import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
import cv2
from tensorflow.keras.models import model_from_json
import os

print("✅ Gradio app: Starting up...")

# --- LOAD THE TRAINED MODEL ---
# Model architecture and weights are expected in the same directory as the app.py script
model_architecture_path = './model_architecture.json'
model_weights_path = './model_weights.weights.h5'

model = None # Initialize model as None

try:
    print("✅ Gradio app: Attempting to load model from JSON and H5 weights...")
    if os.path.exists(model_architecture_path) and os.path.exists(model_weights_path):
        with open(model_architecture_path, 'r') as json_file:
            loaded_model_json = json_file.read()
        # Need custom objects if your model uses them (e.g., custom layers)
        # For this specific EfficientNet-like structure, standard layers might suffice
        # If you have custom layers, you'll need custom_objects parameter
        model = model_from_json(loaded_model_json)
        model.load_weights(model_weights_path)
        print("✅ Gradio app: Model loaded successfully from JSON and H5 weights")
    else:
         print("❌ Gradio app: Model architecture or H5 weights file not found.")


except Exception as load_e:
    print(f"❌ Gradio app: Error loading model: {load_e}")
    model = None # Ensure model is None on failure


if model is None:
    print("🛑 Gradio app: Model could not be loaded. Prediction function will not work.")

# --- PREDICTION FUNCTION FOR GRADIO ---
def predict_cataract(image):
    """Predict cataract with the loaded model"""
    if model is None:
        return "Error: Model could not be loaded. Cannot make prediction."

    try:
        # Preprocess image
        img_array = np.array(image)
        # Ensure image is in RGB format if it's grayscale or RGBA
        if img_array.shape[-1] == 4:
             img_array = cv2.cvtColor(img_array, cv2.COLOR_RGBA2RGB)
        elif len(img_array.shape) == 2:
             img_array = cv2.cvtColor(img_array, cv2.COLOR_GRAY2RGB)

        img_array = cv2.resize(img_array, (224, 224))
        img_array = img_array.astype('float32') / 255.0
        img_array = np.expand_dims(img_array, axis=0)

        # Make prediction
        prediction = model.predict(img_array)[0][0]

        # Convert to percentage and class
        probability = float(prediction)
        class_name = "Normal" if probability < 0.5 else "Cataract"
        # Calculate confidence based on which class was predicted
        confidence = probability if class_name == "Cataract" else (1 - probability)
        confidence_percent = confidence * 100


        result = f"""
        🚨 **OVERFITTED MODEL WARNING** 🚨
        This model has data leakage - results are unreliable!

        📊 **Prediction**: {class_name}
        📈 **Confidence**: {confidence_percent:.1f}%
        🎭 **Raw Score**: {probability:.4f}

        ⚠️ **Do NOT trust these results for medical decisions!**
        This is for educational demonstration only.
        """

        return result

    except Exception as e:
        return f"Error during prediction: {str(e)}"

# Create Gradio interface
demo = gr.Interface(
    fn=predict_cataract,
    inputs=gr.Image(type="pil", label="Upload Eye Image"),
    outputs=gr.Textbox(label="Overfitted Prediction (Unreliable!)"),
    title="👁️ Cataract Detection - OVERFITTED BEAST 🚨",
    description="""
    **⚠️ WARNING: This model has intentional data leakage!**

    This demonstrates what happens when ML models "cheat" by seeing the same data during training and validation.
    The high accuracy (96.7%) is FAKE and doesn't represent real medical AI capability.

    🎯 **Educational Purpose**: Show the importance of proper data splitting in medical AI.
    🏥 **Real Medical AI**: Typically achieves 80-90% accuracy with proper validation.
    """,
    # Add example images (you'll need to upload example images to the repo)
    # Make sure example images are in the 'hf_model_overfitted' directory before upload
    examples=[], # Add example paths here, e.g., ["example1.jpg", "example2.jpg"]
    theme=gr.themes.Soft()
)

if __name__ == "__main__":
    print("
🚀 Gradio app: Launching interface...")
    demo.launch()