Upload 3 files

app.py

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+import torch
+import torch.nn as nn
+import gradio as gr
+from torchvision.utils import make_grid
+import torchvision.transforms as transforms
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image
+
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Fixed Generator definition (with bias=True)
+class Generator(nn.Module):
+    def __init__(self, z_dim=100, channels_img=3, features_g=64):
+        super(Generator, self).__init__()
+        self.net = nn.Sequential(
+            nn.ConvTranspose2d(z_dim, features_g * 8, 4, 1, 0, bias=True),
+            nn.BatchNorm2d(features_g * 8),
+            nn.ReLU(True),
+
+            nn.ConvTranspose2d(features_g * 8, features_g * 4, 4, 2, 1, bias=True),
+            nn.BatchNorm2d(features_g * 4),
+            nn.ReLU(True),
+
+            nn.ConvTranspose2d(features_g * 4, features_g * 2, 4, 2, 1, bias=True),
+            nn.BatchNorm2d(features_g * 2),
+            nn.ReLU(True),
+
+            nn.ConvTranspose2d(features_g * 2, features_g, 4, 2, 1, bias=True),
+            nn.BatchNorm2d(features_g),
+            nn.ReLU(True),
+
+            nn.ConvTranspose2d(features_g, channels_img, 4, 2, 1, bias=True),
+            nn.Tanh()
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+# Load generator
+z_dim = 100
+generator = Generator(z_dim=z_dim).to(device)
+generator.load_state_dict(torch.load("/content/generator_best.pth", map_location=device))
+generator.eval()
+
+# Image generation function
+def generate_image(seed: int = 42):
+    torch.manual_seed(seed)
+    noise = torch.randn(1, z_dim, 1, 1, device=device)
+    with torch.no_grad():
+        fake_image = generator(noise).cpu()
+
+    # Convert to PIL image
+    img_tensor = (fake_image + 1) / 2  # Denormalize from [-1, 1] to [0, 1]
+    img_tensor = img_tensor.squeeze(0)
+    to_pil = transforms.ToPILImage()
+    img_pil = to_pil(img_tensor)
+    return img_pil
+
+# Gradio UI
+gr.Interface(
+    fn=generate_image,
+    inputs=gr.Number(value=42, label="Random Seed"),
+    outputs=gr.Image(type="pil"),
+    title="DCGAN Image Generator",
+    description="Generate fake images using your trained DCGAN Generator"
+).launch()

generator_best.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7e97248165ba1e7954f4a6e43c60c41cb23df14473160429b36527e446b67886
+size 14328298

requirements.txt

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+torch>=1.10
+torchvision
+numpy
+matplotlib
+Pillow
+gradio>=4.0