Initial Deployment

app.py

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+import gradio as gr
+import numpy as np
+import cv2
+from tensorflow.keras.models import load_model
+
+model = load_model("model.h5")  # change if name differs
+
+IMG_SIZE = 224  # adjust based on your model
+
+def predict(image):
+    img = cv2.resize(image, (IMG_SIZE, IMG_SIZE))
+    img = img / 255.0
+    img = np.expand_dims(img, axis=0)
+
+    pred = model.predict(img)
+    return pred.tolist()
+
+gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy"),
+    outputs="text",
+    title="MRI / Ultrasound Classifier"
+).launch()

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5c039aa09e6e59e2f811edada33853d6c6859ede2b3564c7c893d6105f087254
+size 134080104

requirements.txt

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+tensorflow==2.20.0
+gradio
+numpy
+opencv-python-headless