Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load the TFLite model
+def load_tflite_model(model_path):
+    interpreter = tf.lite.Interpreter(model_path=model_path)
+    interpreter.allocate_tensors()
+    return interpreter
+
+# Preprocess image for TFLite model (assuming the model expects 224x224 RGB images)
+def preprocess_image(image):
+    image = image.resize((224, 224))  # Resize to model input size
+    image = np.array(image) / 255.0   # Normalize the image
+    image = np.expand_dims(image, axis=0).astype(np.float32)  # Add batch dimension
+    return image
+
+# Run inference with the TFLite model
+def run_inference(interpreter, image):
+    input_details = interpreter.get_input_details()
+    output_details = interpreter.get_output_details()
+    
+    # Set input tensor
+    interpreter.set_tensor(input_details[0]['index'], image)
+    
+    # Run inference
+    interpreter.invoke()
+    
+    # Get the output tensor
+    output_data = interpreter.get_tensor(output_details[0]['index'])
+    
+    return output_data
+
+# Gradio interface function
+def predict(image):
+    interpreter = load_tflite_model("MNv2Flood_cat_Sept2024.tflite")  # Replace with your TFLite model path
+    preprocessed_image = preprocess_image(image)
+    prediction = run_inference(interpreter, preprocessed_image)
+    
+    # Get class probabilities for Flood and NoFlood
+    flood_prob = prediction[0][0]
+    no_flood_prob = prediction[0][1]
+    
+    # Determine the predicted label based on probabilities
+    if flood_prob > no_flood_prob:
+        return {"Flood": float(flood_prob), "NoFlood": float(no_flood_prob)}
+    else:
+        return {"Flood": float(flood_prob), "NoFlood": float(no_flood_prob)}
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=predict,  # Function that runs the model
+    inputs=gr.Image(type="pil"),  # Input is an image, processed as PIL image
+    outputs=gr.Label(num_top_classes=2)  # Outputs a label with the predicted classes
+)
+
+# Launch the app
+interface.launch()