app.py

app.py

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+import gradio as gr
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+import matplotlib.pyplot as plt
+from io import BytesIO
+
+# Load the trained model
+model = load_model('model1.h5')  # Make sure 'model1.h5' is the correct path to your model
+
+# Prediction function for the Gradio app
+def predict_and_visualize(img):
+    # Store the original image size
+    original_size = img.size
+    
+    # Convert the input image to the target size expected by the model
+    img_resized = img.resize((256, 256))
+    img_array = np.array(img_resized) / 255.0  # Normalize the image
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+
+    # Make a prediction
+    prediction = model.predict(img_array)
+    
+    # Assuming the model outputs a single-channel image, normalize to 0-255 range for display
+    predicted_mask = (prediction[0, :, :, 0] * 255).astype(np.uint8)
+    
+    # Convert the prediction to a PIL image
+    prediction_image = Image.fromarray(predicted_mask, mode='L')  # 'L' mode is for grayscale
+    
+    # Resize the predicted image back to the original image size
+    prediction_image = prediction_image.resize(original_size, Image.NEAREST)
+
+    return prediction_image
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_and_visualize,
+    inputs=gr.Image(type="pil"),  # We expect a PIL Image
+    outputs=gr.Image(type="pil"),  # We will return a PIL Image
+    title="MilitarEye: Military Camouflage Detection",
+    description="Upload an image to see the model's predicted probable camouflage mask position."
+)
+
+# Launch the Gradio app
+iface.launch()

model1.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:77918eb1f7dabb2e27182ee2c02f00f097756308dcbdf3514b4ff3c35302aeba
+size 84387568

requirements.txt

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+tensorflow
+gradio