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