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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
# Make sure to decorate the custom metric class with @register_keras_serializable
@tf.keras.utils.register_keras_serializable()
class F1Score(tf.keras.metrics.Metric):
def __init__(self, name='f1_score', **kwargs):
super().__init__(name=name, **kwargs)
self.precision = tf.keras.metrics.Precision()
self.recall = tf.keras.metrics.Recall()
def update_state(self, y_true, y_pred, sample_weight=None):
y_pred = tf.round(y_pred)
self.precision.update_state(y_true, y_pred, sample_weight)
self.recall.update_state(y_true, y_pred, sample_weight)
def result(self):
p = self.precision.result()
r = self.recall.result()
return 2 * ((p * r) / (p + r + tf.keras.backend.epsilon()))
def reset_states(self):
self.precision.reset_states()
self.recall.reset_states()
# Load your TensorFlow model
model_path = './ACC0.9322_valACC0.9148_loss0.3592_Epoch83.keras'
model = tf.keras.models.load_model(model_path, custom_objects={'F1Score': F1Score})
def predict(image):
# Preprocess the image to match the input shape of the model
img = Image.fromarray(image.astype('uint8'), 'RGB')
img = img.resize((224, 224)) # Update the size if different
img_array = np.array(img)
img_array = img_array / 255.0 # Normalize if your model expects normalization
img_array = img_array[np.newaxis, ...] # Model expects a batch dimension
# Make prediction
prediction = model.predict(img_array)
pred_class = 'Pneumonia' if prediction[0][0] > 0.5 else 'Normal' # Update based on your model output
return {pred_class: float(prediction[0][0])}
iface = gr.Interface(fn=predict,
inputs=gr.components.Image(image_mode='RGB', label="Upload X-ray Image"),
outputs=gr.components.Label(num_top_classes=2),
title="Pneumonia Detection",
description="Upload an X-ray image to detect pneumonia.")
if __name__ == "__main__":
iface.launch() |