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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
# Load model
model = tf.keras.models.load_model("cervical_newmodel.h5")
# Define threshold
THRESHOLD = 0.4657
# Preprocessing function (adjust image size based on your model input)
def preprocess_image(image):
image = image.convert("RGB")
image = image.resize((224, 224)) # change to your model's input shape
img_array = np.array(image) / 255.0
img_array = np.expand_dims(img_array, axis=0)
return img_array
# Prediction function
def predict_cervical_image(image):
if image is None:
print("⚠️ Skipped empty call (no image uploaded yet)")
return "Please upload a cervical image first."
img_array = preprocess_image(image)
prediction = model.predict(img_array)[0][0] # assuming output is single sigmoid neuron
if prediction > THRESHOLD:
label = "🧬 Abnormal (Possible Cancerous Condition)"
else:
label = "✅ Normal"
result = {
"Prediction Probability": float(prediction),
"Classification": label
}
return label
# Gradio Interface
interface = gr.Interface(
fn=predict_cervical_image,
inputs=gr.Image(type="pil", label="Upload Cervical Image"),
outputs=gr.Textbox(label="Result"),
title="Cervical Image Classifier",
description="Upload a cervical image to classify it as Normal or Abnormal (cancerous). \
This model uses a threshold of 0.4657 for classification."
)
# Launch app
if __name__ == "__main__":
interface.launch()
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