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import streamlit as st |
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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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st.title(" Concept Guieded GP_ARC Solver") |
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st.write("Upload your ARC task (JSON format) and let the system solve it.") |
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uploaded_file = st.file_uploader("Upload your ARC task JSON file", type=["json"]) |
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if uploaded_file: |
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data = json.load(uploaded_file) |
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input_output_pairs = [] |
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predicted_HLCs = [] |
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st.write("### Running Recognition Module...") |
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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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st.write("#### Input Grid:") |
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st.text(input_grid) |
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st.write("#### Output Grid:") |
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st.text(output_grid) |
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concept_label, _ = run_inference(model, input_grid, output_grid) |
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st.write(f" Predicted Concept: `{concept_label}`") |
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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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st.write("### Predicted High-Level Concepts:", predicted_HLCs) |
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if st.button("Run Genetic Programming"): |
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st.write("Running Genetic Programming... (this may take a few minutes)") |
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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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st.success(" GP Completed!") |
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st.write("### Best Program Found:") |
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st.code(str(best_program)) |
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