Upload app.py

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+from tensorflow.keras.models import load_model
+from huggingface_hub import hf_hub_download
+
+model_path = hf_hub_download(repo_id="TaliZG03/kidney_normal_CT_classifier_model", filename="model.keras")
+model = load_model(model_path)
+
+def preprocess(image):
+    image = image.resize((300, 300)).convert("RGB")
+    image = np.array(image) / 255.0
+    return np.expand_dims(image, axis=0)
+
+def predict(image):
+    input_array = preprocess(image)
+    prediction = model.predict(input_array)[0][0]
+    label = "NORMAL" if prediction >= 0.5 else "ABNORMAL"
+    confidence = prediction if label == "NORMAL" else 1 - prediction
+
+    if label == "NORMAL" and confidence >= 0.7:
+        explanation = "✅ The kidney CT scan appears normal with high confidence."
+        attention_flag = ""
+    elif label == "NORMAL" and confidence < 0.7:
+        explanation = "⚠️ The scan appears normal, but the model's confidence is low. Consider radiologist review."
+        attention_flag = "🚨 FLAGGED FOR RADIOLOGIST REVIEW"
+    else:
+        explanation = "⚠️ The kidney CT scan shows signs of abnormality. Immediate radiologist attention is recommended."
+        attention_flag = "🚨 FLAGGED FOR RADIOLOGIST REVIEW"
+
+    return f""" Prediction: {label}
+🔎 Confidence: {confidence:.2%}
+
+{explanation}
+{attention_flag}"""
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Kidney CT Classifier",
+    description="Upload a kidney CT image. The model will predict if it's NORMAL or ABNORMAL. Flagged results go to radiologist review.",
+    examples=["images/sample1.png", "images/sample2.png"]
+)
+
+if __name__ == "__main__":
+    demo.launch()