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| import gradio as gr | |
| import tensorflow as tf | |
| from PIL import Image | |
| import numpy as np | |
| import tensorflow as tf | |
| import keras.backend as K | |
| # Define focal loss | |
| def focal_loss(gamma=2., alpha=0.25): | |
| def focal_loss_fixed(y_true, y_pred): | |
| y_pred = tf.clip_by_value(y_pred, K.epsilon(), 1. - K.epsilon()) | |
| cross_entropy = -y_true * tf.math.log(y_pred) | |
| weight = alpha * y_true * tf.math.pow((1 - y_pred), gamma) | |
| loss = weight * cross_entropy | |
| return tf.reduce_sum(loss, axis=-1) | |
| return focal_loss_fixed | |
| # Load models | |
| vgg16_model = tf.keras.models.load_model( | |
| "vgg16_best_model.keras" | |
| ) | |
| xception_model = tf.keras.models.load_model( | |
| 'xception_best.keras', | |
| custom_objects={'focal_loss_fixed': focal_loss()} | |
| ) | |
| def predict_fire(image): | |
| img = Image.fromarray(image).convert("RGB") | |
| # Preprocess for vgg16_model (128x128 input size) | |
| vgg16_img = img.resize((128, 128)) | |
| vgg16_img_array = np.array(vgg16_img) / 255.0 | |
| vgg16_img_array = np.expand_dims(vgg16_img_array, axis=0) | |
| # Fire detection using vgg16_model | |
| fire_pred = vgg16_model.predict(vgg16_img_array) | |
| fire_status = "Fire Detected" if fire_pred[0][0] > 0.5 else "No Fire Detected" | |
| # If fire is detected, preprocess for xception_model (224x224 input size) | |
| if fire_status == "Fire Detected": | |
| xception_img = img.resize((224, 224)) | |
| xception_img_array = np.array(xception_img) / 255.0 | |
| xception_img_array = np.expand_dims(xception_img_array, axis=0) | |
| # Severity prediction using xception_model | |
| severity_pred = xception_model.predict(xception_img_array) | |
| severity_level = np.argmax(severity_pred[0]) | |
| severity = ["Mild", "Moderate", "Severe"][severity_level] | |
| # Static rule-based recommendations with detailed instructions | |
| if severity == "Mild": | |
| recommendation = ( | |
| "Fire detected is mild and manageable. " | |
| "For the Fire Department: Ensure continuous monitoring of the fire. " | |
| "Deploy fire trucks and extinguishing equipment if necessary to prevent escalation. " | |
| "For the Public: Stay alert and stay indoors. Evacuate only if advised by authorities. " | |
| "Ensure clear access routes for emergency services. " | |
| "Keep fire safety equipment such as fire extinguishers readily available." | |
| ) | |
| elif severity == "Moderate": | |
| recommendation = ( | |
| "Fire detected is moderate and poses a significant risk. " | |
| "For the Fire Department: Immediate response is needed. " | |
| "Deploy sufficient fire trucks, helicopters (if possible), and personnel to contain the fire. " | |
| "Establish firebreaks and coordinate with neighboring departments. " | |
| "For the Public: Evacuate the area promptly as the fire might spread. " | |
| "Follow evacuation routes and do not return to the area until authorities deem it safe. " | |
| "Be cautious of smoke inhalation, and wear protective masks if available." | |
| ) | |
| else: # Severe | |
| recommendation = ( | |
| "Severe fire detected with rapid spread potential. Immediate action is critical. " | |
| "For the Fire Department: Prioritize evacuation operations. " | |
| "Deploy all available resources, including specialized teams and air support. " | |
| "Set up perimeters around the affected area and prevent access. " | |
| "Coordinate with national agencies for additional resources and backup. " | |
| "For the Public: Evacuate immediately. Leave all belongings behind and proceed to designated safe zones. " | |
| "Avoid smoke exposure and keep away from fire zones. Follow all official instructions and do not attempt to return to the area until clearance is given by emergency services. " | |
| "Remain in contact with local authorities for further updates." | |
| ) | |
| else: | |
| severity = "N/A" | |
| recommendation = ( | |
| "No fire detected. However, always be cautious of any unusual smoke or smells in your environment. " | |
| "Ensure that fire alarms are functioning, and regularly check fire extinguishers. " | |
| "Stay prepared by familiarizing yourself with fire evacuation routes and emergency contact numbers." | |
| ) | |
| return fire_status, severity, recommendation | |
| # Gradio interface | |
| interface = gr.Interface( | |
| fn=predict_fire, | |
| inputs=gr.Image(type="numpy", label="Upload Image"), | |
| outputs=[ | |
| gr.Textbox(label="Fire Status"), | |
| gr.Textbox(label="Severity Level"), | |
| gr.Textbox(label="Recommendation") | |
| ], | |
| title="Fire Prediction and Severity Classification", | |
| description="Upload an image to predict fire and its severity level (Mild, Moderate, Severe), and get recommendations.", | |
| ) | |
| if __name__ == "__main__": | |
| interface.launch() | |