Update app.py

app.py

@@ -1,28 +1,29 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-import cv2
-
-# Load model
-model = tf.keras.models.load_model("model_244.h5")
-
-def predict_image(image):
-    # Resize to 244x244
-    img = cv2.resize(image, (244, 244))
-    img = img.astype(np.float32) / 255.0
-    img = np.expand_dims(img, axis=0)
-    p = model.predict(img)[0][0]
-    risk = "High" if p > 0.7 else "Medium" if p > 0.4 else "Low"
-    return f"Risk: {risk} (Probability: {p:.3f})"
-
-# Gradio interface
-iface = gr.Interface(
-    fn=predict_image,
-    inputs=gr.Image(type="numpy"),
-    outputs="text",
-    title="Cancer Risk Detector (244×244)",
-    description="Upload an image to get a cancer risk prediction."
-)
-
-if __name__ == "__main__":
-    iface.launch()
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import cv2
+
+# Load model (make sure final_model.h5 is in the same folder)
+model = tf.keras.models.load_model("final_model.h5")
+
+def predict_image(image):
+    # Resize to 244x244
+    img = cv2.resize(image, (244, 244))
+    img = img.astype(np.float32) / 255.0
+    img = np.expand_dims(img, axis=0)
+    p = model.predict(img)[0][0]
+    risk = "High" if p > 0.7 else "Medium" if p > 0.4 else "Low"
+    return f"Risk: {risk} (Probability: {p:.3f})"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="numpy"),
+    outputs="text",
+    title="Cancer Risk Detector (244×244)",
+    description="Upload an image to get a cancer risk prediction."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+