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import os
import tensorflow as tf
from tensorflow.keras.preprocessing.image import load_img, img_to_array
import numpy as np
import gradio as gr
# Force TensorFlow to use CPU
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
# Load the pre-trained model
model = tf.keras.models.load_model("maheshbabu.h5")
# Define class labels
classes = ["Normal", "Cancerous"]
# Prediction function
def predict(image):
try:
# Load and preprocess the image
img = load_img(image, target_size=(224, 224)) # Resize image
img_array = img_to_array(img) / 255.0 # Normalize pixel values
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
# Perform prediction
predictions = model.predict(img_array)
class_idx = np.argmax(predictions[0]) # Get index of highest probability
confidence = predictions[0][class_idx] # Get confidence score
# Modify the output based on the prediction
if classes[class_idx] == "Normal":
return "No Cancer Found"
else:
return "Cancer Found!"
except Exception as e:
return f"Error processing image: {str(e)}"
# Set up the Gradio interface
interface = gr.Interface(
fn=predict,
inputs=gr.Image(type="filepath"), # Use filepath to pass the image path
outputs="text", # Change output type to "text" for customized message
title="Blood Cancer Detection",
description="Upload an image to detect whether it is Normal or Cancerous."
)
if __name__ == "__main__":
interface.launch(server_port=7860, server_name="0.0.0.0", share=True)
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