Create app.py

app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+from PIL import Image
+
+# ==========================================
+# 1. MODEL ARCHITECTURE (Same as training)
+# ==========================================
+class ESPCN(nn.Module):
+    def __init__(self, upscale_factor):
+        super(ESPCN, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=5, padding=2)
+        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
+        self.conv3 = nn.Conv2d(64, 32, kernel_size=3, padding=1)
+        self.conv4 = nn.Conv2d(32, 3 * (upscale_factor ** 2), kernel_size=3, padding=1)
+        self.pixel_shuffle = nn.PixelShuffle(upscale_factor)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.relu(self.conv1(x))
+        x = self.relu(self.conv2(x))
+        x = self.relu(self.conv3(x))
+        x = self.pixel_shuffle(self.conv4(x))
+        return x
+
+# ==========================================
+# 2. LOAD THE TRAINED MODEL (On CPU)
+# ==========================================
+# Device ko CPU set kar rahe hain taaki HF Spaces ke free tier me bina GPU ke chale
+device = torch.device("cpu")
+model = ESPCN(upscale_factor=4)
+
+# Yahan hum .pth file load kar rahe hain
+try:
+    model.load_state_dict(torch.load("universal_sr_model.pth", map_location=device))
+    model.eval()
+    print("Model loaded successfully!")
+except Exception as e:
+    print(f"Error loading model: {e}")
+
+# ==========================================
+# 3. INFERENCE FUNCTION FOR GRADIO
+# ==========================================
+def enhance_image(img):
+    if img is None:
+        return None
+        
+    # Image ko RGB me ensure karna
+    img = img.convert('RGB')
+    
+    # Image ko tensor me convert karna aur batch dimension add karna
+    input_tensor = transforms.ToTensor()(img).unsqueeze(0).to(device)
+    
+    # Model se pass karna (no gradients needed for fast inference)
+    with torch.no_grad():
+        output_tensor = model(input_tensor)
+        
+    # Output tensor ko wapas image me convert karna
+    output_tensor = output_tensor.squeeze().clamp(0, 1)
+    output_img = transforms.ToPILImage()(output_tensor)
+    
+    return output_img
+
+# ==========================================
+# 4. GRADIO INTERFACE SETUP
+# ==========================================
+# UI design setup
+iface = gr.Interface(
+    fn=enhance_image,
+    inputs=gr.Image(type="pil", label="Upload Unclear/Low-Res Image"),
+    outputs=gr.Image(type="pil", label="4x Enhanced High-Res Image"),
+    title="🌟 AI Super Resolution (4x Upscaler)",
+    description="Apni unclear ya chhoti image upload karein aur hamara custom ESPCN model use 4x bada aur sharp kar dega. Yeh model scratch se train kiya gaya hai!",
+    allow_flagging="never"
+)
+
+# App launch karna
+if __name__ == "__main__":
+    iface.launch()