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| #Code was Designed and Developed by 'SKAV TECH' Company | |
| 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(images): | |
| results = [] | |
| cancerous_count = 0 | |
| for image in images: | |
| 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 | |
| result = f"{classes[class_idx]} ({confidence:.2f})" | |
| results.append(result) | |
| if classes[class_idx] == "Cancerous": | |
| cancerous_count += 1 | |
| except Exception as e: | |
| results.append(f"Error processing image: {str(e)}") | |
| # Generate final summary | |
| if cancerous_count > 0: | |
| summary = f"Warning: {cancerous_count} out of {len(images)} samples are Cancerous. Please consult a doctor." | |
| else: | |
| summary = "All samples are Normal. No signs of cancer detected." | |
| return results, summary | |
| # Set up the Gradio interface | |
| interface = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(type="filepath", label="Upload Blood Cell Images", tool=None, shape=None, source="upload", multiple=True), # Allow multiple images | |
| outputs=[ | |
| gr.JSON(label="Detailed Results"), | |
| gr.Textbox(label="Final Summary") | |
| ], | |
| title="Enhanced Blood Cancer Detection", | |
| description=( | |
| "Upload 5-10 blood cell images to detect whether they are Normal or Cancerous. " | |
| "The application uses a deep learning model to analyze each sample. " | |
| "[Learn more about early cancer detection](https://www.cancer.org)." | |
| ), | |
| live=True, | |
| theme="compact" | |
| ) | |
| if __name__ == "__main__": | |
| interface.launch(server_port=7860, server_name="0.0.0.0", share=True) | |