app.py

"""
AnyCoder / Shasha AI – Gradio back‑end

• Serves the custom front‑end shipped in index.html (+ static/style.css & static/index.js).
• Exposes one JSON endpoint (`POST /run/predict`) that the JS front‑end
  calls to run model inference.
"""

from pathlib import Path
from typing import List, Tuple

import gradio as gr

# ---- local helpers --------------------------------------------------------
from inference       import chat_completion
from tavily_search   import enhance_query_with_search
from deploy          import send_to_sandbox
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,
)

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

# ------------------- core callback -----------------------------------------
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]:
    """Called by the JS front‑end via POST /run/predict."""
    history = history or []

    # ----- build system + messages -----------------------------------------
    system_prompt = SYSTEM_PROMPTS.get(language, f"You are an expert {language} developer.")
    messages      = history_to_messages(history, system_prompt)

    ctx_parts: list[str] = [prompt.strip()]

    if file_path:
        ctx_parts.append("[File]")
        ctx_parts.append(extract_text_from_file(file_path)[:5000])
    if website_url:
        site_html = extract_website_content(website_url)
        if not site_html.startswith("Error"):
            ctx_parts.append("[Website]")
            ctx_parts.append(site_html[:8000])

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

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

    # ----- post‑process output ---------------------------------------------
    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

# ------------------- read custom HTML --------------------------------------
HTML_SOURCE = Path("index.html").read_text(encoding="utf‑8")

# ------------------- Gradio UI ---------------------------------------------
with gr.Blocks(css="body{margin:0}", title="AnyCoder AI") as demo:
    # 1 visible: your custom front‑end
    gr.HTML(HTML_SOURCE)              # <- sanitize=False removed

    # 2 hidden: API inputs / outputs
    with gr.Group(visible=False) as api:
        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([])

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

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