Upload app.py

app.py

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+import os
+import tempfile
+import nibabel as nib
+import numpy as np
+import gdown
+import gradio as gr
+import torch
+
+# nnU-Net imports
+from nnunetv2.inference.predict_from_raw_data import nnUNetPredictor
+
+# -------------------------------
+# STEP 1 — Download model if needed
+# -------------------------------
+MODEL_DIR = "models/nnUNet_trained"
+os.makedirs(MODEL_DIR, exist_ok=True)
+
+FOLDER_URL = "https://drive.google.com/drive/folders/163zOL8NmdYqhRCAGNOWG7Ak85_awF4Xv"
+
+if not os.path.exists(os.path.join(MODEL_DIR, "fold_0")):
+    print("⏬ Downloading nnU-Net model from Google Drive ...")
+    gdown.download_folder(FOLDER_URL, output=MODEL_DIR, quiet=False, use_cookies=False)
+    print("✅ Model downloaded.")
+else:
+    print("✅ Model already cached, skipping download.")
+
+# -------------------------------
+# STEP 2 — Initialize predictor
+# -------------------------------
+print("🔧 Initializing nnU-Net predictor ...")
+predictor = nnUNetPredictor(
+    model_training_output_dir=MODEL_DIR,
+    use_folds=[0, 1, 2, 3, 4],  # ensemble of 5 folds
+    checkpoint_name="checkpoint_final.pth",
+    device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
+)
+predictor.initialize_from_trained_model_folder()
+print("✅ Predictor ready.")
+
+# -------------------------------
+# STEP 3 — Define inference function
+# -------------------------------
+def run_inference(nii_file):
+    # Save uploaded file to a temporary path
+    with tempfile.TemporaryDirectory() as tmpdir:
+        input_path = os.path.join(tmpdir, "input.nii.gz")
+        nii_file.save(input_path)
+
+        # Output directory
+        output_dir = os.path.join(tmpdir, "output")
+        os.makedirs(output_dir, exist_ok=True)
+
+        # Run prediction
+        predictor.predict_from_files(
+            [input_path],
+            output_dir=output_dir,
+            save_probabilities=False
+        )
+
+        # Get the result file
+        output_files = [f for f in os.listdir(output_dir) if f.endswith(".nii.gz")]
+        if not output_files:
+            raise RuntimeError("Prediction failed: No output NIfTI file found.")
+
+        result_path = os.path.join(output_dir, output_files[0])
+
+        # Load and overlay for preview
+        img = nib.load(input_path).get_fdata()
+        seg = nib.load(result_path).get_fdata()
+
+        # Simple 2D preview (middle slice)
+        mid_slice = img.shape[2] // 2
+        overlay = np.stack([
+            img[:, :, mid_slice] / np.max(img),             # base grayscale
+            seg[:, :, mid_slice] / np.max(seg) if np.max(seg) > 0 else np.zeros_like(seg[:, :, mid_slice]),
+            np.zeros_like(seg[:, :, mid_slice])
+        ], axis=-1)
+
+        return (overlay, result_path)
+
+# -------------------------------
+# STEP 4 — Gradio Interface
+# -------------------------------
+title = "🧠 Physics-Informed nnU-Net (Fisher PDE) for Glioblastoma MRI Segmentation"
+description = """
+Upload a **.nii** or **.nii.gz** MRI volume to run inference using the Physics-Informed nnU-Net model.
+The model integrates a Fisher diffusion-reaction equation for enhanced tumor boundary accuracy.
+"""
+
+iface = gr.Interface(
+    fn=run_inference,
+    inputs=gr.File(label="Upload MRI NIfTI (.nii / .nii.gz)"),
+    outputs=[
+        gr.Image(label="Segmentation Overlay (mid-slice)"),
+        gr.File(label="Download Full 3D Segmentation (.nii.gz)")
+    ],
+    title=title,
+    description=description,
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    iface.launch()