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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()
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