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import gradio as gr
from peft import AutoPeftModelForCausalLM, PeftConfig
from transformers import AutoTokenizer, AutoConfig
from huggingface_hub import login, snapshot_download
import torch
import os
import json
# Login using secret (secure, no hardcode)
login(os.environ["HF_TOKEN"])
# Model setup (loads once on Space startup)
model_id = "agarkovv/CryptoTrader-LM"
base_model_id = "mistralai/Ministral-8B-Instruct-2410"
MAX_LENGTH = 32768
DEVICE = "cuda" if torch.cuda.is_available() else "cpu" # Use GPU if available (ZeroGPU on HF)
# Download adapter files
adapter_local_dir = snapshot_download(repo_id=model_id)
config_path = os.path.join(adapter_local_dir, "adapter_config.json")
with open(config_path, 'r') as f:
adapter_config = json.load(f)
if 'model_type' in adapter_config:
del adapter_config['model_type']
with open(config_path, 'w') as f:
json.dump(adapter_config, f)
# Download base model config locally to avoid gated access issues
base_local_dir = snapshot_download(repo_id=base_model_id, allow_patterns="config.json")
base_config_path = os.path.join(base_local_dir, "config.json")
base_config = AutoConfig.from_pretrained(base_config_path)
# Load model with explicit base config
token = os.environ["HF_TOKEN"]
model = AutoPeftModelForCausalLM.from_pretrained(
adapter_local_dir,
config=base_config,
token=token
)
tokenizer = AutoTokenizer.from_pretrained(base_model_id, token=token)
model = model.to(DEVICE)
model.eval()
def predict_trading_decision(prompt: str) -> str:
"""Predict daily trading decision (buy, sell, or hold) for BTC or ETH based on news and historical prices.
Args:
prompt: Input prompt containing cryptocurrency news and historical price data (format: [INST]YOUR PROMPT HERE[/INST]).
Returns:
Generated trading decision as text (e.g., 'Buy BTC at $62k').
"""
# Format prompt as required
formatted_prompt = f"[INST]{prompt}[/INST]"
inputs = tokenizer(
formatted_prompt, return_tensors="pt", padding=False, max_length=MAX_LENGTH, truncation=True
)
inputs = {key: value.to(model.device) for key, value in inputs.items()}
res = model.generate(
**inputs,
use_cache=True,
max_new_tokens=MAX_LENGTH,
)
output = tokenizer.decode(res[0], skip_special_tokens=True)
return output
# Gradio Interface
demo = gr.Interface(
fn=predict_trading_decision,
inputs=gr.Textbox(label="Input Prompt (News + Prices)"),
outputs=gr.Textbox(label="Trading Decision"),
title="CryptoTrader-LM MCP Tool",
description="Predict buy/sell/hold for BTC/ETH."
)
# Launch with MCP support
demo.launch(mcp_server=True) |