fix: add InferenceResult, ModelOutput, ModelRepository, FileUploadResult to __all__

src/opengradient/__init__.py

@@ -1,188 +1 @@
-"""
-OpenGradient Python SDK for decentralized AI inference with end-to-end verification.
 
-## Overview
-
-The OpenGradient SDK provides programmatic access to decentralized AI infrastructure, including:
-
-- **LLM Inference** -- Chat and completion with major LLM providers (OpenAI, Anthropic, Google, xAI) through TEE-verified execution
-- **On-chain Model Inference** -- Run ONNX models via blockchain smart contracts with VANILLA, TEE, or ZKML verification
-- **Model Hub** -- Create, version, and upload ML models to the OpenGradient Model Hub
-
-All LLM inference runs inside Trusted Execution Environments (TEEs) and settles on-chain via the x402 payment protocol, giving you cryptographic proof that inference was performed correctly.
-
-## Quick Start
-
-```python
-import opengradient as og
-
-# Initialize the client
-client = og.init(private_key="0x...")
-
-# One-time approval (idempotent — skips if allowance is already sufficient)
-client.llm.ensure_opg_approval(opg_amount=5)
-
-# Chat with an LLM (TEE-verified)
-response = client.llm.chat(
-    model=og.TEE_LLM.CLAUDE_3_5_HAIKU,
-    messages=[{"role": "user", "content": "Hello!"}],
-    max_tokens=200,
-)
-print(response.chat_output)
-
-# Stream a response
-for chunk in client.llm.chat(
-    model=og.TEE_LLM.GPT_4O,
-    messages=[{"role": "user", "content": "Explain TEE in one paragraph."}],
-    max_tokens=300,
-    stream=True,
-):
-    if chunk.choices[0].delta.content:
-        print(chunk.choices[0].delta.content, end="")
-
-# Run on-chain ONNX model inference
-result = client.alpha.infer(
-    model_cid="your_model_cid",
-    inference_mode=og.InferenceMode.VANILLA,
-    model_input={"input": [1.0, 2.0, 3.0]},
-)
-print(result.model_output)
-```
-
-## Private Keys
-
-The SDK operates across two chains. You can use a single key for both, or provide separate keys:
-
-- **``private_key``** -- pays for LLM inference via x402 on **Base Sepolia** (requires OPG tokens)
-- **``alpha_private_key``** *(optional)* -- pays gas for Alpha Testnet on-chain inference on the **OpenGradient network** (requires testnet gas tokens). Falls back to ``private_key`` when omitted.
-
-```python
-# Separate keys for each chain
-client = og.init(private_key="0xBASE_KEY...", alpha_private_key="0xALPHA_KEY...")
-```
-
-## Client Namespaces
-
-The `opengradient.client.Client` object exposes four namespaces:
-
-- **`opengradient.client.llm`** -- Verifiable LLM chat and completion via TEE-verified execution with x402 payments (Base Sepolia OPG tokens)
-- **`opengradient.client.alpha`** -- On-chain ONNX model inference, workflow deployment, and scheduled ML model execution (OpenGradient testnet gas tokens)
-- **`opengradient.client.model_hub`** -- Model repository management
-- **`opengradient.client.twins`** -- Digital twins chat via OpenGradient verifiable inference (requires twins API key)
-
-## Model Hub (requires email auth)
-
-```python
-client = og.init(
-    private_key="0x...",
-    email="you@example.com",
-    password="...",
-)
-
-repo = client.model_hub.create_model("my-model", "A price prediction model")
-client.model_hub.upload("model.onnx", repo.name, repo.initialVersion)
-```
-
-## Framework Integrations
-
-The SDK includes adapters for popular AI frameworks -- see the `agents` submodule for LangChain and OpenAI integration.
-"""
-
-from typing import Optional
-
-from . import agents, alphasense
-from .client import Client
-from .types import (
-    TEE_LLM,
-    CandleOrder,
-    CandleType,
-    FileUploadResult,
-    HistoricalInputQuery,
-    InferenceMode,
-    InferenceResult,
-    ModelOutput,
-    ModelRepository,
-    SchedulerParams,
-    TextGenerationOutput,
-    TextGenerationStream,
-    x402SettlementMode,
-)
-
-global_client: Optional[Client] = None
-"""Global client instance. Set by calling `init()`."""
-
-
-def init(
-    private_key: str,
-    alpha_private_key: Optional[str] = None,
-    email: Optional[str] = None,
-    password: Optional[str] = None,
-    **kwargs,
-) -> Client:
-    """Initialize the global OpenGradient client.
-
-    This is the recommended way to get started. It creates a `Client` instance
-    and stores it as the global client for convenience.
-
-    Args:
-        private_key: Private key whose wallet holds **Base Sepolia OPG tokens**
-            for x402 LLM payments.
-        alpha_private_key: Private key whose wallet holds **OpenGradient testnet
-            gas tokens** for on-chain inference. Optional -- falls back to
-            ``private_key`` for backward compatibility.
-        email: Email for Model Hub authentication. Optional.
-        password: Password for Model Hub authentication. Optional.
-        **kwargs: Additional arguments forwarded to `Client`.
-
-    Returns:
-        The newly created `Client` instance.
-
-    Usage:
-        import opengradient as og
-        client = og.init(private_key="0x...")
-        client.llm.ensure_opg_approval(opg_amount=5)
-        response = client.llm.chat(model=og.TEE_LLM.GPT_4O, messages=[...])
-    """
-    global global_client
-    global_client = Client(
-        private_key=private_key,
-        alpha_private_key=alpha_private_key,
-        email=email,
-        password=password,
-        **kwargs,
-    )
-    return global_client
-
-
-__all__ = [
-    "Client",
-    "global_client",
-    "init",
-    "TEE_LLM",
-    "InferenceMode",
-    "HistoricalInputQuery",
-    "SchedulerParams",
-    "CandleType",
-    "CandleOrder",
-    "TextGenerationOutput",
-    "TextGenerationStream",
-    "x402SettlementMode",
-    "agents",
-    "alphasense",
-]
-
-__pdoc__ = {
-    "account": False,
-    "cli": False,
-    "client": True,
-    "defaults": False,
-    "agents": True,
-    "alphasense": True,
-    "types": True,
-    # Hide niche types from the top-level page -- they are documented under the types submodule
-    "CandleOrder": False,
-    "CandleType": False,
-    "HistoricalInputQuery": False,
-    "SchedulerParams": False,
-    "global_client": False,
-}