feat: add Solana token analysis example using OpenGradient LLM inference

examples/solana_token_analysis/README.md

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+# Solana Token Analysis with OpenGradient LLM
+
+Analyzes Solana tokens using verifiable LLM inference on OpenGradient's
+decentralized network. Fetches live data from DexScreener and returns
+a risk assessment with an onchain transaction hash as proof of execution.
+
+## Setup
+
+pip install -r requirements.txt
+
+## Configure
+
+export OG_PRIVATE_KEY="0x..."
+
+## Run
+
+python main.py
+
+## Output
+
+Wallet: 0x...
+Token: BONK | Price: $0.000012
+Liquidity: $2,500,000 | Volume 24h: $890,000
+
+Running verifiable inference on OpenGradient network...
+
+--- Analysis: BONK ---
+TX Hash: 0x...
+BONK shows strong liquidity and healthy buy pressure...

examples/solana_token_analysis/main.py

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+"""
+Solana Token Analysis using OpenGradient Verifiable LLM Inference
+Fetches live token data from DexScreener and analyzes it using
+an LLM running on OpenGradient's decentralized network.
+"""
+
+import os
+import json
+import requests
+import opengradient as og
+
+
+def init_client():
+    private_key = os.environ.get("OG_PRIVATE_KEY")
+    if not private_key:
+        raise ValueError("Please set OG_PRIVATE_KEY environment variable")
+    og.init(private_key=private_key)
+    print(f"Wallet: {og.global_client.wallet_address}")
+
+
+def fetch_token_data(token_address: str) -> dict:
+    """Fetch live Solana token data from DexScreener API."""
+    url = f"https://api.dexscreener.com/latest/dex/tokens/{token_address}"
+    response = requests.get(url, timeout=10)
+    data = response.json()
+    pairs = data.get("pairs", [])
+    if not pairs:
+        return {}
+    pair = pairs[0]
+    return {
+        "symbol": pair.get("baseToken", {}).get("symbol", "UNKNOWN"),
+        "name": pair.get("baseToken", {}).get("name", "UNKNOWN"),
+        "price_usd": pair.get("priceUsd", "0"),
+        "volume_24h": pair.get("volume", {}).get("h24", 0),
+        "liquidity_usd": pair.get("liquidity", {}).get("usd", 0),
+        "price_change_24h": pair.get("priceChange", {}).get("h24", 0),
+        "txns_buys_24h": pair.get("txns", {}).get("h24", {}).get("buys", 0),
+        "txns_sells_24h": pair.get("txns", {}).get("h24", {}).get("sells", 0),
+    }
+
+
+def analyze_token(token_address: str):
+    """Analyze a Solana token using OpenGradient LLM inference."""
+    print(f"\nFetching data for {token_address}...")
+    token_data = fetch_token_data(token_address)
+
+    if not token_data:
+        print("Could not fetch token data.")
+        return
+
+    print(f"Token: {token_data['symbol']} | Price: ${token_data['price_usd']}")
+    print(f"Liquidity: ${token_data['liquidity_usd']:,} | Volume 24h: ${token_data['volume_24h']:,}")
+
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are a Solana DeFi analyst. Analyze the token data provided "
+                "and give a concise risk assessment. Highlight liquidity, volume, "
+                "buy/sell pressure, and any red flags. Be direct and actionable. "
+                "Keep response under 100 words."
+            )
+        },
+        {
+            "role": "user",
+            "content": f"Analyze this Solana token:\n{json.dumps(token_data, indent=2)}"
+        }
+    ]
+
+    print("\nRunning verifiable inference on OpenGradient network...")
+    result = og.global_client.llm.chat(
+        model=og.TEE_LLM.GEMINI_2_0_FLASH,
+        messages=messages,
+        max_tokens=150
+    )
+
+    print(f"\n--- Analysis: {token_data['symbol']} ---")
+    print(f"TX Hash: {result.transaction_hash}")
+    print(f"\n{result.choices[0].message.content}")
+
+
+if __name__ == "__main__":
+    init_client()
+    # BONK token on Solana
+    BONK = "DezXAZ8z7PnrnRJjz3wXBoRgixCa6xjnB7YaB1pPB263"
+    analyze_token(BONK)

examples/solana_token_analysis/requirements.txt

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+opengradient>=0.7.4
+requests>=2.28.0