Update app.py

app.py

@@ -1,69 +1,293 @@
-from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
+from smolagents import (
+    CodeAgent,
+    DuckDuckGoSearchTool,
+    InferenceClientModel,
+    load_tool,
+    tool
+)
+
 import datetime
-import requests
 import pytz
 import yaml
-from tools.final_answer import FinalAnswerTool
 
+from tools.final_answer import FinalAnswerTool
 from Gradio_UI import GradioUI
 
-# Below is an example of a tool that does nothing. Amaze us with your creativity !
-@tool
-def my_custom_tool(arg1:str, arg2:int)-> str: #it's import to specify the return type
-    #Keep this format for the description / args / args description but feel free to modify the tool
-    """A tool that does nothing yet 
-    Args:
-        arg1: the first argument
-        arg2: the second argument
-    """
-    return "What magic will you build ?"
 
+# ---------------------------------------------------------
+# CUSTOM TOOL 1: Current time
+# ---------------------------------------------------------
 @tool
 def get_current_time_in_timezone(timezone: str) -> str:
-    """A tool that fetches the current local time in a specified timezone.
+    """
+    Gets the current local date and time for a timezone.
+
     Args:
-        timezone: A string representing a valid timezone (e.g., 'America/New_York').
+        timezone: Valid timezone such as America/New_York,
+                  Europe/London, or Asia/Kolkata.
     """
+
     try:
-        # Create timezone object
         tz = pytz.timezone(timezone)
-        # Get current time in that timezone
-        local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
-        return f"The current local time in {timezone} is: {local_time}"
+
+        local_time = datetime.datetime.now(tz).strftime(
+            "%Y-%m-%d %H:%M:%S"
+        )
+
+        return f"The current local time in {timezone} is {local_time}"
+
     except Exception as e:
-        return f"Error fetching time for timezone '{timezone}': {str(e)}"
+        return f"Could not get time for {timezone}: {str(e)}"
+
+
+# ---------------------------------------------------------
+# CUSTOM TOOL 2: AI technology impact analyzer
+# ---------------------------------------------------------
+@tool
+def analyze_ai_technology(topic: str) -> str:
+    """
+    Gives a quick engineering impact analysis for common AI technologies.
+
+    Args:
+        topic: AI technology or concept such as MCP, RAG,
+               LangGraph, vector database, agents, or embeddings.
+    """
+
+    technologies = {
+        "mcp": {
+            "name": "Model Context Protocol (MCP)",
+            "impact": 90,
+            "description":
+                "A standard protocol for connecting AI applications "
+                "to tools, APIs, services, and external data.",
+            "use_case":
+                "Let an AI agent access GitHub, databases, files, "
+                "Slack, APIs, and enterprise systems."
+        },
+
+        "rag": {
+            "name": "Retrieval-Augmented Generation (RAG)",
+            "impact": 88,
+            "description":
+                "Retrieves relevant information before asking an LLM "
+                "to generate an answer.",
+            "use_case":
+                "Enterprise search, documentation assistants, "
+                "knowledge bots, and support systems."
+        },
+
+        "langgraph": {
+            "name": "LangGraph",
+            "impact": 82,
+            "description":
+                "Framework for building stateful and multi-step "
+                "LLM workflows and agents.",
+            "use_case":
+                "Agent orchestration, approval workflows, "
+                "multi-agent systems, and long-running AI tasks."
+        },
+
+        "vector database": {
+            "name": "Vector Database",
+            "impact": 80,
+            "description":
+                "Stores embeddings and performs semantic similarity search.",
+            "use_case":
+                "RAG, recommendation systems, semantic search, "
+                "and AI memory."
+        },
+
+        "embeddings": {
+            "name": "Embeddings",
+            "impact": 78,
+            "description":
+                "Numeric representations of text, images, or other data "
+                "that capture semantic meaning.",
+            "use_case":
+                "Similarity search, clustering, recommendations, and RAG."
+        },
+
+        "agents": {
+            "name": "AI Agents",
+            "impact": 92,
+            "description":
+                "LLM-powered systems that can reason about a task "
+                "and decide which tools or actions to execute.",
+            "use_case":
+                "Coding assistants, research agents, workflow automation, "
+                "customer support, and enterprise automation."
+        }
+    }
+
+    key = topic.lower().strip()
+
+    # Handle a few common variations
+    aliases = {
+        "agent": "agents",
+        "ai agent": "agents",
+        "ai agents": "agents",
+        "model context protocol": "mcp",
+        "retrieval augmented generation": "rag",
+        "vector db": "vector database",
+        "vectors": "vector database",
+        "embedding": "embeddings"
+    }
+
+    key = aliases.get(key, key)
+
+    if key not in technologies:
+        return (
+            f"I don't have a predefined analysis for '{topic}'. "
+            "Use web search to research it instead."
+        )
 
+    item = technologies[key]
 
+    return f"""
+Technology: {item['name']}
+Estimated engineering impact: {item['impact']}%
+
+What it is:
+{item['description']}
+
+Typical use case:
+{item['use_case']}
+"""
+
+
+# ---------------------------------------------------------
+# CUSTOM TOOL 3: Developer recommendation
+# ---------------------------------------------------------
+@tool
+def recommend_ai_learning_topic(current_skill: str) -> str:
+    """
+    Suggests an AI engineering topic to learn based on a developer's skill.
+
+    Args:
+        current_skill: Developer skill such as React, Node.js,
+                       Python, backend, cloud, or fullstack.
+    """
+
+    skill = current_skill.lower()
+
+    if "react" in skill or "frontend" in skill:
+        return (
+            "Recommended next topic: AI application development.\n"
+            "Learn LLM APIs, streaming responses, tool calling, "
+            "AI SDKs, and agent UIs."
+        )
+
+    if "node" in skill or "backend" in skill:
+        return (
+            "Recommended next topic: Agent orchestration.\n"
+            "Learn tool calling, MCP, RAG, LangGraph, queues, "
+            "and asynchronous AI workflows."
+        )
+
+    if "python" in skill:
+        return (
+            "Recommended next topic: AI agent frameworks.\n"
+            "Explore smolagents, LangGraph, PydanticAI, "
+            "RAG pipelines, and evaluation."
+        )
+
+    if "cloud" in skill or "aws" in skill:
+        return (
+            "Recommended next topic: AI platform engineering.\n"
+            "Learn model gateways, vector databases, observability, "
+            "guardrails, GPU inference, and MCP servers."
+        )
+
+    if "fullstack" in skill:
+        return (
+            "Recommended path:\n"
+            "1. LLM APIs\n"
+            "2. Structured outputs\n"
+            "3. Tool calling\n"
+            "4. RAG\n"
+            "5. MCP\n"
+            "6. LangGraph\n"
+            "7. Agent evaluation and observability"
+        )
+
+    return (
+        "Start with LLM APIs, prompt engineering, structured outputs, "
+        "tool calling, RAG, and then AI agents."
+    )
+
+
+# ---------------------------------------------------------
+# FINAL ANSWER TOOL
+# ---------------------------------------------------------
 final_answer = FinalAnswerTool()
 
-# If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder:
-# model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud' 
 
-model = HfApiModel(
-max_tokens=2096,
-temperature=0.5,
-model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded
-custom_role_conversions=None,
+# ---------------------------------------------------------
+# MODEL
+# ---------------------------------------------------------
+model = InferenceClientModel(
+    max_tokens=2096,
+    temperature=0.5,
+    model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
+    custom_role_conversions=None,
 )
 
 
-# Import tool from Hub
-image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
+# ---------------------------------------------------------
+# TOOL FROM HUGGING FACE HUB
+# ---------------------------------------------------------
+image_generation_tool = load_tool(
+    "agents-course/text-to-image",
+    trust_remote_code=True
+)
 
-with open("prompts.yaml", 'r') as stream:
+
+# ---------------------------------------------------------
+# LOAD SYSTEM PROMPTS
+# ---------------------------------------------------------
+with open("prompts.yaml", "r") as stream:
     prompt_templates = yaml.safe_load(stream)
-    
+
+
+# ---------------------------------------------------------
+# CREATE AGENT
+# ---------------------------------------------------------
 agent = CodeAgent(
     model=model,
-    tools=[final_answer], ## add your tools here (don't remove final answer)
-    max_steps=6,
+
+    tools=[
+        # Search current information from the web
+        DuckDuckGoSearchTool(),
+
+        # Custom tools
+        get_current_time_in_timezone,
+        analyze_ai_technology,
+        recommend_ai_learning_topic,
+
+        # Hugging Face Hub tool
+        image_generation_tool,
+
+        # Required final-answer tool
+        final_answer,
+    ],
+
+    max_steps=8,
     verbosity_level=1,
     grammar=None,
     planning_interval=None,
-    name=None,
-    description=None,
+    name="AI Developer Assistant",
+
+    description=(
+        "An AI engineering assistant that can search the web, "
+        "analyze AI technologies, recommend learning topics, "
+        "check world times, and generate images."
+    ),
+
     prompt_templates=prompt_templates
 )
 
 
+# ---------------------------------------------------------
+# START GRADIO UI
+# ---------------------------------------------------------
 GradioUI(agent).launch()