Upload 3 files

app.py

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+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()

final_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8ff8ac00211462b4b533154bae22362e5501838219a1dfb9eecf3bd8121ca533
+size 24526712

requirements.txt

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+tensorflow
+opencv-python
+numpy
+gradio