Deploy Intertextual Reference Network Space - chirho

app.py

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+# 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()

requirements.txt

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+# 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
+
+transformers>=4.40
+sentence-transformers>=3.0
+torch>=2.3
+gradio>=4.0
+numpy
+huggingface-hub>=0.23