Update app.py

app.py

@@ -1,23 +1,121 @@
 import os
+import sys
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
 
+# Allow importing smolagents from hf-smolagents venv when running from project root
+_root = os.path.dirname(os.path.abspath(__file__))
+_venv_lib = os.path.join(_root, "hf-smolagents", ".venv", "lib")
+if os.path.isdir(_venv_lib):
+    for _name in os.listdir(_venv_lib):
+        if _name.startswith("python"):
+            _sp = os.path.join(_venv_lib, _name, "site-packages")
+            if os.path.isdir(_sp):
+                sys.path.insert(0, _sp)
+                break
+    else:
+        _sp = None
+else:
+    _sp = None
+
+from smolagents import CodeAgent, InferenceClientModel
+from smolagents.default_tools import (
+    DuckDuckGoSearchTool,
+    FinalAnswerTool,
+    PythonInterpreterTool,
+    UserInputTool,
+)
+
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+# --- Multi-Agent System (smolagents) ---
+def _create_model():
+    token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGING_FACE_HUB_TOKEN")
+    return InferenceClientModel(
+        model_id="Qwen/Qwen2.5-Coder-7B-Instruct",
+        token=token,
+    )
+
+
+def _create_code_agent(model):
+    """Code agent: Python interpreter + final answer. For math, calculations, data."""
+    return CodeAgent(
+        tools=[PythonInterpreterTool(), UserInputTool()],
+        model=model,
+        name="code_agent",
+        description="Use for math, calculations, data processing, or when the task can be solved by writing and running Python code. Call with a single clear task.",
+        max_steps=15,
+    )
+
+
+def _create_web_agent(model):
+    """Web agent: DuckDuckGo search + final answer. For factual/search tasks."""
+    return CodeAgent(
+        tools=[DuckDuckGoSearchTool(), UserInputTool()],
+        model=model,
+        name="web_agent",
+        description="Use for factual questions, current events, or when you need to search the web. Give one search task per call; you can call me multiple times with different queries.",
+        max_steps=15,
+    )
+
+
+def _create_evaluator_agent(model):
+    """Evaluator: picks best answer from multiple candidates, returns via final_answer."""
+    return CodeAgent(
+        tools=[FinalAnswerTool(), UserInputTool()],
+        model=model,
+        name="evaluator_agent",
+        description="Use to pick the single best answer from multiple candidate answers. Pass a task containing the original question and the list of candidate answers; return only the chosen best answer (concise, factual).",
+        max_steps=5,
+    )
+
+
+def _create_manager_agent(model):
+    """Manager: decides code_agent vs web_agent; if web, runs multiple web queries then evaluator then final_answer."""
+    code_agent = _create_code_agent(model)
+    web_agent = _create_web_agent(model)
+    evaluator_agent = _create_evaluator_agent(model)
+    return CodeAgent(
+        tools=[UserInputTool()],
+        model=model,
+        managed_agents=[code_agent, web_agent, evaluator_agent],
+        name="manager",
+        description="Orchestrator: decide whether to use code_agent or web_agent. For web tasks, call web_agent multiple times with different search queries, then call evaluator_agent with the question and all answers to pick the best one, then call final_answer with that result.",
+        max_steps=25,
+        planning_interval=3,
+    )
+
+
 class BasicAgent:
+    """Wrapper: builds manager-led multi-agent system and returns final answer string per question."""
+
     def __init__(self):
-        print("BasicAgent initialized.")
+        print("Multi-agent system: initializing model and manager...")
+        self._model = _create_model()
+        self._manager = _create_manager_agent(self._model)
+        print("Multi-agent system initialized (manager + code_agent, web_agent, evaluator_agent).")
+
     def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        print(f"Agent received question (first 80 chars): {question[:80]}...")
+        try:
+            result = self._manager.run(
+                question,
+                reset=True,
+                stream=False,
+                return_full_result=False,
+            )
+            if result is None:
+                out = "No answer produced."
+            else:
+                out = str(result).strip()
+            print(f"Agent returning answer (first 80 chars): {out[:80]}...")
+            return out
+        except Exception as e:
+            print(f"Agent error: {e}")
+            return f"AGENT ERROR: {e}"
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -146,11 +244,9 @@ with gr.Blocks() as demo:
     gr.Markdown(
         """
         **Instructions:**
-
         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
         ---
         **Disclaimers:**
         Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
@@ -193,4 +289,5 @@ if __name__ == "__main__":
     print("-"*(60 + len(" App Starting ")) + "\n")
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=False)
+