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3c9542a 03aa314 3c9542a e886cde 3c9542a 03aa314 3c9542a 6d15e3c 283a00d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | import gradio as gr
import cv2
import numpy as np
from tensorflow.keras.models import load_model
# Define the class names
class_names = ['AnnualCrop', 'Forest', 'HerbaceousVegetation', 'Highway', 'Industrial', 'Pasture', 'PermanentCrop', 'Residential', 'River', 'SeaLake']
# Function to load the selected model
def load_selected_model(model_name):
if model_name == "ResNet50":
return load_model('resnet.h5')
elif model_name == "CNN":
return load_model('cnn.h5')
else:
raise ValueError("Invalid model name")
# Function to predict the image
def predict_image(image, model_name):
model = load_selected_model(model_name)
img = cv2.resize(image, (64, 64))
img = img / 255.0
img = np.expand_dims(img, axis=0)
predictions = model.predict(img)[0]
predicted_class_index = np.argmax(predictions)
predicted_class_name = class_names[predicted_class_index]
confidence = predictions[predicted_class_index]
# Get the top 3 predictions
top_indices = predictions.argsort()[-3:][::-1]
top_classes = [class_names[i] for i in top_indices]
top_confidences = [predictions[i] for i in top_indices]
# Format the output
result = "Top Predictions:\n"
for i in range(3):
result += f"{top_classes[i]} (Confidence: {top_confidences[i]:.2f})\n"
return result
with gr.Blocks() as demo:
gr.Markdown("# Satellite Image Classifier (CaseOh edition)")
with gr.Row():
with gr.Column():
model_selector = gr.Dropdown(choices=["ResNet50", "CNN"], label="Select Model")
image_input = gr.Image(type="numpy", label="Upload Image")
predict_button = gr.Button("Predict")
image_output = gr.Image(type="numpy", label="Uploaded Image")
prediction_output = gr.Textbox(label="Prediction")
predict_button.click(fn=predict_image, inputs=[image_input, model_selector], outputs=prediction_output)
image_input.change(fn=lambda x: x, inputs=image_input, outputs=image_output)
demo.launch(share=True)
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