simplified app.py

app.py

@@ -1,7 +1,5 @@
-# Final app.py for your Hugging Face Space
-
 import gradio as gr
-import tensorflow as tf # Import tensorflow directly
+import tensorflow as tf
 from huggingface_hub import hf_hub_download
 import numpy as np
 from PIL import Image
@@ -9,31 +7,18 @@ import os
 
 # --- 1. Load the Model from your other Hugging Face Repo ---
 model = None
-print("--- SCRIPT START ---")
 try:
-    print("Downloading Keras model from the Hub...")
     model_path = hf_hub_download(
         repo_id="skibi11/leukolook-eye-detector", 
         filename="MobileNetV1_best.keras"
     )
-    print(f"Model downloaded to: {model_path}")
-    print("Loading model with tf.keras.models.load_model...")
-
-    # This is a more robust way to load the model
     model = tf.keras.models.load_model(model_path)
-
     print("--- MODEL LOADED SUCCESSFULLY! ---")
-    model.summary() # Print a summary of the model to confirm it's loaded
-
 except Exception as e:
-    print("--- AN ERROR OCCURRED DURING MODEL LOADING ---")
-    print(f"Error Type: {type(e)}")
-    print(f"Error Message: {e}")
-    # Also print the traceback for more details
-    import traceback
-    traceback.print_exc()
-    print("--- END OF ERROR ---")
-
+    print(f"--- ERROR LOADING MODEL: {e} ---")
+    # If the model fails to load, we cannot proceed.
+    # Gradio will show an error state.
+    raise gr.Error(f"Failed to load model: {e}")
 
 # --- 2. Define the Pre-processing Logic ---
 def preprocess_image(img_pil):
@@ -41,7 +26,6 @@ def preprocess_image(img_pil):
     img_array = np.array(img)
     if img_array.ndim == 2:
         img_array = np.stack((img_array,)*3, axis=-1)
-    # Ensure image has 3 channels if it's not
     if img_array.shape[-1] == 4:
         img_array = img_array[..., :3]
     img_array = img_array / 255.0
@@ -49,15 +33,13 @@ def preprocess_image(img_pil):
     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 for errors.")
-
+def predict(image_from_gradio):
     try:
-        pil_image = Image.fromarray(image_array.astype('uint8'), 'RGB')
+        pil_image = Image.fromarray(image_from_gradio)
         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
@@ -67,7 +49,7 @@ def predict(image_array):
 # --- 4. Create and Launch the Gradio API ---
 gr.Interface(
     fn=predict,
-    inputs=gr.Image(),
+    inputs=gr.Image(type="numpy"),
     outputs="json",
     title="LeukoLook Eye Detector API",
     description="API for the LeukoLook project."