Upload the 1 pussy

the 1 pussy

@@ -0,0 +1,136 @@
+Here's a clean, production-ready solution to **auto-deploy your Hugging Face model** using the provided credentials. This includes:
+
+✅ Auto-login with HF token  
+✅ Git clone + LFS skip (for large files)  
+✅ Model upload via `transformers` + `huggingface_hub`  
+✅ Auto-deployment to a **new public space** (we'll use `hf.co/spaces`)  
+✅ Error handling & logging  
+
+---
+
+## ✅ 1. Install Dependencies
+
+```bash
+pip install transformers huggingface_hub torch accelerate tqdm
+```
+
+> 💡 Note: If you're running on a server or cloud (like AWS/GCP), make sure you have `pip` installed.
+
+---
+
+## ✅ 2. Create the Deployment Script (`deploy_hf.py`)
+
+```python
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Auto-deploy script for Hugging Face Spaces from GitHub repo.
+Uses HF token for authentication and uploads model to new space.
+"""
+
+import os
+import subprocess
+import sys
+from datetime import datetime
+from pathlib import Path
+import logging
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s [%(levelname)s] %(message)s',
+    handlers=[
+        logging.FileHandler("deploy.log"),
+        logging.StreamHandler(sys.stdout)
+    ]
+)
+logger = logging.getLogger(__name__)
+
+# Configuration
+HF_USER = "cunt1257"
+HF_PASS = " # From your prompt
+
+REPO_URL = "https://huggingface.co/Cunt1257/pussymagnet"
+SPACE_NAME = "nwa-auto-auction-space"  # Suggested name for clarity
+MODEL_NAME = "pussymagnet"  # The model name on HF
+
+# Optional: Use environment variables instead of hardcoding
+def get_token():
+    if HF_TOKEN:
+        return HF_TOKEN
+    else:
+        raise ValueError("HF_TOKEN not set. Please provide valid HF token.")
+
+def login_to_hf(token):
+    """Login to Hugging Face CLI"""
+    try:
+        logger.info(f"Logging into Hugging Face with token...")
+        result = subprocess.run([
+            "hf", "login",
+            "-u", HF_USER,
+            "-p", HF_PASS
+        ], capture_output=True, text=True)
+        if result.returncode != 0:
+            logger.error(f"Failed to login: {result.stderr}")
+            raise Exception("Login failed")
+        logger.info("Login successful.")
+    except Exception as e:
+        logger.error(f"Error during login: {e}")
+
+def clone_repo(repo_url, target_dir="pussymagnet"):
+    """Clone repository with LFS skipping"""
+    logger.info(f"Cloning {repo_url} to {target_dir}...")
+    try:
+        # Clone without LFS (to avoid issues with large files)
+        cmd = f"git clone --no-checkout {repo_url} {target_dir}"
+        result = subprocess.run(cmd.split(), capture_output=True, text=True)
+        if result.returncode != 0:
+            logger.error(f"Git clone failed:\n{result.stderr}")
+            raise Exception("Clone failed")
+
+        # Initialize submodules if needed (optional)
+        # subprocess.run(["git", "submodule", "update", "--init"], cwd=target_dir, check=True)
+
+        logger.info("Repository cloned successfully.")
+        return target_dir
+    except Exception as e:
+        logger.error(f"Error cloning repo: {e}")
+        raise
+
+def push_model_to_space(model_path, space_name, token=None):
+    """Upload model to Hugging Face Space"""
+    try:
+        logger.info(f"Uploading model to HF Space: {space_name}")
+        
+        # Set up local directory structure for uploading
+        from huggingface_hub import create_repo, upload_file
+
+        # Create repo if doesn't exist
+        repo_id = f"{HF_USER}/{space_name}"  # Format: user/repo-name
+        
+        # Create repo (if not exists)
+        create_repo(
+            repo_id=repo_id,
+            private=False,
+            exist_ok=True,
+            token=token
+        )
+
+        # Upload model files
+        model_files = [
+            ".",
+            "README.md",
+            "LICENSE",
+            "requirements.txt"
+        ]
+
+        # Upload all files recursively (you may want to filter only specific ones)
+        for item in Path(model_path).rglob("*"):
+            if item.is_file():
+                relative_path = str(item.relative_to(model_path))
+                upload_file(
+                    path_or_obj=item,
+                    repo_id=repo_id,
+                    commit_message=f"Deployed at {datetime.now().strftime('%Y-%m-%d %H:%M')}",
+                    token=token
+