Update app/main.py

app/main.py

@@ -5,6 +5,9 @@ import gradio as gr
 from openai import OpenAI
 from pydoc import html
 from typing import List, Generator, Optional
+import requests
+from bs4 import BeautifulSoup
+import re
 
 # تعريف LATEX_DELIMS
 LATEX_DELIMS = [
@@ -25,6 +28,7 @@ logger.info("Files in /app/: %s", os.listdir("/app"))
 HF_TOKEN = os.getenv("HF_TOKEN")
 API_ENDPOINT = os.getenv("API_ENDPOINT", "https://api-inference.huggingface.co/v1")
 MODEL_NAME = os.getenv("MODEL_NAME", "openai/gpt-oss-120b:cerebras")
+SECONDARY_MODEL_NAME = os.getenv("SECONDARY_MODEL_NAME", "MGZON/mgzon-flan-t5-base")
 if not HF_TOKEN:
     logger.error("HF_TOKEN is not set in environment variables.")
     raise ValueError("HF_TOKEN is required for Inference API.")
@@ -34,7 +38,63 @@ client = OpenAI(api_key=HF_TOKEN, base_url=API_ENDPOINT)
 QUEUE_SIZE = int(os.getenv("QUEUE_SIZE", 80))
 CONCURRENCY_LIMIT = int(os.getenv("CONCURRENCY_LIMIT", 20))
 
-# دالة request_generation (دمج من gateway.py)
+# كلمات مفتاحية لتحديد إذا كان السؤال متعلق بـ MGZon
+MGZON_KEYWORDS = [
+    "mgzon", "mgzon products", "mgzon services", "mgzon data", "mgzon platform",
+    "mgzon features", "mgzon mission", "mgzon technology", "mgzon solutions"
+]
+
+# دالة لاختيار النموذج تلقائيًا
+def select_model(query: str) -> str:
+    """Selects the appropriate model based on the query content."""
+    query_lower = query.lower()
+    for keyword in MGZON_KEYWORDS:
+        if keyword in query_lower:
+            logger.info(f"Selected {SECONDARY_MODEL_NAME} for MGZon-related query: {query}")
+            return SECONDARY_MODEL_NAME
+    logger.info(f"Selected {MODEL_NAME} for general query: {query}")
+    return MODEL_NAME
+
+# دالة بحث ويب محسنة
+def web_search(query: str) -> str:
+    try:
+        google_api_key = os.getenv("GOOGLE_API_KEY")
+        google_cse_id = os.getenv("GOOGLE_CSE_ID")
+        if not google_api_key or not google_cse_id:
+            return "Web search requires GOOGLE_API_KEY and GOOGLE_CSE_ID to be set."
+
+        url = f"https://www.googleapis.com/customsearch/v1?key={google_api_key}&cx={google_cse_id}&q={query}"
+        response = requests.get(url)
+        response.raise_for_status()
+        results = response.json().get("items", [])
+        if not results:
+            return "No web results found."
+
+        # جمع النتايج
+        search_results = []
+        for i, item in enumerate(results[:3]):  # نأخذ أول 3 نتايج
+            title = item.get("title", "")
+            snippet = item.get("snippet", "")
+            link = item.get("link", "")
+            # محاولة استخراج محتوى الصفحة
+            try:
+                page_response = requests.get(link, timeout=5)
+                page_response.raise_for_status()
+                soup = BeautifulSoup(page_response.text, "html.parser")
+                # استخراج النصوص من الصفحة (فقط الفقرات)
+                paragraphs = soup.find_all("p")
+                page_content = " ".join([p.get_text() for p in paragraphs][:500])  # نأخذ أول 500 حرف
+            except Exception as e:
+                logger.warning(f"Failed to fetch page content for {link}: {e}")
+                page_content = snippet
+            search_results.append(f"Result {i+1}:\nTitle: {title}\nLink: {link}\nContent: {page_content}\n")
+        
+        return "\n".join(search_results)
+    except Exception as e:
+        logger.exception("Web search failed")
+        return f"Web search error: {e}"
+
+# دالة request_generation (محدثة لدعم المهام المتعددة)
 def request_generation(
     api_key: str,
     api_base: str,
@@ -42,11 +102,12 @@ def request_generation(
     system_prompt: str,
     model_name: str,
     chat_history: Optional[List[dict]] = None,
-    temperature: float = 0.3,
-    max_new_tokens: int = 1024,
+    temperature: float = 0.9,
+    max_new_tokens: int = 2048,
     reasoning_effort: str = "off",
     tools: Optional[List[dict]] = None,
     tool_choice: Optional[str] = None,
+    deep_search: bool = False,
 ) -> Generator[str, None, None]:
     """Streams Responses API events. Emits:
       - "analysis" sentinel once, then raw reasoning deltas
@@ -54,18 +115,43 @@ def request_generation(
     If no visible deltas, emits a tool-call fallback message."""
     client = OpenAI(api_key=api_key, base_url=api_base)
 
+    # تحديد نوع المهمة بناءً على السؤال
+    task_type = "general"
+    if "code" in message.lower() or "programming" in message.lower() or any(ext in message.lower() for ext in ["python", "javascript", "react", "django", "flask"]):
+        task_type = "code"
+        enhanced_system_prompt = f"{system_prompt}\nYou are an expert programmer. Provide accurate, well-commented code with examples and explanations. Support frameworks like React, Django, Flask, and others as needed."
+    elif any(keyword in message.lower() for keyword in ["analyze", "analysis", "تحليل"]):
+        task_type = "analysis"
+        enhanced_system_prompt = f"{system_prompt}\nProvide detailed analysis with step-by-step reasoning, examples, and data-driven insights."
+    elif any(keyword in message.lower() for keyword in ["review", "مراجعة"]):
+        task_type = "review"
+        enhanced_system_prompt = f"{system_prompt}\nReview the provided content thoroughly, identify issues, and suggest improvements with detailed explanations."
+    elif any(keyword in message.lower() for keyword in ["publish", "نشر"]):
+        task_type = "publish"
+        enhanced_system_prompt = f"{system_prompt}\nPrepare content for publishing, ensuring clarity, professionalism, and adherence to best practices."
+    else:
+        enhanced_system_prompt = f"{system_prompt}\nPlease provide detailed and comprehensive responses, including explanations, examples, and relevant details where applicable."
+
+    logger.info(f"Task type detected: {task_type}")
+
     # تنظيف الـ messages من metadata
-    input_messages: List[dict] = [{"role": "system", "content": system_prompt}]
+    input_messages: List[dict] = [{"role": "system", "content": enhanced_system_prompt}]
     if chat_history:
         for msg in chat_history:
             clean_msg = {"role": msg.get("role"), "content": msg.get("content")}
             if clean_msg["content"]:
                 input_messages.append(clean_msg)
-    input_messages.append({"role": "user", "content": message})
-
-    # إعداد tools و tool_choice
-    tools = tools if tools else []
-    tool_choice = tool_choice if tool_choice in ["auto", "none", "any", "required"] else "none"
+    
+    # إذا كان DeepSearch مفعّل أو السؤال عام، أضف نتائج البحث
+    if deep_search or model_name == MODEL_NAME:
+        search_result = web_search(message)
+        input_messages.append({"role": "user", "content": f"User query: {message}\nWeb search context: {search_result}"})
+    else:
+        input_messages.append({"role": "user", "content": message})
+
+    # إعداد tools و tool_choice (فقط لـ GPT-based models)
+    tools = tools if tools and "gpt-oss" in model_name else []
+    tool_choice = tool_choice if tool_choice in ["auto", "none", "any", "required"] and "gpt-oss" in model_name else "none"
 
     try:
         stream = client.chat.completions.create(
@@ -107,7 +193,7 @@ def request_generation(
                     buffer = ""
                 continue
 
-            if chunk.choices[0].delta.tool_calls:
+            if chunk.choices[0].delta.tool_calls and "gpt-oss" in model_name:
                 tool_call = chunk.choices[0].delta.tool_calls[0]
                 name = getattr(tool_call, "function", {}).get("name", None)
                 args = getattr(tool_call, "function", {}).get("arguments", None)
@@ -167,6 +253,9 @@ def generate(message, history, system_prompt, temperature, reasoning_effort, ena
         yield "Please enter a prompt."
         return
 
+    # اختيار النموذج تلقائيًا
+    model_name = select_model(message)
+
     # Flatten gradio history وتنظيف metadata
     chat_history = []
     for h in history:
@@ -179,22 +268,38 @@ def generate(message, history, system_prompt, temperature, reasoning_effort, ena
             if u: chat_history.append({"role": "user", "content": u})
             if a: chat_history.append({"role": "assistant", "content": a})
 
-    # إعداد الأدوات إذا تم تفعيل البحث على الويب
+    # إعداد الأدوات
     tools = [
         {
             "type": "function",
             "function": {
                 "name": "web_search_preview",
-                "description": "Simulate web search for additional context",
+                "description": "Perform a web search to gather additional context",
                 "parameters": {
                     "type": "object",
                     "properties": {"query": {"type": "string", "description": "Search query"}},
                     "required": ["query"],
                 },
             },
+        },
+        {
+            "type": "function",
+            "function": {
+                "name": "code_generation",
+                "description": "Generate or modify code for various frameworks (React, Django, Flask, etc.)",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "code": {"type": "string", "description": "Existing code to modify or empty for new code"},
+                        "framework": {"type": "string", "description": "Framework (e.g., React, Django, Flask)"},
+                        "task": {"type": "string", "description": "Task description (e.g., create a component, fix a bug)"},
+                    },
+                    "required": ["task"],
+                },
+            },
         }
-    ] if enable_browsing else []
-    tool_choice = "auto" if enable_browsing else "none"
+    ] if "gpt-oss" in model_name else []
+    tool_choice = "auto" if "gpt-oss" in model_name else "none"
 
     in_analysis = False
     in_visible = False
@@ -220,12 +325,13 @@ def generate(message, history, system_prompt, temperature, reasoning_effort, ena
             api_base=API_ENDPOINT,
             message=message,
             system_prompt=system_prompt,
-            model_name=MODEL_NAME,
+            model_name=model_name,
             chat_history=chat_history,
             temperature=temperature,
             max_new_tokens=max_new_tokens,
             tools=tools,
             tool_choice=tool_choice,
+            deep_search=enable_browsing or model_name == MODEL_NAME,
         )
 
         for chunk in stream:
@@ -275,30 +381,32 @@ chatbot_ui = gr.ChatInterface(
     fn=generate,
     type="messages",
     chatbot=gr.Chatbot(
-        label="MGZon-120B Chatbot",
+        label="MGZon Chatbot",
         type="messages",
         height=600,
         latex_delimiters=LATEX_DELIMS,
     ),
     additional_inputs_accordion=gr.Accordion("⚙️ Settings", open=True),
     additional_inputs=[
-        gr.Textbox(label="System prompt", value="You are a helpful assistant.", lines=2),
-        gr.Slider(label="Temperature", minimum=0.0, maximum=1.0, step=0.1, value=0.7),
+        gr.Textbox(label="System prompt", value="You are a helpful assistant capable of code generation, analysis, review, and more.", lines=2),
+        gr.Slider(label="Temperature", minimum=0.0, maximum=1.0, step=0.1, value=0.9),
         gr.Radio(label="Reasoning Effort", choices=["low", "medium", "high"], value="medium"),
-        gr.Checkbox(label="Enable web browsing (simulated)", value=False),
-        gr.Slider(label="Max New Tokens", minimum=50, maximum=1024, step=50, value=200),
+        gr.Checkbox(label="Enable DeepSearch (web browsing)", value=True),
+        gr.Slider(label="Max New Tokens", minimum=50, maximum=2048, step=50, value=2048),
     ],
     stop_btn="Stop",
     examples=[
         ["Explain the difference between supervised and unsupervised learning."],
-        ["Summarize the plot of Inception in two sentences."],
-        ["Show me the LaTeX for the quadratic formula."],
-        ["What are advantages of AMD Instinct MI300X GPU?"],
-        ["Derive the gradient of softmax cross-entropy loss."],
-        ["Explain why ∂/∂x xⁿ = n·xⁿ⁻¹ holds."],
+        ["Generate a React component for a login form."],
+        ["Review this Python code: print('Hello World')"],
+        ["Analyze the performance of a Django REST API."],
+        ["Tell me about MGZon products and services."],
+        ["Create a Flask route for user authentication."],
+        ["What are the latest trends in AI?"],
+        ["Provide guidelines for publishing a technical blog post."],
     ],
-    title="MGZon-120B Chatbot on Hugging Face",
-    description="This Space demonstrates the  GPT-MGZon-120B model running via Hugging Face Inference API. Includes analysis for chain of thought insights. Licensed under Apache 2.0. ***DISCLAIMER:*** Analysis may contain internal thoughts not suitable for final response.",
+    title="MGZon Chatbot",
+    description="A versatile chatbot powered by GPT-OSS-120B and MGZon-Flan-T5-Base (auto-selected based on query). Supports code generation, analysis, review, web search, and MGZon-specific queries. Licensed under Apache 2.0. ***DISCLAIMER:*** Analysis may contain internal thoughts not suitable for final response.",
     theme="gradio/soft",
     css=css,
 )
@@ -307,7 +415,7 @@ chatbot_ui = gr.ChatInterface(
 from fastapi import FastAPI
 from gradio import mount_gradio_app
 
-app = FastAPI(title="MGZon-120B API")
+app = FastAPI(title="MGZon Chatbot API")
 app = mount_gradio_app(app, chatbot_ui, path="/")
 
 # تشغيل الخادم