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from flask import Flask, request, render_template, redirect, url_for |
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import os |
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import json |
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from Vit_concept import run_inference, model |
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from GP import genetic_programming |
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app = Flask(__name__) |
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UPLOAD_FOLDER = 'uploads' |
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os.makedirs(UPLOAD_FOLDER, exist_ok=True) |
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@app.route('/') |
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def index(): |
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return render_template('index.html') |
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@app.route('/upload', methods=['POST']) |
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def upload(): |
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if 'file' not in request.files: |
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return "No file part" |
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file = request.files['file'] |
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if file.filename == '': |
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return "No selected file" |
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filepath = os.path.join(UPLOAD_FOLDER, file.filename) |
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file.save(filepath) |
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with open(filepath, 'r') as f: |
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data = json.load(f) |
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input_output_pairs = [] |
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predicted_HLCs = [] |
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for sample in data.get("train", []): |
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input_grid = sample["input"] |
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output_grid = sample["output"] |
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concept_label, _ = run_inference(model, input_grid, output_grid) |
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predicted_HLCs.append(concept_label) |
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input_output_pairs.append((input_grid, output_grid)) |
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predicted_HLCs = list(set(predicted_HLCs)) |
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best_program, generations = genetic_programming( |
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input_output_pairs=input_output_pairs, |
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population_size=300, |
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generations=500, |
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mutation_rate=0.2, |
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crossover_rate=0.7, |
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max_depth=3, |
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predicted_HLCs=predicted_HLCs |
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) |
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return render_template("results.html", hlcs=predicted_HLCs, program=str(best_program)) |
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if __name__ == '__main__': |
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app.run(host="0.0.0.0", port=7860) |
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