Create app.py

app.py

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+import gradio as gr
+import torch
+from torchvision import transforms
+from PIL import Image, ImageFilter
+import os
+import time
+import gc
+
+from RRDBNet_arch import RRDBNet  # Ensure this file is in the same directory
+
+# -------------------------
+# Load ESRGAN Model (from root directory)
+# -------------------------
+@torch.no_grad()
+def load_model():
+    model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23)
+    model_path = "RRDB_ESRGAN_x4.pth"
+    model.load_state_dict(torch.load(model_path, map_location="cpu"), strict=True)
+    model.eval()
+    return model
+
+model = load_model()
+
+# -------------------------
+# Utility Functions
+# -------------------------
+def preprocess(img_pil):
+    transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.5,), (0.5,))
+    ])
+    return transform(img_pil).unsqueeze(0)
+
+def postprocess(tensor):
+    tensor = tensor.squeeze().detach().cpu()
+    tensor = (tensor * 0.5 + 0.5).clamp(0, 1)
+    return transforms.ToPILImage()(tensor)
+
+def fuse_images(img1, img2):
+    img1 = img1.resize((384, 384), Image.LANCZOS)
+    img2 = img2.resize((384, 384), Image.LANCZOS)
+    return Image.blend(img1, img2, alpha=0.5)
+
+def sharpen_image(image: Image.Image) -> Image.Image:
+    return image.filter(ImageFilter.UnsharpMask(radius=1.5, percent=150, threshold=1))
+
+def upscale_to_8k(img: Image.Image) -> Image.Image:
+    return img.resize((8000, 8000), Image.LANCZOS)
+
+# -------------------------
+# Inference Pipeline
+# -------------------------
+def esrgan_pipeline(img1, img2, _):
+    if not img1 or not img2:
+        return None, None, "Please upload two valid images."
+
+    img1 = img1.convert("RGB")
+    img2 = img2.convert("RGB")
+    fused_img = fuse_images(img1, img2)
+
+    start = time.time()
+
+    with torch.no_grad():
+        input_tensor = preprocess(fused_img)
+        sr_output = model(input_tensor)
+
+    base_output = postprocess(sr_output)
+
+    gc.collect()
+    torch.cuda.empty_cache()
+
+    upscaled_img = upscale_to_8k(base_output)
+    final_img = sharpen_image(upscaled_img)
+
+    elapsed = time.time() - start
+    sharpness_score = torch.var(torch.tensor(base_output.convert("L"))).item()
+    msg = f"✅ Done in {elapsed:.2f}s | Sharpness: {sharpness_score:.2f}"
+
+    return base_output, final_img, msg
+
+# -------------------------
+# Gradio UI
+# -------------------------
+with gr.Blocks(title="8000x8000 ESRGAN Ultra-HD Super-Resolution") as demo:
+    gr.Markdown("## 🧠 ESRGAN Ultra-HD Image Upscaler (8000 × 8000 Output)")
+    gr.Markdown("Upload **two low-res images** → Fuse → ESRGAN → Final **8000 × 8000** enhanced image with sharpening.")
+
+    with gr.Row():
+        with gr.Column():
+            img_input1 = gr.Image(type="pil", label="Low-Res Image 1")
+            img_input2 = gr.Image(type="pil", label="Low-Res Image 2")
+            dummy_resolution = gr.Radio(["8000x8000"], value="8000x8000", label="Output Resolution (Fixed)")
+
+            run_button = gr.Button("🚀 Run ESRGAN")
+
+        with gr.Column():
+            output_esrgan = gr.Image(label="🧠 ESRGAN Output")
+            output_final = gr.Image(label="🏞️ Final Enhanced Output (8000 × 8000)")
+            result_text = gr.Textbox(label="📊 Output Log")
+
+    gr.Markdown("---")
+    gr.HTML(
+        "<div style='text-align: center; font-size: 16px;'>"
+        "Made with ❤️ by <b>CodeKarma</b> as a part of <b>Bharatiya Antariksh Hackathon 2025</b>"
+        "</div>"
+    )
+
+    run_button.click(fn=esrgan_pipeline,
+                     inputs=[img_input1, img_input2, dummy_resolution],
+                     outputs=[output_esrgan, output_final, result_text])
+
+# -------------------------
+# Launch
+# -------------------------
+if __name__ == "__main__":
+    demo.launch()