39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
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import json
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import random
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import sys
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import time
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def main():
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"""
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This is a placeholder Python script for ML model inference.
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It simulates a model prediction by returning a random result.
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In a real-world application, this script would:
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1. Load a pre-trained machine learning model (e.g., from an .h5 or .pkl file).
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2. Pre-process the input image received from the command-line argument.
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3. Run the image through the model to get a prediction.
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4. Return the prediction results as a JSON string.
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"""
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# Simulate processing time
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time.sleep(1.5)
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# Get image path from command line arguments (for context, not used in this placeholder)
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image_path = sys.argv[1] if len(sys.argv) > 1 else "no_image_provided.jpg"
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# Possible outcomes
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predictions = [
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{"label": "Alzheimer Detected", "confidence": round(random.uniform(0.75, 0.98), 2)},
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{"label": "Healthy", "confidence": round(random.uniform(0.80, 0.99), 2)}
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]
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# Choose a random result to simulate a real prediction
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result = random.choice(predictions)
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# Output the result as a JSON string to be captured by the PHP script
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print(json.dumps(result))
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if __name__ == "__main__":
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# This check ensures the main function runs only when the script is executed directly
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main()
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