TBO Oracle - HuggingFace Space

README.md

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+---
+title: TBO Oracle
+emoji: 🔮
+colorFrom: green
+colorTo: blue
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: true
+license: mit
+short_description: Temporal Bispectral Operator for future predictions
+tags:
+  - prediction
+  - bispectral-analysis
+  - blockchain
+  - retrocausal
+  - tsvf
+  - oracle
+---
+
+# 🔮 TBO Oracle
+
+**Temporal Bispectral Operator — Blockchain-Anchored Predictions**
+
+> *"The shape is the oracle — we reveal, not compute."*
+
+## What is TBO Oracle?
+
+TBO Oracle is a prediction system based on **bispectral analysis** and **Two-State Vector Formalism (TSVF)**. It detects retrocausal signals in independent noise sources to make predictions about future events.
+
+### Core Concepts
+
+- **Bispectral Analysis:** Detects third-order phase coupling invisible to standard power spectrum analysis
+- **H₂=0 Topology:** Manifold closure condition ensuring prediction convergence
+- **TSVF:** Two-State Vector Formalism from quantum mechanics for retrocausal computation
+- **Dissipative Channels:** Independent noise sources become computational assets
+
+### How It Works
+
+1. **Signal Collection:** Gather data from 4+ independent sources (entropy, clock jitter, hash chains, cyclotomic patterns)
+2. **Bispectral Analysis:** Compute cross-bispectrum B(ω₁, ω₂) for each source
+3. **Z-Score Computation:** Compare observed TBO scalar against null distribution
+4. **Consensus:** Sources showing DEFICIT (z < -1.96) vote YES; majority determines prediction
+5. **Topological Gate:** H₂=0 verification ensures simply-connected deficit structure
+
+### Mathematical Foundation
+
+The TBO scalar is computed as:
+```
+Λ = (1/M) Σ |B(ω₁, ω₂)|
+```
+over the principal domain of the bispectrum.
+
+Z-score relative to permutation null:
+```
+z = (Λ_observed - μ_null) / σ_null
+```
+
+Classification:
+- **DEFICIT** (z < -1.96): Retrocausal signal detected
+- **NORMAL** (-1.96 ≤ z ≤ 1.96): No significant asymmetry
+- **EXCESS** (z > 1.96): Anomalous coupling
+
+## Track Record
+
+| Date | Question | Prediction | Outcome |
+|------|----------|------------|---------|
+| Feb 2026 | Canada wins Hockey Gold | NO | ✅ USA won |
+| Mar 2026 | BTC >$100k | YES | ⏳ Pending |
+
+## Resources
+
+- 📄 **Paper:** [On-Chain (BSV)](https://plugins.whatsonchain.com/api/plugin/main/657b8e90425aeed06b435a16cc759c1d594308bd815535b1628a2df7bbc75c23/0)
+- 🔗 **GitHub:** [OriginNeuralAI/Oracle](https://github.com/OriginNeuralAI/Oracle)
+- 🧠 **Company:** [SmartLedger Solutions](https://smartledger.solutions)
+
+## Citation
+
+```bibtex
+@software{tbo_oracle_2026,
+  author = {Daugherty, Bryan},
+  title = {TBO Oracle: Temporal Bispectral Operator for Retrocausal Prediction},
+  year = {2026},
+  publisher = {SmartLedger Solutions},
+  url = {https://github.com/OriginNeuralAI/Oracle}
+}
+```
+
+---
+
+*Built by Bryan Daugherty | SmartLedger Solutions | 2026*

app.py

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+"""
+TBO Oracle - Hugging Face Space
+================================
+Temporal Bispectral Operator for blockchain-anchored predictions.
+
+"THE SHAPE IS THE ORACLE" - We reveal, not compute.
+"""
+
+import gradio as gr
+import numpy as np
+import hashlib
+import time
+from datetime import datetime, timezone
+from scipy import stats
+
+# ═══════════════════════════════════════════════════════════════════════════
+# TBO CORE FUNCTIONS
+# ═══════════════════════════════════════════════════════════════════════════
+
+def compute_cross_bispectrum_fast(signal, nfft=None):
+    """Vectorized bispectrum computation."""
+    signal = np.asarray(signal, dtype=np.float64)
+    N = len(signal)
+    if nfft is None:
+        nfft = N
+    X = np.fft.fft(signal, n=nfft)
+    idx = np.arange(nfft)
+    i_grid, j_grid = np.meshgrid(idx, idx, indexing='ij')
+    k_grid = (i_grid + j_grid) % nfft
+    B = X[i_grid] * X[j_grid] * np.conj(X[k_grid])
+    return B
+
+
+def compute_tbo_scalar(signal, nfft=None):
+    """Compute the TBO scalar Lambda."""
+    signal = np.asarray(signal, dtype=np.float64)
+    N = len(signal)
+    if nfft is None:
+        nfft = N
+    B = compute_cross_bispectrum_fast(signal, nfft=nfft)
+    half = nfft // 2
+    mask = np.zeros((nfft, nfft), dtype=bool)
+    for i in range(1, half):
+        for j in range(1, half):
+            if i + j < half:
+                mask[i, j] = True
+    magnitudes = np.abs(B[mask])
+    if len(magnitudes) == 0:
+        return 0.0
+    return float(np.mean(magnitudes))
+
+
+def compute_tbo_zscore(signal, n_null=100, seed=None):
+    """Compute z-score relative to null distribution."""
+    rng = np.random.default_rng(seed)
+    signal = np.asarray(signal, dtype=np.float64)
+    lam_obs = compute_tbo_scalar(signal)
+    null_lambdas = np.empty(n_null)
+    for i in range(n_null):
+        perm = rng.permutation(signal)
+        null_lambdas[i] = compute_tbo_scalar(perm)
+    mu = np.mean(null_lambdas)
+    sigma = np.std(null_lambdas, ddof=1)
+    if sigma < 1e-15:
+        sigma = 1e-15
+    z = (lam_obs - mu) / sigma
+    return float(z), float(lam_obs), float(mu), float(sigma), null_lambdas
+
+
+def classify_signal(z_score):
+    """Classify based on z-score."""
+    if z_score < -1.96:
+        return 'DEFICIT'
+    elif z_score > 1.96:
+        return 'EXCESS'
+    return 'NORMAL'
+
+
+def _sieve_primes(limit):
+    """Sieve of Eratosthenes."""
+    is_prime = [True] * (limit + 1)
+    is_prime[0] = is_prime[1] = False
+    for i in range(2, int(limit**0.5) + 1):
+        if is_prime[i]:
+            for j in range(i * i, limit + 1, i):
+                is_prime[j] = False
+    return [i for i in range(2, limit + 1) if is_prime[i]]
+
+
+def generate_cyclotomic(N, order=7, seed=None):
+    """Generate cyclotomic signal with prime harmonics."""
+    rng = np.random.default_rng(seed)
+    t = np.arange(N, dtype=np.float64)
+    primes = _sieve_primes(N // 2)[:order]
+    signal = np.zeros(N)
+    for p in primes:
+        amp = rng.uniform(0.5, 2.0)
+        phase = rng.uniform(0, 2 * np.pi)
+        signal += amp * np.sin(2 * np.pi * p * t / N + phase)
+    signal += 0.1 * rng.standard_normal(N)
+    return signal
+
+
+# ═══════════════════════════════════════════════════════════════════════════
+# SIGNAL COLLECTORS
+# ═══════════════════════════════════════════════════════════════════════════
+
+def collect_entropy(n=256, seed=None):
+    """System entropy signal."""
+    rng = np.random.default_rng(seed)
+    arr = rng.integers(0, 2**32, size=n, dtype=np.uint32).astype(np.float64)
+    return (arr - arr.mean()) / (arr.std() + 1e-15)
+
+
+def collect_clock_jitter(n=256):
+    """Clock timing jitter."""
+    timestamps = []
+    for _ in range(n * 4):
+        timestamps.append(time.perf_counter_ns())
+    deltas = np.diff(timestamps).astype(np.float64)
+    # Subsample
+    block = len(deltas) // n
+    signal = np.array([deltas[i*block:(i+1)*block].mean() for i in range(n)])
+    return (signal - signal.mean()) / (signal.std() + 1e-15)
+
+
+def collect_hash_chain(n=256, seed=None):
+    """SHA-256 hash chain."""
+    rng = np.random.default_rng(seed)
+    h = rng.bytes(32)
+    values = np.empty(n, dtype=np.float64)
+    for i in range(n):
+        h = hashlib.sha256(h + i.to_bytes(4, 'big')).digest()
+        values[i] = float(int.from_bytes(h[:4], 'big'))
+    return (values - values.mean()) / (values.std() + 1e-15)
+
+
+def collect_cyclotomic(n=256, seed=None):
+    """Cyclotomic calibration signal."""
+    signal = generate_cyclotomic(n, order=7, seed=seed)
+    return (signal - signal.mean()) / (signal.std() + 1e-15)
+
+
+# ═══════════════════════════════════════════════════════════════════════════
+# ORACLE PREDICTION
+# ═══════════════════════════════════════════════════════════════════════════
+
+def run_oracle_prediction(question: str, deadline: str, n_samples: int = 256, n_null: int = 100):
+    """Run full TBO Oracle prediction."""
+    
+    # Generate question-based seed
+    q_hash = hashlib.sha256(question.encode()).hexdigest()
+    base_seed = int(q_hash[:8], 16)
+    
+    # Collect signals from 4 sources (simplified for demo)
+    sources = {
+        "entropy": collect_entropy(n_samples, seed=base_seed),
+        "clock": collect_clock_jitter(n_samples),
+        "hash_chain": collect_hash_chain(n_samples, seed=base_seed + 1),
+        "cyclotomic": collect_cyclotomic(n_samples, seed=base_seed + 2),
+    }
+    
+    results = {}
+    predictions = []
+    z_scores = []
+    
+    for name, signal in sources.items():
+        z, lam, mu, sigma, null_dist = compute_tbo_zscore(signal, n_null=n_null, seed=base_seed)
+        classification = classify_signal(z)
+        prediction = 1 if z < -1.96 else 0
+        
+        results[name] = {
+            "z_score": round(z, 4),
+            "lambda": round(lam, 6),
+            "classification": classification,
+            "prediction": "YES" if prediction else "NO",
+        }
+        predictions.append(prediction)
+        z_scores.append(abs(z))
+    
+    # Consensus
+    vote_count = sum(predictions)
+    consensus = "YES" if vote_count >= 2 else "NO"
+    
+    # Confidence
+    mean_z = np.mean(z_scores)
+    if mean_z >= 10:
+        confidence = "HIGH"
+    elif mean_z >= 3:
+        confidence = "MEDIUM"
+    elif mean_z >= 1:
+        confidence = "LOW"
+    else:
+        confidence = "UNCERTAIN"
+    
+    # Probability estimate
+    k = 0.25
+    prob = 1.0 / (1.0 + np.exp(-k * mean_z))
+    prob = max(0.5, min(prob, 0.95))
+    
+    return results, consensus, confidence, prob, vote_count
+
+
+def format_results(question, deadline, results, consensus, confidence, prob, votes):
+    """Format results as markdown."""
+    
+    # Header
+    md = f"""
+# 🔮 TBO Oracle Prediction
+
+**Question:** {question}  
+**Deadline:** {deadline}  
+**Timestamp:** {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S UTC')}
+
+---
+
+## 📊 Prediction Result
+
+| Metric | Value |
+|--------|-------|
+| **Consensus** | **{consensus}** |
+| **Confidence** | {confidence} |
+| **Probability** | {prob:.1%} |
+| **Vote** | {votes}/4 sources |
+
+---
+
+## 🔬 Per-Source Analysis
+
+| Source | Z-Score | Classification | Prediction |
+|--------|---------|----------------|------------|
+"""
+    
+    for name, data in results.items():
+        emoji = "🔴" if data["classification"] == "DEFICIT" else "⚪" if data["classification"] == "NORMAL" else "🟣"
+        md += f"| {name} | {data['z_score']:.2f} | {emoji} {data['classification']} | {data['prediction']} |\n"
+    
+    md += f"""
+---
+
+## 🧬 Methodology
+
+- **Algorithm:** Temporal Bispectral Operator (TBO)
+- **Signal Sources:** 4 independent channels
+- **Null Surrogates:** 100 permutations per source
+- **Threshold:** z < -1.96 (95% confidence deficit)
+
+> *"The shape is the oracle — we reveal, not compute."*
+
+---
+
+📄 **Paper:** [On-Chain (BSV)](https://plugins.whatsonchain.com/api/plugin/main/657b8e90425aeed06b435a16cc759c1d594308bd815535b1628a2df7bbc75c23/0)  
+🔗 **GitHub:** [OriginNeuralAI/Oracle](https://github.com/OriginNeuralAI/Oracle)
+"""
+    
+    return md
+
+
+def predict(question: str, deadline: str, n_samples: int, n_null: int):
+    """Main prediction function for Gradio."""
+    
+    if not question.strip():
+        return "❌ Please enter a question."
+    
+    if not deadline:
+        return "❌ Please select a deadline."
+    
+    try:
+        results, consensus, confidence, prob, votes = run_oracle_prediction(
+            question, deadline, int(n_samples), int(n_null)
+        )
+        return format_results(question, deadline, results, consensus, confidence, prob, votes)
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+
+# ═══════════════════════════════════════════════════════════════════════════
+# GRADIO INTERFACE
+# ═══════════════════════════════════════════════════════════════════════════
+
+EXAMPLES = [
+    ["Will BTC exceed $150k by December 2026?", "2026-12-31", 256, 100],
+    ["Will there be a major AI breakthrough in 2026?", "2026-12-31", 256, 100],
+    ["Will SpaceX land humans on Mars by 2030?", "2030-12-31", 256, 100],
+    ["Will the Fed cut rates in Q2 2026?", "2026-06-30", 256, 100],
+]
+
+with gr.Blocks(
+    title="TBO Oracle",
+    theme=gr.themes.Base(
+        primary_hue="teal",
+        secondary_hue="blue",
+        neutral_hue="slate",
+    ),
+    css="""
+    .gradio-container { max-width: 900px !important; }
+    .gr-button-primary { background: linear-gradient(135deg, #00d4aa, #0088ff) !important; }
+    """
+) as demo:
+    
+    gr.Markdown("""
+    # 🔮 TBO Oracle
+    ### Temporal Bispectral Operator — Blockchain-Anchored Predictions
+    
+    > *"The shape is the oracle — we reveal, not compute."*
+    
+    TBO Oracle uses **bispectral analysis** and **Two-State Vector Formalism (TSVF)** 
+    to detect retrocausal signals in independent noise sources. When sources converge 
+    despite their independence, the topology reveals the answer.
+    
+    ---
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            question = gr.Textbox(
+                label="🎯 Question",
+                placeholder="Ask a binary yes/no question about the future...",
+                lines=2,
+            )
+            deadline = gr.Textbox(
+                label="📅 Deadline",
+                placeholder="YYYY-MM-DD",
+                value="2026-12-31",
+            )
+        
+        with gr.Column(scale=1):
+            n_samples = gr.Slider(
+                label="Signal Length",
+                minimum=64,
+                maximum=512,
+                value=256,
+                step=64,
+            )
+            n_null = gr.Slider(
+                label="Null Surrogates",
+                minimum=50,
+                maximum=200,
+                value=100,
+                step=25,
+            )
+    
+    predict_btn = gr.Button("🔮 Query the Oracle", variant="primary", size="lg")
+    
+    output = gr.Markdown(label="Prediction")
+    
+    predict_btn.click(
+        fn=predict,
+        inputs=[question, deadline, n_samples, n_null],
+        outputs=output,
+    )
+    
+    gr.Examples(
+        examples=EXAMPLES,
+        inputs=[question, deadline, n_samples, n_null],
+    )
+    
+    gr.Markdown("""
+    ---
+    
+    ### 📚 About TBO Oracle
+    
+    **Key Concepts:**
+    - **Bispectral Analysis:** Detects third-order phase coupling invisible to power spectrum
+    - **H₂=0 Topology:** Manifold closure condition for prediction convergence
+    - **TSVF:** Two-State Vector Formalism for retrocausal signal detection
+    - **Dissipative Channels:** Independent noise sources as computational assets
+    
+    **Sources:**
+    - 📄 [On-Chain Paper (BSV)](https://plugins.whatsonchain.com/api/plugin/main/657b8e90425aeed06b435a16cc759c1d594308bd815535b1628a2df7bbc75c23/0)
+    - 🔗 [GitHub Repository](https://github.com/OriginNeuralAI/Oracle)
+    - 🧠 [SmartLedger Solutions](https://smartledger.solutions)
+    
+    ---
+    
+    *Built by Bryan Daugherty | SmartLedger Solutions | 2026*
+    """)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+numpy>=1.24
+scipy>=1.10
+gradio>=4.0