From 53c29fe83dd480602f2c94e9723d065d68fddc89 Mon Sep 17 00:00:00 2001
From: Sofia Reyes <sofia.reyes@gmail.com>
Date: Mon, 18 May 2026 21:56:08 +0100
Subject: [PATCH] =?UTF-8?q?feat:=20Python=20SDK=20stub=20=E2=80=94=20one-l?=
 =?UTF-8?q?ine=20inference=20logging=20for=20ML=20frameworks?=
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Adds modeltrace Python SDK scaffolding with ModelTraceClient class,
type stubs for ModelRecord/AuditEvent/RegistryStats, and documented
integration points for stellar-sdk.
---
 sdk/python/modeltrace.py | 143 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 143 insertions(+)
 create mode 100644 sdk/python/modeltrace.py

diff --git a/sdk/python/modeltrace.py b/sdk/python/modeltrace.py
new file mode 100644
index 0000000..9a8db2f
--- /dev/null
+++ b/sdk/python/modeltrace.py
@@ -0,0 +1,143 @@
+"""
+ModelTrace Python SDK
+Provides one-line inference logging for ML frameworks.
+
+Usage:
+    from modeltrace import ModelTraceClient
+    
+    client = ModelTraceClient(
+        rpc_url="https://soroban-testnet.stellar.org",
+        secret_key="S...",
+        contract_id="C..."
+    )
+    
+    # Register a model
+    model_id = client.register_model(
+        name="MyLLM-v1",
+        version="1.0.0",
+        weights_hash="sha256:abc123...",
+        training_data_cid="QmXzY7bN9..."
+    )
+    
+    # Log an inference
+    event_id = client.log_inference(
+        model_id=model_id,
+        payload_hash="sha256:def456...",
+        risk_level="LOW"
+    )
+"""
+
+from typing import Optional, Literal
+
+RiskLevel = Literal["LOW", "MEDIUM", "HIGH", "CRITICAL"]
+
+
+class ModelRecord:
+    def __init__(self, model_id: int, name: str, version: str,
+                 weights_hash: str, training_data_cid: str,
+                 registered_at: int, risk_level: str):
+        self.model_id = model_id
+        self.name = name
+        self.version = version
+        self.weights_hash = weights_hash
+        self.training_data_cid = training_data_cid
+        self.registered_at = registered_at
+        self.risk_level = risk_level
+
+
+class AuditEvent:
+    def __init__(self, event_id: int, model_id: int, event_type: str,
+                 payload_hash: str, risk_level: str, timestamp: int):
+        self.event_id = event_id
+        self.model_id = model_id
+        self.event_type = event_type
+        self.payload_hash = payload_hash
+        self.risk_level = risk_level
+        self.timestamp = timestamp
+
+
+class RegistryStats:
+    def __init__(self, total_models: int, total_events: int, high_risk_flags: int):
+        self.total_models = total_models
+        self.total_events = total_events
+        self.high_risk_flags = high_risk_flags
+
+
+class ModelTraceClient:
+    """
+    High-level client for the ModelTrace Audit Registry on Stellar Soroban.
+    """
+
+    def __init__(
+        self,
+        rpc_url: str,
+        secret_key: str,
+        contract_id: str,
+        network_passphrase: str = "Public Global Stellar Network ; September 2015",
+    ):
+        self.rpc_url = rpc_url
+        self.contract_id = contract_id
+        self.network_passphrase = network_passphrase
+        # stellar-sdk integration point
+        # In production: initialize stellar_sdk.SorobanServer and Keypair
+
+    def register_model(
+        self,
+        name: str,
+        version: str,
+        weights_hash: str,
+        training_data_cid: str,
+    ) -> int:
+        """
+        Register an AI model in the on-chain Audit Registry.
+
+        Args:
+            name: Human-readable model name (e.g., "GPT-Audit-v2")
+            version: Semantic version string (e.g., "2.1.0")
+            weights_hash: SHA-256 hash of the model weights file
+            training_data_cid: IPFS CID of the training dataset manifest
+
+        Returns:
+            model_id: On-chain ID for subsequent event logging
+        """
+        raise NotImplementedError(
+            "Install stellar-sdk and implement Soroban transaction builder. "
+            "See docs/sdk-setup.md for detailed instructions."
+        )
+
+    def log_inference(
+        self,
+        model_id: int,
+        payload_hash: str,
+        risk_level: RiskLevel = "LOW",
+    ) -> int:
+        """
+        Log an inference event for an already-registered model.
+
+        Args:
+            model_id: The on-chain ID from register_model()
+            payload_hash: SHA-256 hash of the inference input+output pair
+            risk_level: Assessed risk level of this inference
+
+        Returns:
+            event_id: On-chain event ID for this inference log
+        """
+        raise NotImplementedError(
+            "Install stellar-sdk and implement Soroban transaction builder."
+        )
+
+    def flag_bias(self, model_id: int, evidence_hash: str) -> int:
+        """
+        Flag a bias incident for a model with cryptographic evidence.
+        """
+        raise NotImplementedError(
+            "Install stellar-sdk and implement Soroban transaction builder."
+        )
+
+    def get_model(self, model_id: int) -> ModelRecord:
+        """Query model record from on-chain registry."""
+        raise NotImplementedError()
+
+    def get_stats(self) -> RegistryStats:
+        """Get aggregate protocol statistics."""
+        raise NotImplementedError()