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