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| # Final app.py for your Hugging Face Space | |
| import gradio as gr | |
| 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" | |
| ) | |
| 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 ---") | |
| # --- 2. Define the Pre-processing Logic --- | |
| def preprocess_image(img_pil): | |
| 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 | |
| # --- 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.") | |
| try: | |
| pil_image = Image.fromarray(image_array.astype('uint8'), 'RGB') | |
| processed_image = preprocess_image(pil_image) | |
| prediction = model.predict(processed_image) | |
| 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", | |
| description="API for the LeukoLook project." | |
| ).launch() |