Upload app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Load models
+vgg16_model = tf.keras.models.load_model(
+    "/content/drive/MyDrive/Deep Learning Project/vgg16_best_model.keras"
+)
+xception_model = tf.keras.models.load_model(
+    "/content/drive/MyDrive/Deep Learning Project/Tri Classification/xception_best.keras"
+)
+
+
+def predict_fire(image):
+    img = Image.fromarray(image).convert("RGB")
+    img = img.resize((224, 224))  # Match model input size
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    fire_pred = vgg16_model.predict(img_array)
+    fire_status = "Fire Detected" if fire_pred[0][0] > 0.5 else "No Fire Detected"
+
+    if fire_status == "Fire Detected":
+        severity_pred = xception_model.predict(img_array)
+        severity_level = np.argmax(severity_pred[0])
+        severity = ["Mild", "Moderate", "Severe"][severity_level]
+    else:
+        severity = "N/A"
+
+    return fire_status, severity
+
+
+# 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"),
+    ],
+    title="Fire Prediction and Severity Classification",
+    description="Upload an image to predict fire and its severity level (Mild, Moderate, Severe).",
+)
+
+if __name__ == "__main__":
+    interface.launch()