from huggingface_hub import hf_hub_download, snapshot_download, login
import pandas as pd
import importlib
import importlib.util
import streamlit as st
import sys
import os
from pathlib import Path
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

#login(token=os.environ.get("HF_TOKEN_LLAMA"))
HF_TOKEN = os.environ.get("HF_TOKEN") #get HF_TOKEN 
login(token=HF_TOKEN)

USER_NAME = os.getenv("USER_NAME", "").strip().strip('"')
PRIVATE_SPACE_NAME = os.getenv("PRIVATE_SPACE_NAME", "").strip().strip('"')

#Construct the repo ID
REPO_ID = f"{USER_NAME}/{PRIVATE_SPACE_NAME}"
REPO_TYPE = "space"

# sys.path.append(repo_dir)

# Download the entire space, including the fine-tuned model folder
repo_dir = snapshot_download(
    repo_id=REPO_ID,
    repo_type=REPO_TYPE,
    token=HF_TOKEN,
    cache_dir="private_space_cache",
    force_download=True  # Forces redownload
)

# Change the working directory to the downloaded snapshot directory
# This step is very imporptant
os.chdir(repo_dir)

# # Add repo directory to sys.path so Python can find modules inside it
sys.path.append(repo_dir)



# Download specific files (if snapshot_download wasn't used)
app_path = hf_hub_download(
    repo_id=REPO_ID,
    filename="app.py",
    repo_type=REPO_TYPE
)

# Load and execute `app.py`
spec_app = importlib.util.spec_from_file_location("*", app_path)
app_module = importlib.util.module_from_spec(spec_app)
spec_app.loader.exec_module(app_module)

# Now you can use functions from utils_module
result = app_module.main()