Shorten short_description to meet Hugging Face metadata requirements

main.py

@@ -1,4 +1,5 @@
 import os
+import io
 import logging
 from fastapi import FastAPI, Request
 from fastapi.responses import HTMLResponse, RedirectResponse
@@ -7,80 +8,34 @@ from fastapi.templating import Jinja2Templates
 from starlette.middleware.base import BaseHTTPMiddleware
 from fastapi.openapi.docs import get_swagger_ui_html
 import gradio as gr
-from api.endpoints import router as api_router
-from utils.generation import generate, LATEX_DELIMS
 
+from api.endpoints import router as api_router
 from utils.generation import generate, LATEX_DELIMS
 
-# إعداد التسجيل
+# ================= إعداد اللوج =================
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# تحقق من الملفات في /app/
 logger.info("Files in /app/: %s", os.listdir("/app"))
 
-# إعداد العميل لـ Hugging Face Inference API
+# ================= مفاتيح HuggingFace =================
 HF_TOKEN = os.getenv("HF_TOKEN")
-BACKUP_HF_TOKEN = os.getenv("BACKUP_HF_TOKEN")  # إضافة التوكن الاحتياطي
+BACKUP_HF_TOKEN = os.getenv("BACKUP_HF_TOKEN")
 if not HF_TOKEN:
     logger.error("HF_TOKEN is not set in environment variables.")
     raise ValueError("HF_TOKEN is required for Inference API.")
 
-# إعدادات الـ queue
+# ================= إعداد Queue =================
 QUEUE_SIZE = int(os.getenv("QUEUE_SIZE", 80))
 CONCURRENCY_LIMIT = int(os.getenv("CONCURRENCY_LIMIT", 20))
 
-# إعداد CSS
+# ================= CSS =================
 css = """
-.gradio-container { max-width: 1200px; margin: auto; }
-.chatbot { border: 1px solid #ccc; border-radius: 10px; padding: 15px; background-color: #f9f9f9; }
-.input-textbox { font-size: 16px; padding: 10px; }
-.upload-button::before {
-    content: '📷';
-    margin-right: 8px;
-    font-size: 22px;
-}
-.audio-input::before {
-    content: '🎤';
-    margin-right: 8px;
-    font-size: 22px;
-}
-.audio-output::before {
-    content: '🔊';
-    margin-right: 8px;
-    font-size: 22px;
-}
-.loading::after {
-    content: '';
-    display: inline-block;
-    width: 16px;
-    height: 16px;
-    border: 2px solid #333;
-    border-top-color: transparent;
-    border-radius: 50%;
-    animation: spin 1s linear infinite;
-    margin-left: 8px;
-}
-@keyframes spin {
-    to { transform: rotate(360deg); }
-}
-.output-container {
-    margin-top: 20px;
-    padding: 10px;
-    border: 1px solid #ddd;
-    border-radius: 8px;
-}
-.audio-output-container {
-    display: flex;
-    align-items: center;
-    gap: 10px;
-    margin-top: 10px;
-}
+#input-row {display:flex; gap:6px; align-items:center;}
+#msg-box {flex:1;}
 """
 
-# دالة لمعالجة الإدخال (نص، صوت، صور، ملفات)
-
-# === دالة المعالجة ===
+# ================= دالة المعالجة =================
 def process_input(message, history, audio_input=None, file_input=None):
     input_type = "text"
     audio_data, image_data = None, None
@@ -102,31 +57,30 @@ def process_input(message, history, audio_input=None, file_input=None):
             message = f"Analyze file: {file_input}"
 
     response_text, audio_response = "", None
-    for chunk in generate(message=message, history=history,
-                          input_type=input_type,
-                          audio_data=audio_data,
-                          image_data=image_data):
-        if isinstance(chunk, bytes):
+    for chunk in generate(
+        message=message,
+        history=history,
+        input_type=input_type,
+        audio_data=audio_data,
+        image_data=image_data
+    ):
+        if isinstance(chunk, bytes):  # 🔊 صوت
             audio_response = io.BytesIO(chunk)
             audio_response.name = "reply.wav"
-        else:
+        else:  # 📝 نص
             response_text += chunk
 
         yield response_text, audio_response
 
-# === Gradio UI ===
-with gr.Blocks(css="""
-#input-row {display:flex; gap:6px; align-items:center;}
-#msg-box {flex:1;}
-""") as demo:
-    chatbot = gr.Chatbot(label="MGZon Chatbot", height=700,
-                         latex_delimiters=LATEX_DELIMS)
+# ================= واجهة Gradio =================
+with gr.Blocks(css=css, theme="gradio/soft") as chatbot_ui:
+    chatbot = gr.Chatbot(label="MGZon Chatbot", height=700, latex_delimiters=LATEX_DELIMS)
     state = gr.State([])
 
     with gr.Row(elem_id="input-row"):
         msg = gr.Textbox(placeholder="Type your message...", elem_id="msg-box")
-        mic = gr.Audio(sources=["microphone"], type="filepath", label="", elem_classes="audio-input")
-        file = gr.File(file_types=["image", ".pdf", ".txt"], label="", elem_classes="upload-button")
+        mic = gr.Audio(sources=["microphone"], type="filepath", label="🎤", elem_classes="audio-input")
+        file = gr.File(file_types=["image", ".pdf", ".txt"], label="📎", elem_classes="upload-button")
         send_btn = gr.Button("Send")
 
     voice_reply = gr.Audio(label="🔊 Voice Reply", type="filepath", autoplay=True)
@@ -142,59 +96,17 @@ with gr.Blocks(css="""
         process_input, [msg, state, mic, file], [chatbot, voice_reply]
     )
 
-demo.launch()
-
-
-
-# إعداد واجهة Gradio
-chatbot_ui = gr.ChatInterface(
-    fn=process_input,
-    chatbot=gr.Chatbot(
-        label="MGZon Chatbot",     
-        height=800,
-        latex_delimiters=LATEX_DELIMS,
-    ),
-    additional_inputs_accordion=gr.Accordion("⚙️ Settings", open=True),
-    additional_inputs=[
-        gr.Textbox(
-            label="System Prompt",
-            value="You are an expert assistant providing detailed, comprehensive, and well-structured responses. Support text, audio, image, and file inputs. For audio, transcribe using Whisper. For text-to-speech, use Parler-TTS. For images and files, analyze content appropriately. Continue generating content until the query is fully addressed, leveraging the full capacity of the model.",
-            lines=4
-        ),
-        gr.Slider(label="Temperature", minimum=0.0, maximum=1.0, step=0.1, value=0.7),
-        gr.Radio(label="Reasoning Effort", choices=["low", "medium", "high"], value="medium"),
-        gr.Checkbox(label="Enable DeepSearch (web browsing)", value=True),
-        gr.Slider(label="Max New Tokens", minimum=50, maximum=128000, step=50, value=128000),
-        gr.Audio(label="Voice Input", type="filepath", elem_classes="audio-input"),
-        gr.File(label="Upload Image/File", file_types=["image", ".pdf", ".txt"], elem_classes="upload-button"),
-    ],
-    additional_outputs=[gr.Audio(label="Voice Output", type="filepath", elem_classes="audio-output", autoplay=True)],
-    stop_btn="Stop",
-    examples=[
-        ["Explain the difference between supervised and unsupervised learning in detail with examples."],
-        ["Generate a complete React component for a login form with form validation and error handling."],
-        ["Describe this image: https://example.com/image.jpg"],
-        ["Transcribe this audio: [upload audio file]."],
-        ["Convert this text to speech: Hello, welcome to MGZon!"],
-        ["Analyze this file: [upload PDF or text file]."],
-    ],
-    title="MGZon Chatbot",
-    description="A versatile chatbot powered by DeepSeek, CLIP, Whisper, and Parler-TTS for text, image, audio, and file queries. Supports long responses, voice input/output, file uploads with custom icons, and backup token switching. Licensed under Apache 2.0.",
-    theme="gradio/soft",
-    css=css,
-)
-
-# إعداد FastAPI
+# ================= FastAPI =================
 app = FastAPI(title="MGZon Chatbot API")
 
-# ربط Gradio مع FastAPI
+# ربط Gradio داخل FastAPI
 app = gr.mount_gradio_app(app, chatbot_ui, path="/gradio")
 
-# ربط الملفات الثابتة والقوالب
+# ملفات ثابتة + قوالب
 app.mount("/static", StaticFiles(directory="static"), name="static")
 templates = Jinja2Templates(directory="templates")
 
-# Middleware لمعالجة 404
+# Middleware 404
 class NotFoundMiddleware(BaseHTTPMiddleware):
     async def dispatch(self, request: Request, call_next):
         try:
@@ -209,27 +121,28 @@ class NotFoundMiddleware(BaseHTTPMiddleware):
 
 app.add_middleware(NotFoundMiddleware)
 
-# Root endpoint
+# Root
 @app.get("/", response_class=HTMLResponse)
 async def root(request: Request):
     return templates.TemplateResponse("index.html", {"request": request})
 
-# Docs endpoint
+# Docs
 @app.get("/docs", response_class=HTMLResponse)
 async def docs(request: Request):
     return templates.TemplateResponse("docs.html", {"request": request})
 
-# Swagger UI endpoint
+# Swagger
 @app.get("/swagger", response_class=HTMLResponse)
 async def swagger_ui():
     return get_swagger_ui_html(openapi_url="/openapi.json", title="MGZon API Documentation")
 
-# Redirect لـ /gradio
+# Redirect
 @app.get("/launch-chatbot", response_class=RedirectResponse)
 async def launch_chatbot():
     return RedirectResponse(url="/gradio", status_code=302)
 
-# تشغيل الخادم
+# Run
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=int(os.getenv("PORT", 7860)))
+