app.py

"""
AnyCoder AI — static‑first UI wrapper
Loads HTML/CSS/JS from the /static folder and exposes /run/predict for
the front‑end to call.

• static/index.html      dark themed UI
• static/style.css       styles
• static/index.js        JS logic (model list, fetch /run/predict)

Back‑end helpers (models.py, inference.py, plugins.py …) are unchanged.
"""

from pathlib import Path
from typing import List, Tuple

import gradio as gr

# ---------- imports that actually do the work ----------
from inference import chat_completion                # runs the model
from tavily_search import enhance_query_with_search
from utils import (                                  # misc helpers
    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,
)
from models import AVAILABLE_MODELS, find_model, ModelInfo
# -------------------------------------------------------

SYSTEM_PROMPTS = {
    "html": (
        "ONLY USE HTML, CSS AND JAVASCRIPT.  Return **one** HTML file "
        "wrapped in ```html```."
    ),
    "transformers.js": (
        "Generate THREE fenced blocks: index.html, index.js, style.css."
    ),
}

History = List[Tuple[str, str]]

# ------------------------------------------------------------------
# /run/predict  — called by static/index.js
# ------------------------------------------------------------------
def generate(
    prompt: str,
    file_path: str | None,
    website_url: str | None,
    model_id: str,
    language: str,
    enable_search: bool,
    history: History | None,
):
    history = history or []

    # 1 · system + user messages
    sys_prompt = SYSTEM_PROMPTS.get(language, f"You are an expert {language} developer.")
    msgs = history_to_messages(history, sys_prompt)

    parts = [prompt.strip()]

    if file_path:
        parts.append(extract_text_from_file(file_path)[:5_000])

    if website_url:
        html = extract_website_content(website_url)
        if not html.startswith("Error"):
            parts.append(html[:8_000])

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

    # 2 · run model
    model: ModelInfo = find_model(model_id) or AVAILABLE_MODELS[0]
    reply = chat_completion(model.id, msgs)

    # 3 · post‑process
    if language == "transformers.js":
        files = parse_transformers_js_output(reply)
        code = format_transformers_js_output(files)
    else:
        cleaned = remove_code_block(reply)
        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


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

with gr.Blocks(css="body{margin:0}", title="AnyCoder AI") as demo:
    # Front‑end
    gr.HTML(HTML_SOURCE)

    # Hidden components for API
    prompt_in  = gr.Textbox(visible=False)
    file_in    = gr.File(visible=False)
    url_in     = gr.Textbox(visible=False)
    model_in   = gr.Textbox(visible=False)
    lang_in    = gr.Textbox(visible=False)
    search_in  = gr.Checkbox(visible=False)
    hist_state = gr.State([])

    code_out   = gr.Textbox(visible=False)
    hist_out   = gr.State([])

    # Expose /run/predict
    dummy_btn = gr.Button(visible=False)
    dummy_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",
        queue=True,
    )

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