import os import gradio as gr import numpy as np import tensorflow as tf from tensorflow.keras.preprocessing.image import load_img, img_to_array import h5py import google.generativeai as genai genai.configure(api_key=os.getenv("GENAI_API_KEY")) # Set up generation configuration generation_config = { "temperature": 1, "top_p": 0.95, "top_k": 0, "max_output_tokens": 8192, } # Safety settings to filter harmful content safety_settings = [ {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"}, {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_MEDIUM_AND_ABOVE"}, {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"}, {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"}, ] # Set system message for the model system_instruction = ( "You are Dr. Bot, a medical assistant specializing in breast cancer. " "Your role is to provide accurate information about breast cancer types, symptoms, " "screening guidelines, treatment options, and supportive resources. " "Offer compassionate support and respond to patient inquiries with empathy and evidence-based information." ) # Load Gemini model model = genai.GenerativeModel( model_name="gemini-1.5-pro-latest", generation_config=generation_config, system_instruction=system_instruction, safety_settings=safety_settings ) # Function to interact with the Gemini model def query_model(input_text): convo = model.start_chat(history=[{"role": "user", "parts": [input_text]}]) response = convo.send_message("YOUR_USER_INPUT") return convo.last.text f = h5py.File("best_model_2.h5", mode="r+") model_config_string = f.attrs.get("model_config") if model_config_string.find('"groups": 1,') != -1: model_config_string = model_config_string.replace('"groups": 1,', '') f.attrs.modify('model_config', model_config_string) f.flush() model_config_string = f.attrs.get("model_config") assert model_config_string.find('"groups": 1,') == -1 f.close() # Load the breast cancer detection model incept_model = tf.keras.models.load_model('best_model_2.h5') fixed_image_url = "breast-cancer-awareness-month-1200x834.jpg" # Example images and their descriptions examples = [ ["malignant.png", "Malignant X-ray image."], ["normal.png", "X-ray image indicating normal."], ["benign.png", "X-ray image showing no signs of benign."] ] IMAGE_SHAPE = (224, 224) classes = ['benign', 'malignant', 'normal'] # Function to prepare the image for prediction def prepare_image(file): img = load_img(file, target_size=IMAGE_SHAPE) img_array = img_to_array(img) img_array = np.expand_dims(img_array, axis=0) return tf.keras.applications.efficientnet.preprocess_input(img_array) # Prediction function for breast cancer detection def predict(file): if file is None: return "Please upload an image.", fixed_image_url img = prepare_image(file) res = incept_model.predict(img) pred_index = np.argmax(res) pred = classes[pred_index] # Specific advice for each prediction if pred == 'malignant': advice = "As a healthcare professional, I recommend immediate further evaluation. Malignant findings can indicate the presence of cancer. Please consult a specialist." elif pred == 'benign': advice = "The results show benign characteristics, which is a positive outcome. This means there are no cancerous cells. However, it’s essential to have regular follow-ups with your healthcare provider to ensure that there are no changes over time." else: # pred == 'normal' advice = "The results appear normal. Continue with regular check-ups and maintain a healthy lifestyle." return advice, fixed_image_url # Function to provide project information def show_info(): return ( "

πŸŽ—οΈ Welcome to Our Breast Cancer System πŸŽ—οΈ

\n\n" "Breast cancer is one of the most common causes of death among women worldwide.\n\n " "Early detection plays a crucial role in reducing mortality rates.\n\n " "This project includes two main components:\n\n" "- **Ultrasound Image Classification**: \n\n We classify breast ultrasound images into three categories: normal, benign, and malignant. \n\n" "The dataset consists of 780 ultrasound images collected in 2018 from 600 female patients, aged 25 to 75. \n\n " " Each image is in PNG format with an average size of 500x500 pixels.\n\n\n" "- **Breast Cancer Information Chatbot**: \n\n Our chatbot is designed to provide reliable information and answer questions about breast cancer, helping users to understand the disease better.\n\n\n" "For additional assistance, you can interact with our chatbot or upload images for classification." ) # Create the Gradio interface for both functionalities chatbot_interface = gr.Interface( fn=query_model, inputs=gr.Textbox(label="Enter your question about breast cancer:", placeholder="e.g., What are the symptoms of breast cancer?", lines=2), outputs=gr.Textbox(label="Response:", placeholder="Your answer will appear here..."), title="Breast Cancer Chatbot πŸŽ—οΈ", description="Ask your questions related to breast cancer. Our chatbot provides information and guidance based on your inquiries.", ) breast_cancer_interface = gr.Interface( fn=predict, inputs=gr.Image(type="filepath", label="Upload an Image"), outputs=[ gr.Textbox(label="Prediction"), gr.Image(label="Your Partner in Breast Health Awareness πŸŽ—οΈ", value=fixed_image_url) ], title="Breast Cancer Detection", description="Predicting Your Breast Health: Is it Benign, Malignant, or Normal?", examples=examples, ) # Create the information display as a separate Markdown element info_markdown = gr.Markdown(show_info()) # Combine interfaces into a themed Blocks app with gr.Blocks(theme=gr.themes.Default(primary_hue=gr.themes.colors.pink, secondary_hue=gr.themes.colors.neutral)) as demo: combined_interface = gr.TabbedInterface( [info_markdown,chatbot_interface, breast_cancer_interface], ["Project Information","Breast Cancer Chatbot", "Breast Cancer Detection"] ) # Launch the combined interface if __name__ == "__main__": demo.launch(debug=True)