User's custom AI Agent deploy

Dockerfile

@@ -1,8 +1,23 @@
-FROM python:3.11-slim
-RUN apt-get update -qq && apt-get install -y -qq curl wget git build-essential && rm -rf /var/lib/apt/lists/*
+FROM python:3.10-slim
+
+# System dependencies (Web browsing aur tools ke liye jaruri packages)
+RUN apt-get update && apt-get install -y \
+    curl \
+    gcc \
+    python3-dev \
+    && rm -rf /var/lib/apt/lists/*
+
 WORKDIR /app
+
+# Dependencies install karein
 COPY requirements.txt .
-RUN pip3 install --no-cache-dir -r requirements.txt
-COPY app.py .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Pura code copy karein
+COPY . .
+
+# Hugging Face Space default port 7860 use karta hai
 EXPOSE 7860
-CMD ["python3", "app.py"]
+ENV GRADIO_SERVER_NAME="0.0.0.0"
+
+CMD ["python", "app.py"]

app.py

@@ -1,172 +1,186 @@
-"""
-AIAGENT - Aapka Personal AI Agent
-smolagents + Gradio on Hugging Face Spaces
-"""
-
+import os
+import json
+import io
+import sys
+import requests
 import gradio as gr
-from smolagents import CodeAgent, InferenceClientModel, tool, DuckDuckGoSearchTool
-import subprocess, os
-from datetime import datetime
-
-HF_TOKEN = os.environ.get("HF_TOKEN", None)
+from duckduckgo_search import DDGS
+from bs4 import BeautifulSoup
 
-@tool
-def execute_python(code: str) -> str:
-    """Python code execute karo.
+# Hugging Face Token Space Settings -> Secrets mein 'HF_TOKEN' naam se save karein
+HF_TOKEN = os.environ.get("HF_TOKEN")
+API_URL = "https://api-inference.huggingface.co/models/Qwen/Qwen2.5-72B-Instruct/v1/chat/completions"
+HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}
 
-    Args:
-        code: Python code to execute.
+# ==========================================
+# 1. CORE TOOLS DEFINITIONS (LobeHub style)
+# ==========================================
 
-    Returns:
-        Output string with results or error.
-    """
-    fname = f"/tmp/agent_{datetime.now().strftime('%Y%m%d_%H%M%S')}.py"
-    with open(fname, "w") as f:
-        f.write(code)
+def web_search(query: str) -> str:
+    """Internet par live search karne ke liye."""
     try:
-        r = subprocess.run(["python3", fname], capture_output=True, text=True, timeout=30)
-        o = ""
-        if r.stdout: o += f"OUTPUT:\n{r.stdout}\n"
-        if r.stderr: o += f"ERRORS:\n{r.stderr}\n"
-        return o if o else "Done (no output)"
+        with DDGS() as ddgs:
+            results = list(ddgs.text(query, max_results=3))
+            return json.dumps([{"title": r['title'], "snippet": r['body'], "link": r['href']} for r in results])
     except Exception as e:
-        return f"Error: {str(e)}"
-    finally:
-        if os.path.exists(fname): os.remove(fname)
-
-@tool
-def create_file(filename: str, content: str) -> str:
-    """Create a file with content.
-
-    Args:
-        filename: File path like hello.py.
-        content: Content to write.
-
-    Returns:
-        Success message.
-    """
-    os.makedirs(os.path.dirname(os.path.abspath(filename)) or ".", exist_ok=True)
-    with open(filename, "w") as f:
-        f.write(content)
-    return f"Created: {filename} ({len(content)}b)"
-
-@tool
-def read_file(filename: str) -> str:
-    """Read content of a file.
-
-    Args:
-        filename: File path to read.
-
-    Returns:
-        File content or error.
-    """
-    if not os.path.exists(filename):
-        return f"Not found: {filename}"
-    with open(filename, "r") as f:
-        return f.read()
-
-@tool
-def run_shell(command: str) -> str:
-    """Run a Linux shell command.
-
-    Args:
-        command: Shell command to execute.
-
-    Returns:
-        Command output.
-    """
-    r = subprocess.run(command, shell=True, capture_output=True, text=True, timeout=30)
-    o = ""
-    if r.stdout: o += r.stdout
-    if r.stderr: o += f"\n[stderr] {r.stderr}"
-    return o if o else "Done"
-
-@tool
-def install_package(pkg: str) -> str:
-    """Install a Python package with pip.
+        return f"Search failed: {str(e)}"
 
-    Args:
-        pkg: Package name like requests.
-
-    Returns:
-        Installation output.
-    """
-    r = subprocess.run(["pip3", "install", pkg], capture_output=True, text=True, timeout=60)
-    return (r.stdout + "\n" + r.stderr).strip() or "Installed"
-
-@tool
-def list_files(path: str = ".") -> str:
-    """List files in a directory.
-
-    Args:
-        path: Directory path, default is current.
-
-    Returns:
-        File listing as string.
-    """
+def read_webpage(url: str) -> str:
+    """Kisi bhi URL ka text content padhne ke liye."""
     try:
-        files = os.listdir(path)
-        if not files: return "(empty)"
-        result = []
-        for f in sorted(files):
-            full = os.path.join(path, f)
-            if os.path.isfile(full):
-                result.append(f"  {f} ({os.path.getsize(full):,}b)")
-            else:
-                result.append(f"/ {f}/")
-        return "\n".join(result)
+        resp = requests.get(url, timeout=10, headers={"User-Agent": "Mozilla/5.0"})
+        soup = BeautifulSoup(resp.text, 'html.parser')
+        text = ' '.join(soup.stripped_strings)[:3000] # Token limit ke liye truncate
+        return text
     except Exception as e:
-        return f"Error: {str(e)}"
+        return f"Could not read webpage: {str(e)}"
 
-# Agent setup
-model = InferenceClientModel(
-    model_id="Qwen/Qwen2.5-72B-Instruct",
-    token=HF_TOKEN
-)
+def calculator(expression: str) -> str:
+    """Complex maths calculations solve karne ke liye."""
+    try:
+        # Sanitize input for basic security
+        allowed_chars = "0123456789+-*/(). "
+        if all(c in allowed_chars for c in expression):
+            return str(eval(expression, {"__builtins__": {}}, {}))
+        return "Error: Invalid characters in math expression."
+    except Exception as e:
+        return f"Math error: {str(e)}"
 
-tools = [
-    DuckDuckGoSearchTool(), execute_python, create_file,
-    read_file, run_shell, install_package, list_files,
+def python_interpreter(code: str) -> str:
+    """Python code run karke logic execute karne ke liye (Sandbox)."""
+    old_stdout = sys.stdout
+    redirected_output = sys.stdout = io.StringIO()
+    try:
+        # Docker container ke andar safe execution environment
+        exec(code, {"__builtins__": __builtins__}, {})
+        sys.stdout = old_stdout
+        return redirected_output.getvalue() or "Code executed successfully with no output."
+    except Exception as e:
+        sys.stdout = old_stdout
+        return f"Execution Error: {str(e)}"
+
+# ==========================================
+# 2. LLM TOOL SCHEMA (JSON Format)
+# ==========================================
+
+TOOLS = [
+    {
+        "type": "function",
+        "function": {
+            "name": "web_search",
+            "description": "Use this tool to search the internet for current events, news, or general info.",
+            "parameters": {
+                "type": "object",
+                "properties": {"query": {"type": "string", "description": "The search query"}},
+                "required": ["query"]
+            }
+        }
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "read_webpage",
+            "description": "Extract raw text content from a given website URL.",
+            "parameters": {
+                "type": "object",
+                "properties": {"url": {"type": "string", "description": "The full web URL"}},
+                "required": ["url"]
+            }
+        }
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "calculator",
+            "description": "Evaluate mathematical expressions. Input should only contain numbers and basic operators.",
+            "parameters": {
+                "type": "object",
+                "properties": {"expression": {"type": "string", "description": "The math expression, e.g. (55 * 4) + 12"}},
+                "required": ["expression"]
+            }
+        }
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "python_interpreter",
+            "description": "Execute Python code to solve complex logical problems, data manipulation, or algorithms.",
+            "parameters": {
+                "type": "object",
+                "properties": {"code": {"type": "string", "description": "Clean Python code block"}},
+                "required": ["code"]
+            }
+        }
+    }
 ]
 
-agent = CodeAgent(
-    tools=tools, model=model, max_steps=15,
-    additional_authorized_imports=[
-        "requests","json","os","datetime","math","random","re",
-    ]
-)
-
-def chat_fn(msg, history):
-    if not msg.strip():
-        return "", history
+def execute_tool(name, args):
+    if name == "web_search": return web_search(args.get("query"))
+    if name == "read_webpage": return read_webpage(args.get("url"))
+    if name == "calculator": return calculator(args.get("expression"))
+    if name == "python_interpreter": return python_interpreter(args.get("code"))
+    return "Unknown tool"
+
+# ==========================================
+# 3. AGENT CORE LOOP
+# ==========================================
+
+def run_agent(message, history):
+    # Chat history formatting
+    messages = [{"role": "system", "content": "You are a helpful AI Agent equipped with advanced tools. Use them whenever necessary to give accurate answers."}]
+    for user, bot in history:
+        messages.append({"role": "user", "content": user})
+        if bot: messages.append({"role": "assistant", "content": bot})
+    messages.append({"role": "user", "content": message})
+
+    payload = {
+        "model": "Qwen/Qwen2.5-72B-Instruct",
+        "messages": messages,
+        "tools": TOOLS,
+        "tool_choice": "auto"
+    }
+    
     try:
-        result = agent.run(f"User: {msg}")
-        history.append((msg, str(result)))
-        return "", history
+        response = requests.post(API_URL, headers=HEADERS, json=payload).json()
+        choice = response["choices"][0]["message"]
+        
+        # Check if LLM wants to use a tool
+        if choice.get("tool_calls"):
+            tool_call = choice["tool_calls"][0]
+            func_name = tool_call["function"]["name"]
+            func_args = json.loads(tool_call["function"]["arguments"])
+            
+            # Execute selected tool
+            tool_output = execute_tool(func_name, func_args)
+            
+            # Feed tool result back to LLM
+            messages.append(choice)
+            messages.append({
+                "role": "tool",
+                "name": func_name,
+                "content": tool_output,
+                "tool_call_id": tool_call.get("id", "call_1")
+            })
+            
+            # Final LLM call to generate user response
+            final_payload = {"model": "Qwen/Qwen2.5-72B-Instruct", "messages": messages}
+            final_response = requests.post(API_URL, headers=HEADERS, json=final_payload).json()
+            return final_response["choices"][0]["message"]["content"]
+            
+        return choice["content"]
     except Exception as e:
-        import traceback
-        history.append((msg, f"Error: {str(e)}\n{traceback.format_exc()}"))
-        return "", history
+        return f"API Error: Kripya check karein ki HF_TOKEN correctly set hai ya nahi. Details: {str(e)}"
 
-def clear_fn():
-    return [], ""
+# ==========================================
+# 4. GRADIO INTERFACE
+# ==========================================
 
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.HTML("""
-    <div style="text-align:center;margin-bottom:10px">
-        <h1 style="background:linear-gradient(135deg,#667eea,#764ba2);-webkit-background-clip:text;-webkit-text-fill-color:transparent;font-size:2em">AIAGENT</h1>
-        <p style="color:#666">Python Code | Web Search | Files | Shell</p>
-    </div>
-    """)
-    chatbot = gr.Chatbot(height=450, bubble_full_width=False, avatar_images=(None, "AI"))
-    with gr.Row():
-        msg = gr.Textbox(label="", placeholder="Kuch bhi likho...", scale=4, container=False)
-        btn = gr.Button("Send", variant="primary", scale=1)
-    with gr.Row():
-        clr = gr.Button("Clear", variant="secondary", size="sm")
-    msg.submit(chat_fn, [msg, chatbot], [msg, chatbot])
-    btn.click(chat_fn, [msg, chatbot], [msg, chatbot])
-    clr.click(clear_fn, None, [chatbot, msg])
+demo = gr.ChatInterface(
+    fn=run_agent,
+    title="📦 LobeHub-Style Docker Agent",
+    description="Docker container backend ke sath chalne wala Agent: Search, Browser, Math aur Python Interpreter sab free!",
+    theme="soft"
+)
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+    demo.launch()

requirements.txt

@@ -1,4 +1,4 @@
-gradio>=5.0.0
-smolagents>=1.0.0
-huggingface-hub>=0.20.0
-requests>=2.31.0
+gradio
+requests
+beautifulsoup4
+duckduckgo-search