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""" |
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app.py - HuggingFace Space for the Intertextual Reference Network. |
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Loads embedder + classifier from HuggingFace Hub for cross-reference discovery. |
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""" |
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import json |
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import gradio as gr |
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import numpy as np |
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import torch |
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from sentence_transformers import SentenceTransformer |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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EMBEDDER_ID_CHIRHO = "LoveJesus/intertextual-embedder-chirho" |
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CLASSIFIER_ID_CHIRHO = "LoveJesus/intertextual-classifier-chirho" |
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DATASET_ID_CHIRHO = "LoveJesus/intertextual-dataset-chirho" |
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LABELS_CHIRHO = [ |
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"direct_quote", "allusion", "thematic_parallel", "typological", |
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"prophecy_fulfillment", "parallel_narrative", "contrast", |
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] |
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LABEL_DISPLAY_CHIRHO = { |
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"direct_quote": "Direct Quote", |
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"allusion": "Allusion", |
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"thematic_parallel": "Thematic Parallel", |
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"typological": "Typological", |
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"prophecy_fulfillment": "Prophecy Fulfillment", |
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"parallel_narrative": "Parallel Narrative", |
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"contrast": "Contrast", |
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} |
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embedder_chirho = None |
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classifier_chirho = None |
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classifier_tokenizer_chirho = None |
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verse_ids_chirho = [] |
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verse_texts_chirho = [] |
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verse_embeddings_chirho = None |
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device_chirho = None |
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def load_models_chirho(): |
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"""Load both models from HuggingFace Hub.""" |
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global embedder_chirho, classifier_chirho, classifier_tokenizer_chirho |
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global verse_ids_chirho, verse_texts_chirho, verse_embeddings_chirho, device_chirho |
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if torch.cuda.is_available(): |
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device_chirho = torch.device("cuda") |
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elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available(): |
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device_chirho = torch.device("mps") |
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else: |
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device_chirho = torch.device("cpu") |
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print(f"Using device: {device_chirho}") |
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print("Loading embedder...") |
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embedder_chirho = SentenceTransformer(EMBEDDER_ID_CHIRHO, device=str(device_chirho)) |
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print("Loading classifier...") |
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classifier_tokenizer_chirho = AutoTokenizer.from_pretrained(CLASSIFIER_ID_CHIRHO) |
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classifier_chirho = AutoModelForSequenceClassification.from_pretrained(CLASSIFIER_ID_CHIRHO) |
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classifier_chirho.to(device_chirho) |
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classifier_chirho.eval() |
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print("Loading verse map...") |
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try: |
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from huggingface_hub import hf_hub_download |
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verse_map_path_chirho = hf_hub_download( |
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repo_id=DATASET_ID_CHIRHO, |
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filename="verse-map-chirho.json", |
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repo_type="dataset", |
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) |
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with open(verse_map_path_chirho, "r") as f_chirho: |
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verse_map_chirho = json.load(f_chirho) |
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verse_ids_chirho = list(verse_map_chirho.keys()) |
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verse_texts_chirho = list(verse_map_chirho.values()) |
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print(f" Loaded {len(verse_ids_chirho)} verses") |
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print("Encoding all verses (this takes a moment)...") |
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verse_embeddings_chirho = embedder_chirho.encode( |
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verse_texts_chirho, |
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batch_size=256, |
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show_progress_bar=True, |
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convert_to_numpy=True, |
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normalize_embeddings=True, |
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) |
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print(" Index built!") |
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except Exception as e_chirho: |
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print(f" Warning: Could not load verse map: {e_chirho}") |
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print(" Find References tab will be unavailable.") |
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print("All models loaded!") |
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def classify_pair_chirho(text_a_chirho: str, text_b_chirho: str) -> dict: |
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"""Classify connection type between two texts.""" |
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inputs_chirho = classifier_tokenizer_chirho( |
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text_a_chirho, text_b_chirho, |
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return_tensors="pt", truncation=True, max_length=256, padding=True, |
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) |
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inputs_chirho = {k_chirho: v_chirho.to(device_chirho) for k_chirho, v_chirho in inputs_chirho.items()} |
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with torch.no_grad(): |
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outputs_chirho = classifier_chirho(**inputs_chirho) |
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probs_chirho = torch.softmax(outputs_chirho.logits, dim=-1).cpu().numpy()[0] |
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pred_idx_chirho = int(np.argmax(probs_chirho)) |
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return { |
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"type_chirho": LABELS_CHIRHO[pred_idx_chirho], |
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"confidence_chirho": float(probs_chirho[pred_idx_chirho]), |
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"scores_chirho": {LABELS_CHIRHO[i_chirho]: float(probs_chirho[i_chirho]) for i_chirho in range(len(LABELS_CHIRHO))}, |
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} |
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def find_references_tab_chirho(query_text_chirho: str, k_chirho: int = 10) -> str: |
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"""Tab 1: Find cross-references for a verse.""" |
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if not query_text_chirho.strip(): |
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return "Please enter a verse text." |
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if verse_embeddings_chirho is None: |
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return "Verse index not available. Please try again later." |
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query_emb_chirho = embedder_chirho.encode( |
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[query_text_chirho], normalize_embeddings=True, convert_to_numpy=True |
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)[0] |
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similarities_chirho = np.dot(verse_embeddings_chirho, query_emb_chirho) |
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top_indices_chirho = np.argsort(similarities_chirho)[::-1][:int(k_chirho)] |
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lines_chirho = ["| # | Verse | Similarity | Connection Type | Confidence |", "| --- | --- | --- | --- | --- |"] |
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for rank_chirho, idx_chirho in enumerate(top_indices_chirho, 1): |
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verse_id_chirho = verse_ids_chirho[idx_chirho] |
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verse_text_chirho = verse_texts_chirho[idx_chirho][:80] |
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sim_chirho = float(similarities_chirho[idx_chirho]) |
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cls_chirho = classify_pair_chirho(query_text_chirho, verse_texts_chirho[idx_chirho]) |
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type_display_chirho = LABEL_DISPLAY_CHIRHO.get(cls_chirho["type_chirho"], cls_chirho["type_chirho"]) |
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lines_chirho.append( |
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f"| {rank_chirho} | **{verse_id_chirho}** {verse_text_chirho}... | {sim_chirho:.3f} | {type_display_chirho} | {cls_chirho['confidence_chirho']:.1%} |" |
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) |
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return "\n".join(lines_chirho) |
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def classify_pair_tab_chirho(text_a_chirho: str, text_b_chirho: str) -> tuple: |
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"""Tab 2: Classify connection between two verses.""" |
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if not text_a_chirho.strip() or not text_b_chirho.strip(): |
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return "Please enter both verses.", "" |
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result_chirho = classify_pair_chirho(text_a_chirho, text_b_chirho) |
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type_display_chirho = LABEL_DISPLAY_CHIRHO.get(result_chirho["type_chirho"], result_chirho["type_chirho"]) |
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main_result_chirho = f"**{type_display_chirho}** (confidence: {result_chirho['confidence_chirho']:.1%})" |
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scores_lines_chirho = ["| Connection Type | Score |", "| --- | --- |"] |
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for type_chirho, score_chirho in sorted(result_chirho["scores_chirho"].items(), key=lambda x: -x[1]): |
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display_chirho = LABEL_DISPLAY_CHIRHO.get(type_chirho, type_chirho) |
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bar_chirho = "=" * int(score_chirho * 20) |
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scores_lines_chirho.append(f"| {display_chirho} | {score_chirho:.3f} {bar_chirho} |") |
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return main_result_chirho, "\n".join(scores_lines_chirho) |
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def explore_tab_chirho(verse_id_chirho: str, k_chirho: int = 10) -> str: |
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"""Tab 3: Explore references for a specific verse ID.""" |
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if not verse_id_chirho.strip(): |
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return "Please enter a verse ID (e.g., Gen.1.1, John.3.16)." |
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if verse_embeddings_chirho is None: |
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return "Verse index not available." |
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verse_id_chirho = verse_id_chirho.strip() |
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if verse_id_chirho in verse_ids_chirho: |
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idx_chirho = verse_ids_chirho.index(verse_id_chirho) |
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query_text_chirho = verse_texts_chirho[idx_chirho] |
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return f"**{verse_id_chirho}**: _{query_text_chirho}_\n\n" + find_references_tab_chirho(query_text_chirho, k_chirho) |
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else: |
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return f"Verse ID '{verse_id_chirho}' not found. Use OSIS format: Gen.1.1, Matt.5.3, Rev.21.1" |
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def build_demo_chirho() -> gr.Blocks: |
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"""Build the Gradio demo.""" |
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with gr.Blocks( |
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title="Intertextual Reference Network - loveJesus/models-chirho", |
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theme=gr.themes.Soft(), |
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) as demo_chirho: |
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gr.Markdown("# Intertextual Reference Network") |
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gr.Markdown( |
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"*For God so loved the world that he gave his only begotten Son, " |
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"that whoever believes in him should not perish but have eternal life. - John 3:16*" |
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) |
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gr.Markdown( |
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"Discover **biblical cross-references** and classify their **connection types** " |
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"using a two-model ML pipeline: MiniLM-L12 embedder + RoBERTa-base classifier. " |
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"Trained on 344,799 cross-reference pairs from the Treasury of Scripture Knowledge." |
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) |
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with gr.Tab("Find References"): |
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query_input_chirho = gr.Textbox( |
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label="Enter verse text", |
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placeholder="In the beginning God created the heaven and the earth.", |
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lines=2, |
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) |
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k_input_chirho = gr.Slider(5, 25, value=10, step=1, label="Number of results") |
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find_btn_chirho = gr.Button("Find Cross-References", variant="primary") |
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find_output_chirho = gr.Markdown() |
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find_btn_chirho.click( |
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find_references_tab_chirho, |
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inputs=[query_input_chirho, k_input_chirho], |
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outputs=[find_output_chirho], |
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) |
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gr.Examples( |
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examples=[ |
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["In the beginning God created the heaven and the earth."], |
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["For God so loved the world, that he gave his only begotten Son, that whosoever believeth in him should not perish, but have everlasting life."], |
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["The LORD is my shepherd; I shall not want."], |
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["But thou, Bethlehem Ephratah, though thou be little among the thousands of Judah, yet out of thee shall he come forth unto me that is to be ruler in Israel."], |
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], |
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inputs=[query_input_chirho], |
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) |
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with gr.Tab("Classify Pair"): |
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pair_a_chirho = gr.Textbox(label="Verse A", lines=2, placeholder="Enter first verse...") |
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pair_b_chirho = gr.Textbox(label="Verse B", lines=2, placeholder="Enter second verse...") |
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classify_btn_chirho = gr.Button("Classify Connection") |
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cls_result_chirho = gr.Markdown(label="Result") |
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cls_scores_chirho = gr.Markdown(label="All Scores") |
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classify_btn_chirho.click( |
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classify_pair_tab_chirho, |
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inputs=[pair_a_chirho, pair_b_chirho], |
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outputs=[cls_result_chirho, cls_scores_chirho], |
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) |
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gr.Examples( |
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examples=[ |
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[ |
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"Therefore the Lord himself shall give you a sign; Behold, a virgin shall conceive, and bear a son, and shall call his name Immanuel.", |
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"Now all this was done, that it might be fulfilled which was spoken of the Lord by the prophet, saying, Behold, a virgin shall be with child.", |
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], |
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[ |
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"The LORD is my shepherd; I shall not want.", |
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"I am the good shepherd: the good shepherd giveth his life for the sheep.", |
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], |
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[ |
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"For as in Adam all die, even so in Christ shall all be made alive.", |
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"Wherefore, as by one man sin entered into the world, and death by sin; and so death passed upon all men, for that all have sinned.", |
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], |
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], |
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inputs=[pair_a_chirho, pair_b_chirho], |
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) |
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with gr.Tab("Explore"): |
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explore_input_chirho = gr.Textbox( |
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label="Verse ID (OSIS format)", |
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placeholder="Gen.1.1, John.3.16, Ps.23.1, Rev.21.1", |
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) |
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explore_k_chirho = gr.Slider(5, 25, value=10, step=1, label="Results") |
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explore_btn_chirho = gr.Button("Explore") |
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explore_output_chirho = gr.Markdown() |
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explore_btn_chirho.click( |
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explore_tab_chirho, |
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inputs=[explore_input_chirho, explore_k_chirho], |
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outputs=[explore_output_chirho], |
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) |
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with gr.Tab("About"): |
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gr.Markdown("""# Intertextual Reference Network |
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## What This Does |
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This AI system discovers **cross-references** between Bible verses and classifies the **type of connection**: |
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| Type | Description | Example | |
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| --- | --- | --- | |
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| **Direct Quote** | NT directly quotes OT | Mt 1:23 quotes Is 7:14 | |
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| **Allusion** | Clear reference without direct quotation | Rev 5:5 alludes to Gen 49:9 | |
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| **Thematic Parallel** | Shared theme or motif | Ps 23 parallels Jn 10 | |
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| **Typological** | OT type foreshadows NT antitype | Isaac sacrifice prefigures Christ | |
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| **Prophecy Fulfillment** | OT prophecy fulfilled in NT | Is 53 fulfilled in Passion | |
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| **Parallel Narrative** | Same event in parallel accounts | Synoptic gospels | |
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| **Contrast** | Deliberate theological contrast | Adam vs Christ (Rom 5) | |
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## Two-Model Pipeline |
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1. **Embedder** (MiniLM-L12) — Encodes verses into semantic space for similarity search |
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2. **Classifier** (RoBERTa-base) — Classifies the connection type between verse pairs |
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## Training Data |
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- **344,799** cross-reference pairs from the Treasury of Scripture Knowledge (OpenBible.info) |
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- **31,102** KJV verses indexed for retrieval |
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- **28,612** Grok-labeled pairs for connection type classification |
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## Important Note |
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This is a **research tool** for exploring biblical intertextuality. Always consult scholarly commentaries and original languages for serious study. |
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--- |
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Built with love for Jesus. Published by [loveJesus](https://huggingface.co/LoveJesus). |
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""") |
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return demo_chirho |
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load_models_chirho() |
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demo_chirho = build_demo_chirho() |
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demo_chirho.launch() |
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|
