Update app.py

app.py

@@ -1,129 +1,201 @@
 # 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.
+
 """
+Main application file for SHASHA AI, a Gradio-based AI code generation tool.
 
-from pathlib import Path
-from typing import List, Tuple
+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.
+"""
 
 import gradio as gr
-
-# ── local helpers (unchanged) ────────────────────────────────────────
-from inference import chat_completion
+from typing import Optional, Dict, List, Tuple, Any
+
+# --- Local module imports ---
+from constants import (
+    HTML_SYSTEM_PROMPT,
+    TRANSFORMERS_JS_SYSTEM_PROMPT,
+    AVAILABLE_MODELS,
+    DEMO_LIST,
+)
+from hf_client import get_inference_client
 from tavily_search import enhance_query_with_search
-from models import AVAILABLE_MODELS, find_model, ModelInfo
-from deploy import send_to_sandbox
 from utils import (
     extract_text_from_file,
     extract_website_content,
+    apply_search_replace_changes,
     history_to_messages,
     history_to_chatbot_messages,
-    apply_search_replace_changes,
     remove_code_block,
     parse_transformers_js_output,
     format_transformers_js_output,
 )
+from deploy import send_to_sandbox
 
-# ── constants ────────────────────────────────────────────────────────
+# --- Type aliases ---
 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,
+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],
+    website_url: Optional[str],
+    current_model: Model,
     enable_search: bool,
-    history: History | None,
-) -> Tuple[str, History]:
-    """Backend for /run/predict."""
+    language: str,
+    history: Optional[History],
+) -> Tuple[str, History, str, List[Dict[str, str]]]:
+    query = query or ""
     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()]
-
-    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
+    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
     if language == "transformers.js":
-        files = parse_transformers_js_output(answer)
+        files = parse_transformers_js_output(content)
         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
-
-    history.append((prompt, code))
-    return code, history
-
-
-# ── 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:
-    # 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([])
-
-    # 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",
+        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
+        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
+
+# --- 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); }
+"""
+
+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)
+
+    gr.Markdown("# 🚀 Shasha AI", elem_id="main_title")
+    gr.Markdown("Your AI partner for generating, modifying, and understanding code.", elem_id="subtitle")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 1. Select Model")
+            model_dd = gr.Dropdown(
+                choices=[m["name"] for m in AVAILABLE_MODELS],
+                value=initial_model["name"],
+                label="AI Model"
+            )
+
+            gr.Markdown("### 2. Provide Context")
+            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.Row():
+                clr_btn = gr.Button("Clear Session", variant="secondary")
+                gen_btn = gr.Button("Generate Code", variant="primary", elem_id="gen_btn")
+
+        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],
+    )
+
+    clr_btn.click(
+        lambda: ("", None, "", [], "", "", []),
+        outputs=[prompt_in, file_in, url_in, history_state, code_out, preview_out, chat_out],
+        queue=False,
     )
 
-# ── static assets (.css / .js) are picked up automatically by HF Spaces
 if __name__ == "__main__":
     demo.queue().launch()