Update app.py

app.py

@@ -1,82 +1,59 @@
+# app.py  ── root of the repo
 """
-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.
+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
 
-# ---- 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,
+    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."
-    ),
+    "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]]
 
-# ------------------- 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."""
+# ─────────────────────────────────────────────────────────────────────────────
+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)
 
-    # ----- 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()]
-
+    ctx: list[str] = [prompt.strip()]
     if file_path:
-        ctx_parts.append("[File]")
-        ctx_parts.append(extract_text_from_file(file_path)[:5000])
+        ctx.append("[File]\n" + extract_text_from_file(file_path)[:5_000])
     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])
+        html = extract_website_content(website_url)
+        if not html.startswith("Error"):
+            ctx.append("[Website]\n" + 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})
+    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})
 
-    # ----- 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)
@@ -89,35 +66,27 @@ def generate(
     history.append((prompt, code))
     return code, history
 
-# ------------------- read custom HTML --------------------------------------
-HTML_SOURCE = Path("index.html").read_text(encoding="utf‑8")
+# ───────────────────────────────────────────��─────────────────────────────────
+HTML_SOURCE = Path("static/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([])
-
+    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([])
 
-    # 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],
+    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",
     )