Update app.py

app.py

@@ -1,201 +1,182 @@
 # app.py
-
 """
-Main application file for SHASHA AI, a Gradio-based AI code generation tool.
+ShashaCode Builder – AI code‑generation playground.
 
-Provides a UI for generating code in many languages using various AI models.
-Supports text prompts, file uploads, website scraping, optional web search,
-and live previews of HTML output.
+• Hugging Face Spaces + Gradio front‑end
+• Supports prompts, file upload, web‑site scraping, optional web search
+• Streams code back, shows live HTML preview, can deploy to a user Space
 """
 
+# ─────────────────────────────────────────  Imports
 import gradio as gr
-from typing import Optional, Dict, List, Tuple, Any
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple, Any
 
-# --- Local module imports ---
-from constants import (
+from constants import (                               # ← all constants live here
     HTML_SYSTEM_PROMPT,
     TRANSFORMERS_JS_SYSTEM_PROMPT,
+    SYSTEM_PROMPTS,
     AVAILABLE_MODELS,
     DEMO_LIST,
+    GRADIO_SUPPORTED_LANGUAGES,                      # ← new import
+    SEARCH_START, DIVIDER, REPLACE_END,
 )
-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,
+
+from hf_client      import get_inference_client
+from tavily_search  import enhance_query_with_search
+from utils          import (                          # helpers split into utils.py
     history_to_messages,
     history_to_chatbot_messages,
     remove_code_block,
     parse_transformers_js_output,
     format_transformers_js_output,
+    parse_svelte_output,
+    format_svelte_output,
+    apply_search_replace_changes,
+    apply_transformers_js_search_replace_changes,
+    extract_text_from_file,
+    extract_website_content,
+    get_gradio_language,
 )
-from deploy import send_to_sandbox
+from deploy         import send_to_sandbox
 
-# --- Type aliases ---
+# ─────────────────────────────────────────  Type Aliases
 History = List[Tuple[str, str]]
-Model = Dict[str, Any]
-
-# --- Supported languages for dropdown ---
-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"
-]
-
-def get_model_details(name: str) -> Optional[Model]:
-    for m in AVAILABLE_MODELS:
-        if m["name"] == name:
-            return m
-    return None
-
-def generation_code(
-    query: Optional[str],
-    file: Optional[str],
+ModelInfo = Dict[str, Any]
+
+# ─────────────────────────────────────────  Core Function
+def generate_code(
+    query: str,
+    file_path: Optional[str],
     website_url: Optional[str],
-    current_model: Model,
+    model: ModelInfo,
     enable_search: bool,
     language: str,
     history: Optional[History],
 ) -> Tuple[str, History, str, List[Dict[str, str]]]:
+    """Main inference pipeline: build prompt → call model → post‑process."""
     query = query or ""
     history = history or []
-    try:
-        # Choose system prompt based on language
-        if language == "html":
-            system_prompt = HTML_SYSTEM_PROMPT
-        elif language == "transformers.js":
-            system_prompt = TRANSFORMERS_JS_SYSTEM_PROMPT
-        else:
-            # Generic fallback prompt
-            system_prompt = (
-                f"You are an expert {language} developer. "
-                f"Write clean, idiomatic {language} code based on the user's request."
-            )
 
-        model_id = current_model["id"]
-        # Determine provider
-        if model_id.startswith("openai/") or model_id in {"gpt-4", "gpt-3.5-turbo"}:
-            provider = "openai"
-        elif model_id.startswith("gemini/") or model_id.startswith("google/"):
-            provider = "gemini"
-        elif model_id.startswith("fireworks-ai/"):
-            provider = "fireworks-ai"
-        else:
-            provider = "auto"
-
-        # Build message history
-        msgs = history_to_messages(history, system_prompt)
-        context = query
-        if file:
-            ftext = extract_text_from_file(file)
-            context += f"\n\n[Attached file]\n{ftext[:5000]}"
-        if website_url:
-            wtext = extract_website_content(website_url)
-            if not wtext.startswith("Error"):
-                context += f"\n\n[Website content]\n{wtext[:8000]}"
-        final_q = enhance_query_with_search(context, enable_search)
-        msgs.append({"role": "user", "content": final_q})
-
-        # Call the model
-        client = get_inference_client(model_id, provider)
-        resp = client.chat.completions.create(
-            model=model_id,
-            messages=msgs,
-            max_tokens=16000,
-            temperature=0.1
-        )
-        content = resp.choices[0].message.content
-
-    except Exception as e:
-        err = f"❌ **Error:**\n```\n{e}\n```"
-        history.append((query, err))
-        return "", history, "", history_to_chatbot_messages(history)
-
-    # Process model output
+    # 1. pick system prompt
+    if   language == "html":             system = HTML_SYSTEM_PROMPT
+    elif language == "transformers.js":  system = TRANSFORMERS_JS_SYSTEM_PROMPT
+    else:                                system = SYSTEM_PROMPTS.get(language, HTML_SYSTEM_PROMPT)
+
+    # 2. build message list
+    messages = history_to_messages(history, system)
+
+    ctx_parts = [query.strip()]
+
+    if file_path:    ctx_parts += ["[File]",    extract_text_from_file(file_path)[:5000]]
+    if website_url:
+        html = extract_website_content(website_url)
+        if not html.startswith("Error"):
+            ctx_parts += ["[Website]", html[:8000]]
+
+    user_query = "\n\n".join(ctx_parts)
+    user_query = enhance_query_with_search(user_query, enable_search)
+    messages.append({"role": "user", "content": user_query})
+
+    # 3. call model
+    client = get_inference_client(model["id"])
+    resp   = client.chat.completions.create(
+        model=model["id"],
+        messages=messages,
+        max_tokens=16000,
+        temperature=0.15,
+    )
+    answer = resp.choices[0].message.content
+
+    # 4. post‑process
     if language == "transformers.js":
-        files = parse_transformers_js_output(content)
-        code = format_transformers_js_output(files)
+        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(content)
-        if history and history[-1][1] and not history[-1][1].startswith("❌"):
-            code = apply_search_replace_changes(history[-1][1], cleaned)
-        else:
-            code = cleaned
+        clean  = remove_code_block(answer)
+        if history and not history[-1][1].startswith("❌"):
+            clean = apply_search_replace_changes(history[-1][1], clean)
+        code    = clean
         preview = send_to_sandbox(code) if language == "html" else ""
 
-    new_hist = history + [(query, code)]
-    chat = history_to_chatbot_messages(new_hist)
-    return code, new_hist, preview, chat
+    history.append((query, code))
+    chat_msgs = history_to_chatbot_messages(history)
+    return code, history, preview, chat_msgs
+
+
+# ─────────────────────────────────────────  UI
+LOGO_PATH = "assets/logo.png"                       # ensure this file exists
 
-# --- Custom CSS ---
 CUSTOM_CSS = """
-body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; }
-#main_title { text-align: center; font-size: 2.5rem; margin-top: 1.5rem; }
-#subtitle { text-align: center; color: #4a5568; margin-bottom: 2.5rem; }
-.gradio-container { background-color: #f7fafc; }
-#gen_btn { box-shadow: 0 4px 6px rgba(0,0,0,0.1); }
+body {font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;}
+#logo {max-height:64px;margin:auto;}
 """
 
-with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue"), css=CUSTOM_CSS, title="Shasha AI") as demo:
-    history_state = gr.State([])
-    initial_model = AVAILABLE_MODELS[0]
-    model_state = gr.State(initial_model)
+with gr.Blocks(css=CUSTOM_CSS, title="ShashaCode Builder") as demo:
+    state_history = gr.State([])
+    state_model   = gr.State(AVAILABLE_MODELS[0])
 
-    gr.Markdown("# 🚀 Shasha AI", elem_id="main_title")
-    gr.Markdown("Your AI partner for generating, modifying, and understanding code.", elem_id="subtitle")
+    # Header
+    with gr.Row():
+        gr.Image(LOGO_PATH, elem_id="logo", show_label=False, height=64)
+        gr.Markdown("## **AnyCoder AI**\nYour AI partner for generating, modifying & understanding code.")
 
+    # Sidebar (inputs)
     with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("### 1. Select Model")
-            model_dd = gr.Dropdown(
+        with gr.Column(scale=1, min_width=300):
+            # Model
+            dd_model = gr.Dropdown(
+                label="AI Model",
                 choices=[m["name"] for m in AVAILABLE_MODELS],
-                value=initial_model["name"],
-                label="AI Model"
+                value=AVAILABLE_MODELS[0]["name"],
             )
 
-            gr.Markdown("### 2. Provide Context")
+            # Prompt / File / Website tabs
             with gr.Tabs():
-                with gr.Tab("📝 Prompt"):
-                    prompt_in = gr.Textbox(lines=7, placeholder="Describe your request...", show_label=False)
-                with gr.Tab("📄 File"):
-                    file_in = gr.File(type="filepath")
-                with gr.Tab("🌐 Website"):
-                    url_in = gr.Textbox(placeholder="https://example.com")
-
-            gr.Markdown("### 3. Configure Output")
-            lang_dd = gr.Dropdown(SUPPORTED_LANGUAGES, value="html", label="Target Language")
-            search_chk = gr.Checkbox(label="Enable Web Search")
+                with gr.Tab("Prompt"):
+                    tb_prompt = gr.Textbox(label="Describe what you'd like to build…", lines=6)
+                with gr.Tab("File"):
+                    inp_file  = gr.File(label="Reference file", type="filepath")
+                with gr.Tab("Website"):
+                    tb_url    = gr.Textbox(label="URL to redesign")
+
+            # Output config
+            dd_lang = gr.Dropdown(
+                label="Target language",
+                choices=[l for l in GRADIO_SUPPORTED_LANGUAGES if l],  # ← fixed list
+                value="html",
+            )
+            chk_search = gr.Checkbox(label="Enable Tavily Web Search")
 
-            with gr.Row():
-                clr_btn = gr.Button("Clear Session", variant="secondary")
-                gen_btn = gr.Button("Generate Code", variant="primary", elem_id="gen_btn")
+            # Buttons
+            btn_generate = gr.Button("Generate Code", variant="primary")
+            btn_clear    = gr.Button("Clear Session",  variant="secondary")
 
+        # Main panel (outputs)
         with gr.Column(scale=2):
             with gr.Tabs():
-                with gr.Tab("💻 Code"):
-                    code_out = gr.Code(language="html", interactive=True)
-                with gr.Tab("👁️ Live Preview"):
-                    preview_out = gr.HTML()
-                with gr.Tab("📜 History"):
-                    chat_out = gr.Chatbot(type="messages")
-
-    model_dd.change(lambda n: get_model_details(n) or initial_model, inputs=[model_dd], outputs=[model_state])
-
-    gen_btn.click(
-        fn=generation_code,
-        inputs=[prompt_in, file_in, url_in, model_state, search_chk, lang_dd, history_state],
-        outputs=[code_out, history_state, preview_out, chat_out],
+                with gr.Tab("Code"):
+                    out_code = gr.Code(language="html", show_label=False)
+                with gr.Tab("Preview"):
+                    out_prev = gr.HTML()
+                with gr.Tab("History"):
+                    out_hist = gr.Chatbot(type="messages")
+
+    # ─── Callbacks ─────────────────────────────────────────────
+    def _model_from_name(name):
+        return next((m for m in AVAILABLE_MODELS if m["name"] == name), AVAILABLE_MODELS[0])
+
+    dd_model.change(lambda n: _model_from_name(n), inputs=dd_model, outputs=state_model)
+
+    btn_generate.click(
+        fn=generate_code,
+        inputs=[tb_prompt, inp_file, tb_url, state_model, chk_search, dd_lang, state_history],
+        outputs=[out_code, state_history, out_prev, out_hist],
     )
 
-    clr_btn.click(
-        lambda: ("", None, "", [], "", "", []),
-        outputs=[prompt_in, file_in, url_in, history_state, code_out, preview_out, chat_out],
-        queue=False,
-    )
+    btn_clear.click(lambda: ("", None, "", [], "", []),
+                    outputs=[tb_prompt, inp_file, tb_url, state_history, out_code, out_prev])
 
 if __name__ == "__main__":
     demo.queue().launch()