Spaces:
Sleeping
Sleeping
File size: 2,947 Bytes
c05af84 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | import os
import time
from typing import Dict, Optional
from groq import Groq
from huggingface_hub import InferenceClient
from dotenv import load_dotenv
load_dotenv()
class PersonaExplainer:
def __init__(self):
self.groq_api_key = os.getenv("GROQ_API_KEY")
self.hf_api_token = os.getenv("HF_API_TOKEN")
self.groq_client = Groq(api_key=self.groq_api_key) if self.groq_api_key else None
self.hf_client = InferenceClient(token=self.hf_api_token) if self.hf_api_token else None
def generate_explanation(self, persona: str, stats: Dict[str, float]) -> str:
"""
Generates a humorous/insightful explanation of the wallet's persona.
"""
prompt = self._construct_prompt(persona, stats)
# Try Groq First (Fastest)
if self.groq_client:
try:
return self._call_groq(prompt)
except Exception as e:
print(f"Groq API failed: {e}. Falling back...")
# Fallback to Hugging Face
if self.hf_client:
try:
return self._call_hf(prompt)
except Exception as e:
print(f"HF API failed: {e}.")
return "Analysis unavailable (AI models busy)."
def _construct_prompt(self, persona: str, stats: Dict) -> str:
# Simplify stats for the LLM to avoid token bloat
key_stats = {
"Transactions": int(stats.get('tx_count', 0)),
"NFT Volume (USD)": f"${stats.get('total_nft_volume_usd', 0):,.2f}",
"Gas Spent (ETH)": f"{stats.get('total_gas_spent', 0):.4f}",
"Active Days": int(stats.get('active_days', 0)),
"DEX Trades": int(stats.get('dex_trades', 0))
}
return (
f"You are a crypto analytics bot with a witty, slightly roasting personality. "
f"Analyze this wallet:\n"
f"Persona: {persona}\n"
f"Stats: {key_stats}\n\n"
f"Task: Write a 2-3 sentence 'Roast' or 'Insight' about this user. "
f"Explain WHY they fit this persona based on the stats. "
f"Be specific but concise."
)
def _call_groq(self, prompt: str) -> str:
chat_completion = self.groq_client.chat.completions.create(
messages=[
{"role": "system", "content": "You are a witty crypto analyst."},
{"role": "user", "content": prompt}
],
model="llama-3.1-8b-instant",
temperature=0.7,
max_tokens=200,
)
return chat_completion.choices[0].message.content
def _call_hf(self, prompt: str) -> str:
# Using Mistral-7B-Instruct via HF Inference API
return self.hf_client.text_generation(
prompt,
model="mistralai/Mistral-7B-Instruct-v0.2",
max_new_tokens=200,
temperature=0.7
)
|