Create app.py

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load the trained model
+model = tf.keras.models.load_model("MobileNet_model.h5")  # Ensure the model file is uploaded in the same directory
+
+# Define class names
+class_names = ["Fake", "Low", "Medium", "High"]  # Modify if needed
+
+# Image Preprocessing Function
+img_size = (128, 128)  # Ensure it matches the input size used during training
+
+def preprocess_image(image):
+    image = image.resize(img_size)  # Resize image
+    image = np.array(image) / 255.0  # Normalize as done in ImageDataGenerator (rescale=1./255)
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    return image
+
+# API Endpoint for Prediction
+def predict(image):
+    image = preprocess_image(image)
+    predictions = model.predict(image)
+    predicted_class = np.argmax(predictions, axis=1)[0]  # Get predicted class index
+    confidence_scores = {class_names[i]: float(predictions[0][i]) for i in range(len(class_names))}  # Get probability scores
+    
+    return {"Predicted Class": class_names[predicted_class], "Confidence Scores": confidence_scores}
+
+# Gradio API Interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),  # Accept image as input
+    outputs=gr.JSON(),  # Return JSON response
+    title="Fire Detection API",
+    description="Send an image to classify it into one of four categories: Fake, Low, Medium, or High."
+)
+
+# Launch API
+if __name__ == "__main__":
+    interface.launch(share=True)