# app.py  (root)
from pathlib import Path
import gradio as gr
from inference import chat_completion           # your back‑end helpers
from tavily_search import enhance_query_with_search
from utils import (extract_text_from_file, extract_website_content,
                   history_to_messages, apply_search_replace_changes,
                   remove_code_block, parse_transformers_js_output,
                   format_transformers_js_output)
from models import AVAILABLE_MODELS, find_model
from deploy import send_to_sandbox

# ---------- backend callback -------------
def generate(payload):
    prompt  = payload["prompt"]
    model   = find_model(payload["model_id"])
    lang    = payload["language"]
    history = payload.get("history", [])
    # … same logic as before, returns {"code": ..., "history": [...] }
    # (keep your existing generate() from the compact app.py)
    return code, history

# ---------- UI ---------------------------
INDEX = Path("static/index.html").read_text(encoding="utf-8")

with gr.Blocks(css="body{margin:0}", title="AnyCoder AI") as demo:
    # custom front‑end
    gr.HTML(INDEX, unsafe_allow_html=True)

    # hidden API endpoints
    generator = gr.JSON(
        label="hidden",
        visible=False
    ).api(generate, path="/run/predict", methods=["POST"])

if __name__ == "__main__":
    demo.launch()