app.py

# app.py
"""
Shasha / AnyCoder AI  –  Gradio front‑end + back‑end

Generate or refactor code with multiple foundation models.
Uploads, website scrape, optional Tavily search enrichment, live HTML preview.
"""

from __future__ import annotations
from typing import List, Tuple, Dict, Any, Optional

import gradio as gr

# ──────────────────────────────────────────────────────────────────────────────
# Local helpers (unchanged)
# ──────────────────────────────────────────────────────────────────────────────
from constants import (
    HTML_SYSTEM_PROMPT,
    TRANSFORMERS_JS_SYSTEM_PROMPT,
    AVAILABLE_MODELS,
)
from hf_client      import get_inference_client
from tavily_search  import enhance_query_with_search
from utils          import (
    extract_text_from_file,
    extract_website_content,
    apply_search_replace_changes,
    history_to_messages,
    history_to_chatbot_messages,
    remove_code_block,
    parse_transformers_js_output,
    format_transformers_js_output,
)
from deploy         import send_to_sandbox

History = List[Tuple[str, str]]

SUPPORTED_LANGUAGES = [
    "python", "c", "cpp", "markdown", "latex", "json", "html", "css",
    "javascript", "jinja2", "typescript", "yaml", "dockerfile", "shell",
    "r", "sql", "sql-msSQL", "sql-mySQL", "sql-mariaDB", "sql-sqlite",
    "sql-cassandra", "sql-plSQL", "sql-hive", "sql-pgSQL", "sql-gql",
    "sql-gpSQL", "sql-sparkSQL", "sql-esper",
]

# ──────────────────────────────────────────────────────────────────────────────
# Generation callback
# ──────────────────────────────────────────────────────────────────────────────
def generation_code(
    query: Optional[str],
    file_path: Optional[str],
    website_url: Optional[str],
    current_model: Dict[str, Any],
    enable_search: bool,
    language: str,
    history: History | None,
) -> Tuple[str, History, str, List[Dict[str, str]]]:
    """Core back‑end that the “Generate Code” button calls."""
    query   = (query or "").strip()
    history = history or []

    # Pick proper system prompt
    if language == "html":
        system_prompt = HTML_SYSTEM_PROMPT
    elif language == "transformers.js":
        system_prompt = TRANSFORMERS_JS_SYSTEM_PROMPT
    else:
        system_prompt = (
            f"You are an expert {language} developer. "
            f"Write clean, idiomatic {language} based on the user request."
        )

    # Route to provider
    model_id = current_model["id"]
    if model_id.startswith(("openai/", "gpt-")):
        provider = "openai"
    elif model_id.startswith(("gemini/", "google/")):
        provider = "gemini"
    elif model_id.startswith("fireworks-ai/"):
        provider = "fireworks-ai"
    else:
        provider = "auto"

    # Build message list
    messages = history_to_messages(history, system_prompt)
    context_parts: List[str] = [query]

    if file_path:
        context_parts.append("[Attached file]")
        context_parts.append(extract_text_from_file(file_path)[:5_000])

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

    user_msg = "\n\n".join(context_parts)
    user_msg = enhance_query_with_search(user_msg, enable_search)
    messages.append({"role": "user", "content": user_msg})

    # Call model
    try:
        client  = get_inference_client(model_id, provider)
        resp    = client.chat.completions.create(
            model      = model_id,
            messages   = messages,
            max_tokens = 16_000,
            temperature= 0.1,
        )
        answer = resp.choices[0].message.content
    except Exception as exc:
        err = f"❌ **Error**\n```\n{exc}\n```"
        history.append((query, err))
        return "", history, "", history_to_chatbot_messages(history)

    # Post‑process
    if language == "transformers.js":
        files   = parse_transformers_js_output(answer)
        code    = format_transformers_js_output(files)
        preview = send_to_sandbox(files.get("index.html", ""))
    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
        preview = send_to_sandbox(code) if language == "html" else ""

    history.append((query, code))
    chat_history = history_to_chatbot_messages(history)
    return code, history, preview, chat_history

# ──────────────────────────────────────────────────────────────────────────────
# Gradio UI
# ──────────────────────────────────────────────────────────────────────────────
CUSTOM_CSS = """
body{font-family:-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,sans-serif;}
.gradio-container{background:#f9fafb;}
#gen-btn{box-shadow:0 2px 4px rgba(0,0,0,.08);}
"""

with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo"),
               title="AnyCoder AI",
               css=CUSTOM_CSS) as demo:

    state_history: gr.State = gr.State([])
    state_model  : gr.State = gr.State(AVAILABLE_MODELS[0])

    gr.Markdown("## 🏢 **AnyCoder AI**\nYour AI partner for generating, modifying  and  understanding code.",
                elem_id="header")

    # 1 · MODEL
    gr.Markdown("### 1 · Model")
    model_dd = gr.Dropdown(
        choices=[m["name"] for m in AVAILABLE_MODELS],
        value=AVAILABLE_MODELS[0]["name"],
        label="AI Model",
    )

    # 2 · CONTEXT
    gr.Markdown("### 2 · Context")
    with gr.Tabs():
        with gr.Tab("Prompt"):
            prompt_tb = gr.Textbox(lines=6, placeholder="Describe what you need…")
        with gr.Tab("File"):
            file_upl  = gr.File(label="Reference file")
            import_btn= gr.Button("Import Project", variant="secondary")
            import_btn.click(lambda: print("import clicked"), outputs=[])
        with gr.Tab("Website"):
            url_tb    = gr.Textbox(placeholder="https://example.com")

    # 3 · OUTPUT
    gr.Markdown("### 3 · Output")
    lang_dd   = gr.Dropdown(SUPPORTED_LANGUAGES, value="html", label="Target Language")
    search_ck = gr.Checkbox(label="Enable Tavily Web Search")

    with gr.Row():
        clear_btn = gr.Button("Clear Session", variant="secondary")
        gen_btn   = gr.Button("Generate Code", elem_id="gen-btn")

    # OUTPUT PANELS
    with gr.Tabs():
        with gr.Tab("Code"):
            code_out = gr.Code(language="html", interactive=True, lines=25)
        with gr.Tab("Live Preview"):
            preview  = gr.HTML()
        with gr.Tab("History"):
            chatbox  = gr.Chatbot(type="messages")

    # ── CALLBACKS ──────────────────────────────────────────────
    model_dd.change(lambda n: next(m for m in AVAILABLE_MODELS if m["name"] == n),
                    inputs=model_dd, outputs=state_model)

    gen_btn.click(
        fn=generation_code,
        inputs=[prompt_tb, file_upl, url_tb,
                state_model, search_ck, lang_dd, state_history],
        outputs=[code_out, state_history, preview, chatbox],
    )

    clear_btn.click(
        lambda: ("", None, "", [], "", "", []),
        outputs=[prompt_tb, file_upl, url_tb, state_history,
                 code_out, preview, chatbox],
        queue=False,
    )

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