app.py

# app.py  ── root of the repo
"""
AnyCoder / Shasha AI – Gradio back‑end
• Hosts the custom HTML/JS/CSS in /static
• Exposes POST /run/predict for the browser‑side fetch()
"""
from __future__ import annotations
from pathlib import Path
from typing import List, Tuple

import gradio as gr

from inference       import chat_completion
from tavily_search   import enhance_query_with_search
from models          import AVAILABLE_MODELS, find_model, ModelInfo
from utils           import (
    extract_text_from_file, extract_website_content,
    history_to_messages, history_to_chatbot_messages,
    apply_search_replace_changes, remove_code_block,
    parse_transformers_js_output, format_transformers_js_output,
)

SYSTEM_PROMPTS = {
    "html":           "ONLY USE HTML, CSS & JS. Return ONE file wrapped in ```html```.",
    "transformers.js":"Generate THREE files (index.html / index.js / style.css) as fenced blocks."
}
History = List[Tuple[str, str]]

# ─────────────────────────────────────────────────────────────────────────────
def generate(prompt:str,
             file_path:str|None,
             website_url:str|None,
             model_id:str,
             language:str,
             enable_search:bool,
             history:History|None) -> Tuple[str,History]:
    """Invoked by the JS front‑end."""
    history = history or []
    sys_prompt = SYSTEM_PROMPTS.get(language, f"You are an expert {language} developer.")
    messages  = history_to_messages(history, sys_prompt)

    ctx: list[str] = [prompt.strip()]
    if file_path:
        ctx.append("[File]\n" + extract_text_from_file(file_path)[:5_000])
    if website_url:
        html = extract_website_content(website_url)
        if not html.startswith("Error"):
            ctx.append("[Website]\n" + html[:8_000])

    user_q = "\n\n".join(filter(None, ctx))
    user_q = enhance_query_with_search(user_q, enable_search)
    messages.append({"role": "user", "content": user_q})

    model: ModelInfo = find_model(model_id) or AVAILABLE_MODELS[0]
    answer = chat_completion(model.id, messages)

    if language == "transformers.js":
        files = parse_transformers_js_output(answer)
        code  = format_transformers_js_output(files)
    else:
        cleaned = remove_code_block(answer)
        if history and not history[-1][1].startswith("❌"):
            cleaned = apply_search_replace_changes(history[-1][1], cleaned)
        code = cleaned

    history.append((prompt, code))
    return code, history

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

with gr.Blocks(css="body{margin:0}", title="AnyCoder AI") as demo:
    gr.HTML(HTML_SOURCE)                            # the whole UI
    # hidden I/O elements for the JS fetch()
    with gr.Group(visible=False):
        prompt_in  = gr.Textbox()
        file_in    = gr.File()
        url_in     = gr.Textbox()
        model_in   = gr.Textbox()
        lang_in    = gr.Textbox()
        search_in  = gr.Checkbox()
        hist_state = gr.State([])
        code_out, hist_out = gr.Textbox(), gr.State([])

    gr.Button(visible=False).click(                 # POST /run/predict
        generate,
        [prompt_in, file_in, url_in,
         model_in, lang_in, search_in, hist_state],
        [code_out, hist_out],
        api_name="predict",
    )

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