import os
import gradio as gr
from transformers import AutoModel, AutoTokenizer

def process_models(model_name, save_dir, additional_models):
    log_lines = []
    
    # Process primary model
    log_lines.append(f"🚀 Loading model: **{model_name}**")
    try:
        model = AutoModel.from_pretrained(model_name)
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        model_save_path = os.path.join(save_dir, model_name.replace("/", "_"))
        os.makedirs(model_save_path, exist_ok=True)
        model.save_pretrained(model_save_path)
        log_lines.append(f"✅ Saved **{model_name}** to `{model_save_path}`")
    except Exception as e:
        log_lines.append(f"❌ Error with **{model_name}**: {e}")
    
    # Process additional models if any
    if additional_models:
        for m in additional_models:
            log_lines.append(f"🚀 Loading model: **{m}**")
            try:
                model = AutoModel.from_pretrained(m)
                tokenizer = AutoTokenizer.from_pretrained(m)
                model_save_path = os.path.join(save_dir, m.replace("/", "_"))
                os.makedirs(model_save_path, exist_ok=True)
                model.save_pretrained(model_save_path)
                log_lines.append(f"✅ Saved **{m}** to `{model_save_path}`")
            except Exception as e:
                log_lines.append(f"❌ Error with **{m}**: {e}")
    
    return "\n".join(log_lines)

# Mermaid glossary: a one-line flow summary of our UI actions.
mermaid_glossary = """graph LR
A[🚀 Model Input] --> B[Load Model]
B --> C[💾 Save Model]
D[🧩 Additional Models] --> B

"""