kapoyag update aning app.py uy

app.py

@@ -1,3 +1,5 @@
+# Final robust app.py for your Hugging Face Space
+
 import gradio as gr
 import tensorflow as tf
 from huggingface_hub import hf_hub_download
@@ -5,7 +7,7 @@ import numpy as np
 from PIL import Image
 import os
 
-# --- 1. Load the Model from your other Hugging Face Repo ---
+# --- 1. Load the Model ---
 model = None
 try:
     model_path = hf_hub_download(
@@ -16,11 +18,9 @@ try:
     print("--- MODEL LOADED SUCCESSFULLY! ---")
 except Exception as e:
     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 ---
+# --- 2. Pre-processing Logic ---
 def preprocess_image(img_pil):
     img = img_pil.resize((224, 224))
     img_array = np.array(img)
@@ -32,26 +32,29 @@ def preprocess_image(img_pil):
     img_array = np.expand_dims(img_array, axis=0)
     return img_array
 
-# --- 3. Define the Prediction Function ---
+# --- 3. Prediction Logic ---
 def predict(image_from_gradio):
+    if not isinstance(image_from_gradio, np.ndarray):
+        return {"error": "Invalid input type. Expected an image."}
+
     try:
         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
     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(type="numpy"),
-    outputs="json",
-    title="LeukoLook Eye Detector API",
-    description="API for the LeukoLook project.",
-    api_name="predict"  # <--- ADD THIS LINE
-).launch()
+# --- 4. Create Gradio Interface using gr.Blocks for stability ---
+with gr.Blocks() as demo:
+    gr.Interface(
+        fn=predict,
+        inputs=gr.Image(type="numpy"),
+        outputs=gr.JSON(),
+        api_name="predict"
+    )
+
+demo.launch()