create app.py

file containing the Gradio code to run your model

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+from tensorflow.keras.models import load_model
+from huggingface_hub import hf_hub_download
+import numpy as np
+from PIL import Image
+
+# --- 1. Load the Model from your other Hugging Face Repo ---
+try:
+    model_path = hf_hub_download(
+        repo_id="skibi11/leukolook-eye-detector", 
+        filename="MobileNetV1_best.keras"
+    )
+    model = load_model(model_path)
+    print("Model loaded successfully!")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    model = None
+
+# --- 2. Define the Pre-processing Logic ---
+def preprocess_image(img_pil):
+    # This MUST match your training pre-processing
+    img = img_pil.resize((224, 224))
+    img_array = np.array(img)
+    if img_array.ndim == 2:
+        img_array = np.stack((img_array,)*3, axis=-1)
+    img_array = img_array / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array
+
+# --- 3. Define the Prediction Function ---
+def predict(image_array):
+    if model is None:
+        raise gr.Error("Model is not loaded. Please check the Space logs.")
+
+    pil_image = Image.fromarray(image_array.astype('uint8'), 'RGB')
+    processed_image = preprocess_image(pil_image)
+    prediction = model.predict(processed_image)
+
+    # Convert prediction to a JSON-friendly format
+    labels = [f"Class_{i}" for i in range(prediction.shape[1])]
+    confidences = {label: float(score) for label, score in zip(labels, prediction[0])}
+    return confidences
+
+# --- 4. Create and Launch the Gradio API ---
+gr.Interface(
+    fn=predict,
+    inputs=gr.Image(),
+    outputs="json",
+    title="LeukoLook Eye Detector API"
+).launch()