app production

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+import joblib
+
+# Load the trained model
+model = joblib.load("model.pkl")
+
+# Define class labels (غير الأسامي دي حسب الكلاسات اللي عندك بالضبط)
+class_names = ["Not Monkeypox", "Monkeypox"]
+
+def predict(img):
+    # Resize image to match model input size
+    img = img.resize((224,224))
+    img = np.array(img) / 255.0  # normalize
+    img = np.expand_dims(img, axis=0)
+
+    # Predict
+    preds = model.predict(img)
+
+    # Convert predictions to dictionary
+    return {class_names[i]: float(preds[0][i]) for i in range(len(class_names))}
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=2),
+    title="Monkeypox Classifier",
+    description="Upload an image and the model will classify it as Monkeypox or Not."
+)
+
+if __name__ == "__main__":
+    demo.launch()

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:230ac62ec49f907be731124dd76cba9ae0b777e9fa0744c62e5adcf7383ab30d
+size 265147396

requirements.txt

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+gradio
+numpy
+pillow
+tensorflow
+scikit-learn
+joblib