"""
eden_fix_missing_repos.py
Creates the missing CIFAR-10 repos for classic models (test2/test3)
that were skipped during the initial README generation, then re-uploads
their CSV logs.
"""
from huggingface_hub import HfApi, create_repo, upload_file
import os, glob

TOKEN = os.environ.get("HF_TOKEN", "")
USER  = "Shanmuk4622"
BASE  = os.path.dirname(os.path.abspath(__file__))

api = HfApi(token=TOKEN)
print(f"Logged in as: {api.whoami()['name']}\n")

HARDWARE = {
    "gpu": "NVIDIA GeForce GTX 1080 Ti (11 GB VRAM, 250 W TDP)",
    "cpu": "Intel Xeon W-2125 (4 cores / 8 threads @ 4.00 GHz)",
    "ram": "63.66 GB System RAM",
}

# ── Missing repos: classic models on CIFAR-10 ─────────────────────────────────
MISSING = [
    ("AlexNet",    "CIFAR-10", "test2", "AlexNet_CIFAR10.py"),
    ("DenseNet121","CIFAR-10", "test2", "DenseNet121_CIFAR10.py"),
    ("InceptionV3","CIFAR-10", "test2", "InceptionV3_CIFAR10.py"),
    ("ResNet18",   "CIFAR-10", "test2", "ResNet18_CIFAR10.py"),
    ("ResNet50",   "CIFAR-10", "test2", "ResNet50_CIFAR10.py"),
]

def make_readme(arch, dataset, folder):
    phase = "Baseline – Standard Full Training (Reference Study)"
    technique = (
        "Standard full fine-tuning used as the **Brute-Force Baseline** for "
        "energy comparison. All layers trained from epoch 1 with a fixed learning "
        "rate and no gradient accumulation. Included for transparent EAG benchmarking."
    )
    arch_tag = arch.lower()
    ds_size  = "60,000 images – 10 classes (32×32 px)"

    return f"""---
language: en
license: apache-2.0
tags:
- image-classification
- green-ai
- energy-efficiency
- computer-vision
- {arch_tag}
- eden-framework
- reference-study
- sustainable-ai
datasets:
- cifar10
metrics:
- accuracy
---

# EDEN-{arch}-{dataset} — *{phase}*

> **Primary KPI:** EAG (Energy-to-Accuracy Gradient) — see Green Delta Table below.

## Abstract
This model is part of **Project EDEN (Energy-Driven Evolution of Networks)**.
It serves as the **Brute-Force Baseline** for the {arch} architecture on {dataset},
providing a transparent energy reference for EAG benchmarking against EDEN-optimized models.

**Applied Technique:** {phase}

## Profiling Environment
| Component | Specification |
|---|---|
| **GPU** | {HARDWARE['gpu']} |
| **CPU** | {HARDWARE['cpu']} |
| **RAM** | {HARDWARE['ram']} |
| **Dataset** | {dataset} — {ds_size} |

## 🟢 Green Delta Table
*This is the reference baseline. Compare against EDEN-optimized models for EAG.*

| Metric | {arch} Baseline | EDEN Optimized | Δ |
|---|---|---|---|
| Accuracy | See CSV log | See SOTA repo | — |
| Total Energy (J) | See CSV log | See SOTA repo | — |
| **EAG Score** | — | See SOTA repo | ΔAcc/ΔJoules |

## E2AM Algorithm — Applied Phase
{technique}

## Cite This Research
```bibtex
@misc{{eden2025,
  title     = {{Project EDEN: Energy-Driven Evolution of Networks}},
  author    = {{EDEN Research Team}},
  year      = {{2025}},
  note      = {{Hugging Face: Shanmuk4622}},
  url       = {{https://huggingface.co/{USER}}}
}}
```
"""

def safe_upload(local_path, repo_id, repo_filename):
    try:
        upload_file(
            path_or_fileobj=local_path,
            path_in_repo=repo_filename,
            repo_id=repo_id,
            token=TOKEN,
            repo_type="model",
        )
        print(f"  ✓  {repo_filename}  →  {repo_id}")
    except Exception as e:
        print(f"  ✗  {repo_filename}  →  {repo_id}  |  {e}")

# ── STEP 1: Create missing repos + upload READMEs ─────────────────────────────
print("="*60)
print("STEP 1 — Creating missing CIFAR-10 repos & READMEs")
print("="*60)

readme_dir = os.path.join(BASE, "hf_readmes")
os.makedirs(readme_dir, exist_ok=True)

for arch, dataset, folder, _ in MISSING:
    repo_name = f"EDEN-{arch}-{dataset}"
    repo_id   = f"{USER}/{repo_name}"

    # Write README locally
    readme_content = make_readme(arch, dataset, folder)
    readme_path = os.path.join(readme_dir, f"{repo_name}_README.md")
    with open(readme_path, "w", encoding="utf-8") as f:
        f.write(readme_content)

    # Create repo + upload README
    create_repo(repo_id, token=TOKEN, repo_type="model", exist_ok=True, private=False)
    safe_upload(readme_path, repo_id, "README.md")

# ── STEP 2: Re-upload the failed CSVs ─────────────────────────────────────────
print("\n" + "="*60)
print("STEP 2 — Re-uploading failed CIFAR-10 CSV logs")
print("="*60)

def parse_arch_ds(fn):
    fn = fn.lower().replace("\\", "/")
    ds, arch = "unknown", "unknown"
    if   "cifar100"  in fn: ds = "CIFAR-100"
    elif "cifar10"   in fn: ds = "CIFAR-10"
    elif "imagenet"  in fn: ds = "Custom-ImageNet300"
    if   "efficientnet" in fn: arch = "EfficientNetV2"
    elif "convnext"     in fn: arch = "ConvNeXtV2"
    elif "mobilevit"    in fn: arch = "MobileViTv3"
    elif "resnet50"     in fn: arch = "ResNet50"
    elif "resnet18"     in fn: arch = "ResNet18"
    elif "vgg16"        in fn: arch = "VGG16"
    elif "alexnet"      in fn: arch = "AlexNet"
    elif "inception"    in fn: arch = "InceptionV3"
    elif "densenet"     in fn: arch = "DenseNet121"
    elif "unet"         in fn: arch = "UNet"
    return arch, ds

# Only target CIFAR-10 CSVs for classic models
target_archs = {"AlexNet", "DenseNet121", "InceptionV3", "ResNet18", "ResNet50"}

for csv in sorted(glob.glob(os.path.join(BASE, "**", "*.csv"), recursive=True)):
    if any(skip in csv for skip in ["green_ai_", "experiment_summary", "repository"]):
        continue
    arch, ds = parse_arch_ds(csv)
    if arch not in target_archs or ds != "CIFAR-10":
        continue
    repo_id = f"{USER}/EDEN-{arch}-{ds}"
    safe_upload(csv, repo_id, os.path.basename(csv))

print("\n" + "="*60)
print("FIX COMPLETE — Check https://huggingface.co/Shanmuk4622")
print("="*60)