Create app.py

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+import numpy as np
+import cv2
+from tensorflow.keras.models import load_model
+from huggingface_hub import hf_hub_download
+
+# Load models from your repos
+classification_model_path = hf_hub_download(
+    repo_id="MohammedAH/Brrain-MRI-Classification",
+    filename="brain_mri.h5"
+)
+
+segmentation_model_path = hf_hub_download(
+    repo_id="MohammedAH/Unet-Brain-Segmentation",
+    filename="Unet_model.h5"
+)
+
+classification_model = load_model(classification_model_path, compile=False)
+segmentation_model = load_model(segmentation_model_path, compile=False)
+
+class_names = ['glioma', 'meningioma', 'no_tumor', 'pituitary']
+
+
+def predict(image):
+    # Classification
+    img = cv2.resize(image, (224, 224)) / 255.0
+    cls_input = np.expand_dims(img, axis=0)
+
+    preds = classification_model.predict(cls_input)
+    idx = int(np.argmax(preds[0]))
+
+    # Segmentation
+    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    seg = cv2.resize(gray, (128, 128)) / 255.0
+    seg_input = np.expand_dims(seg, axis=(0, -1))
+
+    mask = segmentation_model.predict(seg_input)
+    mask = (mask > 0.5).astype(np.uint8)[0, :, :, 0]
+
+    return {
+        "prediction": class_names[idx],
+        "confidence": float(preds[0][idx]),
+        "mask": mask.tolist()
+    }
+
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.JSON()   # 🔥 important (API-friendly)
+)
+
+interface.launch()