Shorten short_description to meet Hugging Face metadata requirements

main.py

@@ -1,5 +1,4 @@
 import os
-import io
 import logging
 from fastapi import FastAPI, Request
 from fastapi.responses import HTMLResponse, RedirectResponse
@@ -8,105 +7,166 @@ 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
+import io
 
-# ================= إعداد اللوج =================
+# إعداد التسجيل
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
+# تحقق من الملفات في /app/
 logger.info("Files in /app/: %s", os.listdir("/app"))
 
-# ================= مفاتيح HuggingFace =================
+# إعداد العميل لـ Hugging Face Inference API
 HF_TOKEN = os.getenv("HF_TOKEN")
-BACKUP_HF_TOKEN = os.getenv("BACKUP_HF_TOKEN")
+BACKUP_HF_TOKEN = os.getenv("BACKUP_HF_TOKEN")  # إضافة التوكن الاحتياطي
 if not HF_TOKEN:
     logger.error("HF_TOKEN is not set in environment variables.")
     raise ValueError("HF_TOKEN is required for Inference API.")
 
-# ================= إعداد Queue =================
+# إعدادات الـ queue
 QUEUE_SIZE = int(os.getenv("QUEUE_SIZE", 80))
 CONCURRENCY_LIMIT = int(os.getenv("CONCURRENCY_LIMIT", 20))
 
-# ================= CSS =================
+# إعداد CSS
 css = """
-#input-row {display:flex; gap:6px; align-items:center;}
-#msg-box {flex:1;}
+.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;
+}
 """
 
-# ================= دالة المعالجة =================
-def process_input(message, history, audio_input=None, file_input=None):
+# دالة لمعالجة الإدخال (نص، صوت، صور، ملفات)
+def process_input(message, audio_input=None, file_input=None, history=None, system_prompt=None, temperature=0.7, reasoning_effort="medium", enable_browsing=True, max_new_tokens=128000):
     input_type = "text"
-    audio_data, image_data = None, None
-
-    if audio_input:  # 🎤 صوت
+    audio_data = None
+    image_data = None
+    if audio_input:
         input_type = "audio"
         with open(audio_input, "rb") as f:
             audio_data = f.read()
         message = "Transcribe this audio"
-
-    elif file_input:  # 📎 صورة / ملف
+    elif file_input:
+        input_type = "file"
         if file_input.endswith(('.png', '.jpg', '.jpeg')):
             input_type = "image"
             with open(file_input, "rb") as f:
                 image_data = f.read()
             message = f"Analyze image: {file_input}"
         else:
-            input_type = "file"
             message = f"Analyze file: {file_input}"
-
-    response_text, audio_response = "", None
+    
+    response_text = ""
+    audio_response = None
     for chunk in generate(
         message=message,
         history=history,
+        system_prompt=system_prompt,
+        temperature=temperature,
+        reasoning_effort=reasoning_effort,
+        enable_browsing=enable_browsing,
+        max_new_tokens=max_new_tokens,
         input_type=input_type,
         audio_data=audio_data,
         image_data=image_data
     ):
-        if isinstance(chunk, bytes):  # 🔊 صوت
+        if isinstance(chunk, bytes):
             audio_response = io.BytesIO(chunk)
-            audio_response.name = "reply.wav"
-        else:  # 📝 نص
+            audio_response.name = "response.wav"
+        else:
             response_text += chunk
-
         yield response_text, audio_response
 
-# ================= 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")
-        send_btn = gr.Button("Send")
-
-    voice_reply = gr.Audio(label="🔊 Voice Reply", type="filepath", autoplay=True)
-
-    def user_submit(message, history):
-        history = history + [(message, None)]
-        return "", history
-
-    msg.submit(user_submit, [msg, state], [msg, state]).then(
-        process_input, [msg, state, mic, file], [chatbot, voice_reply]
-    )
-    send_btn.click(user_submit, [msg, state], [msg, state]).then(
-        process_input, [msg, state, mic, file], [chatbot, voice_reply]
-    )
-
-# ================= FastAPI =================
+# 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
 app = FastAPI(title="MGZon Chatbot API")
 
-# ربط Gradio داخل FastAPI
+# ربط Gradio مع FastAPI
 app = gr.mount_gradio_app(app, chatbot_ui, path="/gradio")
 
-# ملفات ثابتة + قوالب
+# ربط الملفات الثابتة والقوالب
 app.mount("/static", StaticFiles(directory="static"), name="static")
 templates = Jinja2Templates(directory="templates")
 
-# Middleware 404
+# Middleware لمعالجة 404
 class NotFoundMiddleware(BaseHTTPMiddleware):
     async def dispatch(self, request: Request, call_next):
         try:
@@ -121,28 +181,27 @@ class NotFoundMiddleware(BaseHTTPMiddleware):
 
 app.add_middleware(NotFoundMiddleware)
 
-# Root
+# Root endpoint
 @app.get("/", response_class=HTMLResponse)
 async def root(request: Request):
     return templates.TemplateResponse("index.html", {"request": request})
 
-# Docs
+# Docs endpoint
 @app.get("/docs", response_class=HTMLResponse)
 async def docs(request: Request):
     return templates.TemplateResponse("docs.html", {"request": request})
 
-# Swagger
+# Swagger UI endpoint
 @app.get("/swagger", response_class=HTMLResponse)
 async def swagger_ui():
     return get_swagger_ui_html(openapi_url="/openapi.json", title="MGZon API Documentation")
 
-# Redirect
+# Redirect لـ /gradio
 @app.get("/launch-chatbot", response_class=RedirectResponse)
 async def launch_chatbot():
     return RedirectResponse(url="/gradio", status_code=302)
 
-# Run
+# تشغيل الخادم
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=int(os.getenv("PORT", 7860)))
-