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from flask import Flask, request, render_template
import os
import json
import numpy as np
from Vit_concept import run_inference, model
from GP import genetic_programming
app = Flask(__name__)
UPLOAD_FOLDER = 'uploads'
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
def tolist_safe(obj):
return obj.tolist() if isinstance(obj, np.ndarray) else obj
@app.route('/')
def index():
return render_template('index.html')
@app.route('/submit_json', methods=['POST'])
def submit_json():
try:
raw_json = request.form.get('json_input')
data = json.loads(raw_json)
except Exception as e:
return f"Invalid JSON input: {e}"
# Collect training data and predict HLCs
input_output_pairs = []
predicted_HLCs = []
for sample in data.get("train", []):
input_grid = sample["input"]
output_grid = sample["output"]
concept_label, _ = run_inference(model, input_grid, output_grid)
predicted_HLCs.append(concept_label)
input_output_pairs.append((tolist_safe(input_grid), tolist_safe(output_grid)))
predicted_HLCs = list(set(predicted_HLCs))
# GP optimization
best_program, generations = genetic_programming(
input_output_pairs=input_output_pairs,
population_size=200,
generations=200,
mutation_rate=0.2,
crossover_rate=0.7,
max_depth=3,
predicted_HLCs=predicted_HLCs
)
# Evaluate GP program on test inputs
test_pairs = []
predicted_test_outputs = []
for sample in data.get("test", []):
test_input = tolist_safe(sample["input"])
test_output = tolist_safe(sample["output"])
test_pairs.append((test_input, test_output))
try:
pred = tolist_safe(best_program.evaluate(test_input))
except Exception as e:
print(f"Prediction error: {e}")
pred = [["ERROR"]]
predicted_test_outputs.append(pred)
return render_template(
"results.html",
hlcs=predicted_HLCs,
input_output_pairs=input_output_pairs,
test_pairs=test_pairs,
predicted_test_outputs=predicted_test_outputs,
best_program=str(best_program)
)
if __name__ == '__main__':
app.run(host='0.0.0.0', port=7860, debug=False)
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