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""" |
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Inference example for Polyjuice MBTI model from Hugging Face |
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Since this is a custom Rust/PyTorch model, it cannot use HF Inference API. |
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Users need to download the model files and use the Rust binary for inference. |
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This script shows how to download and prepare for inference. |
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""" |
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from huggingface_hub import hf_hub_download |
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import os |
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import subprocess |
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import json |
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def download_model(repo_id="ElderRyan/polyjuice", cache_dir="./model_cache"): |
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"""Download model files from Hugging Face""" |
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print(f"Downloading model from {repo_id}...") |
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files_to_download = [ |
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"mlp_weights_multitask.pt", |
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"tfidf_vectorizer_multitask.json", |
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"config.json" |
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] |
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downloaded_paths = {} |
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for filename in files_to_download: |
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print(f" Downloading {filename}...") |
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path = hf_hub_download( |
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repo_id=repo_id, |
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filename=filename, |
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cache_dir=cache_dir |
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) |
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downloaded_paths[filename] = path |
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print(f" ✓ Saved to: {path}") |
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return downloaded_paths |
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def show_usage(): |
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"""Show how to use the downloaded model""" |
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print("\n" + "="*60) |
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print("MODEL DOWNLOADED SUCCESSFULLY") |
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print("="*60) |
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print("\nThis is a Rust-based model. To use it:") |
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print("\n1. Clone the Rust project:") |
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print(" git clone https://github.com/RyanKung/polyjuice") |
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print(" cd polyjuice") |
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print("\n2. Copy downloaded model files:") |
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print(" mkdir -p models") |
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print(" cp <downloaded_path>/mlp_weights_multitask.pt models/") |
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print(" cp <downloaded_path>/tfidf_vectorizer_multitask.json models/") |
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print("\n3. Build and run:") |
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print(" cargo build --release") |
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print(" ./target/release/psycial hybrid predict \"Your text here\"") |
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print("\n" + "="*60) |
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print("\nAlternatively, use the web interface at:") |
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print("https://polyjuice.0xbase.ai") |
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print("="*60 + "\n") |
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def main(): |
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print("\n╔═══════════════════════════════════════════════════════════╗") |
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print("║ Polyjuice MBTI Classifier - Model Download ║") |
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print("╚═══════════════════════════════════════════════════════════╝\n") |
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paths = download_model() |
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with open(paths["config.json"], 'r') as f: |
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config = json.load(f) |
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print("\n" + "="*60) |
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print("MODEL INFORMATION") |
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print("="*60) |
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print(f"Model Type: {config.get('model_type', 'N/A')}") |
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print(f"Input Features: {config.get('input_features', 'N/A')}") |
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print(f"Architecture: {config.get('architecture', 'N/A')}") |
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print(f"\nAccuracy:") |
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acc = config.get('accuracy', {}) |
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print(f" Overall: {acc.get('overall', 'N/A')}%") |
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print(f" E/I: {acc.get('e_i', 'N/A')}%") |
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print(f" S/N: {acc.get('s_n', 'N/A')}%") |
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print(f" T/F: {acc.get('t_f', 'N/A')}%") |
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print(f" J/P: {acc.get('j_p', 'N/A')}%") |
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show_usage() |
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if __name__ == "__main__": |
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main() |
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