updated app.py

app.py

@@ -1,28 +1,49 @@
+# Final app.py for your Hugging Face Space
+
 import gradio as gr
-from tensorflow.keras.models import load_model
+import tensorflow as tf # Import tensorflow directly
 from huggingface_hub import hf_hub_download
 import numpy as np
 from PIL import Image
+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"
     )
-    model = load_model(model_path)
-    print("Model loaded successfully!")
+    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(f"Error loading model: {e}")
-    model = None
+    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 ---")
+
 
 # --- 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)
+    # 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
     img_array = np.expand_dims(img_array, axis=0)
     return img_array
@@ -30,21 +51,24 @@ def preprocess_image(img_pil):
 # --- 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.")
+        raise gr.Error("Model is not loaded. Please check the Space logs for errors.")
 
-    pil_image = Image.fromarray(image_array.astype('uint8'), 'RGB')
-    processed_image = preprocess_image(pil_image)
-    prediction = model.predict(processed_image)
+    try:
+        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
+        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
+    except Exception as e:
+        raise gr.Error(f"Error during prediction: {e}")
 
 # --- 4. Create and Launch the Gradio API ---
 gr.Interface(
     fn=predict,
     inputs=gr.Image(),
     outputs="json",
-    title="LeukoLook Eye Detector API"
+    title="LeukoLook Eye Detector API",
+    description="API for the LeukoLook project."
 ).launch()