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9163080 149b6c3 9163080 149b6c3 9163080 149b6c3 9163080 149b6c3 4d30d15 149b6c3 4d30d15 149b6c3 4d30d15 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | import gradio as gr
import numpy as np
from tensorflow.keras.models import load_model
from PIL import Image
# Load the trained model
MODEL_PATH = "vgg19_fine_tuned_block5_91.keras"
model = load_model(MODEL_PATH)
# Define class labels and confidence threshold
CLASS_LABELS = ["NORMAL", "PNEUMONIA"]
CONFIDENCE_THRESHOLD = 0.7
def preprocess_image(image):
img = image.convert("RGB") # Ensure the image is RGB
img = img.resize((128, 128)) # Resize to model's input size
img_array = np.array(img) / 255.0 # Normalize pixel values to [0, 1]
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
return img_array
def predict_image(image):
img_array = preprocess_image(image)
prediction = model.predict(img_array)
confidence = np.max(prediction)
if confidence < CONFIDENCE_THRESHOLD:
return "Uncertain: Low confidence", confidence
predicted_class = CLASS_LABELS[np.argmax(prediction)]
return predicted_class, confidence
def acknowledge(agree):
if agree:
return (
gr.update(visible=True),
"Thank you for acknowledging the disclaimer. You may now use the app.",
)
else:
return gr.update(visible=False), "You must accept the disclaimer to proceed."
# Create a Gradio interface using Blocks
with gr.Blocks() as app:
gr.Markdown(
"**Disclaimer:** This application is a student project developed as part of coursework and is intended solely for educational and experimental purposes. It is not a substitute for professional medical advice, diagnosis, or treatment. The results provided by this application should not be relied upon for medical decision-making. Use at your own discretion."
)
agree = gr.Checkbox(
label="I acknowledge that this application is for experimental use only and not suitable for medical purposes."
)
message = gr.Textbox(interactive=False)
with gr.Row(visible=False) as interface_row:
image_input = gr.Image(type="pil")
submit_button = gr.Button("Submit")
predicted_class = gr.Textbox(label="Predicted Class")
confidence = gr.Textbox(label="Confidence")
submit_button.click(
fn=predict_image, inputs=image_input, outputs=[predicted_class, confidence]
)
agree.change(acknowledge, agree, [interface_row, message])
# Launch the app
if __name__ == "__main__":
app.launch()
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