Delete app.py

app.py

@@ -1,47 +0,0 @@
-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()