch.device("cpu")
+generator = Generator()
+generator.load_state_dict(torch.load("generator_digit.pth", map_location=device))
+generator.eval()
+
+def generate_images(digit):
+    noise = torch.randn(5, 100)
+    labels = torch.tensor([digit] * 5)
+    with torch.no_grad():
+        images = generator(noise, labels).squeeze().numpy()
+
+    # Create a grid of images (5 horizontal)
+    fig, axs = plt.subplots(1, 5, figsize=(10, 2))
+    for i in range(5):
+        axs[i].imshow(images[i], cmap='gray')
+        axs[i].axis('off')
+    return fig
+
+interface = gr.Interface(
+    fn=generate_images,
+    inputs=gr.inputs.Slider(0, 9, step=1, label="Digit (0–9)"),
+    outputs=gr.outputs.Image(type="plot"),
+    title="MNIST Digit Generator",
+    description="Generate 5 images of the digit using a conditional GAN trained on MNIST."
+)
+
+interface.launch()

generator_digit.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:72679173523a721a7ecf0499d0b3eda442158eddcb50c4989b4570ed5976ca0b
+size 5959844

model.py

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+import torch
+import torch.nn as nn
+
+class Generator(nn.Module):
+    def __init__(self, noise_dim=100, num_classes=10, img_dim=28*28):
+        super(Generator, self).__init__()
+        self.label_emb = nn.Embedding(num_classes, num_classes)
+        self.model = nn.Sequential(
+            nn.Linear(noise_dim + num_classes, 256),
+            nn.ReLU(),
+            nn.Linear(256, 512),
+            nn.ReLU(),
+            nn.Linear(512, 1024),
+            nn.ReLU(),
+            nn.Linear(1024, img_dim),
+            nn.Tanh()
+        )
+
+    def forward(self, z, labels):
+        label_input = self.label_emb(labels)
+        x = torch.cat([z, label_input], dim=1)
+        x = self.model(x)
+        return x.view(-1, 1, 28, 28)

requirements.txt

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+torch
+gradio
+matplotlib