Update app.py

app.py

@@ -1,37 +1,129 @@
-# app.py  (root)
+# app.py
+# --------------------------------------------------------------------
+# AnyCoder / Shasha AI – Gradio back‑end
+# --------------------------------------------------------------------
+"""
+• Renders the custom front‑end stored in index.html (+ static assets).
+• Provides one API route (`POST /run/predict`) the JS front‑end calls
+  to run model inference.
+• Relies on helper modules (inference.py, models.py, utils.py, …)
+  exactly as you already have them.
+"""
+
 from pathlib import Path
+from typing import List, Tuple
+
 import gradio as gr
-from inference import chat_completion           # your back‑end helpers
+
+# ── local helpers (unchanged) ────────────────────────────────────────
+from inference import chat_completion
 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 models import AVAILABLE_MODELS, find_model, ModelInfo
 from deploy import send_to_sandbox
+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 ────────────────────────────────────────────────────────
+History = List[Tuple[str, str]]
+
+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."
+    ),
+}
+
+# ── core back‑end 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]:
+    """Backend for /run/predict."""
+    history = history or []
+
+    # 1) system prompt + history
+    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()]
 
-# ---------- 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)
+    if file_path:
+        ctx_parts.append("[File]")
+        ctx_parts.append(extract_text_from_file(file_path)[:5_000])
+    if website_url:
+        html = extract_website_content(website_url)
+        if not html.startswith("Error"):
+            ctx_parts.append("[Website]")
+            ctx_parts.append(html[:8_000])
+
+    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})
+
+    # 2) model call
+    model: ModelInfo = find_model(model_id) or AVAILABLE_MODELS[0]
+    answer = chat_completion(model.id, messages)
+
+    # 3) post‑processing
+    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
 
-# ---------- UI ---------------------------
-INDEX = Path("static/index.html").read_text(encoding="utf-8")
 
+# ── read the custom HTML front‑end ───────────────────────────────────
+INDEX = Path("index.html").read_text(encoding="utf-8")
+
+# ── Gradio UI (wrapper only) ─────────────────────────────────────────
 with gr.Blocks(css="body{margin:0}", title="AnyCoder AI") as demo:
-    # custom front‑end
-    gr.HTML(INDEX, unsafe_allow_html=True)
+    # 1 visible: your static front‑end
+    gr.HTML(INDEX)                     # ← NO unsafe_allow_html / sanitize
+
+    # 2 hidden components for the API call wiring
+    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([])
 
-    # hidden API endpoints
-    generator = gr.JSON(
-        label="hidden",
-        visible=False
-    ).api(generate, path="/run/predict", methods=["POST"])
+    # bind /run/predict
+    trig = gr.Button(visible=False)
+    trig.click(
+        generate,
+        inputs=[prompt_in, file_in, url_in, model_in, lang_in, search_in, hist_state],
+        outputs=[code_out, hist_out],
+        api_name="predict",
+    )
 
+# ── static assets (.css / .js) are picked up automatically by HF Spaces
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue().launch()