app.py

import gradio as gr
import requests
import json
import torch
import pandas as pd
import os
import inspect

try:
    from smolagents import CodeAgent, DuckDuckGoSearchTool, TransformersModel
    SMOLAGENTS_AVAILABLE = True
except ImportError:
    print("Warning: smolagents not available, using fallback implementation")
    SMOLAGENTS_AVAILABLE = False

# (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 ------
class BasicAgent:
    """
    Simple agent using smolagents CodeAgent with DuckDuckGoSearchTool.
    
    Based on Unit 2 course content:
    - Uses CodeAgent from smolagents (writes Python code to solve problems)
    - Has access to DuckDuckGoSearchTool for web search
    - Follows ReAct pattern automatically within CodeAgent
    
    How ReAct works in smolagents:
    1. CodeAgent receives a question
    2. INTERNAL LOOP (hidden from us):
       - Think: LLM reasons about what to do next
       - Act: LLM writes Python code (may call tools like DuckDuckGoSearchTool)
       - Observe: LLM sees the code execution results
       - Repeat: If not satisfied, continues the loop (up to max_steps)
    3. Returns final answer when LLM decides it's complete
    
    The while loop is built into CodeAgent - we just call agent.run(question)!
    """
    
    def __init__(self):
        print("SimpleCodeAgent initialized.")
        
        if SMOLAGENTS_AVAILABLE:
            try:
                # Initialize the model (from Unit 2)
                self.model = TransformersModel(model_id="google/gemma-3-4b-it", torch_dtype=torch.float16)
                
                # Create CodeAgent with DuckDuckGoSearchTool (core Unit 2 concept)
                self.agent = CodeAgent(
                    tools=[DuckDuckGoSearchTool()],
                    model=self.model,
                    additional_authorized_imports=[
                        'math', 'statistics', 're',  # Basic computation
                        'requests', 'json',           # Web requests and JSON
                        'pandas', 'numpy',            # Data analysis  
                        'zipfile', 'os',              # File processing
                        'datetime', 'time'            # Date/time operations
                    ]
                )
                
                self.available = True
                print("✅ Smolagents CodeAgent initialized successfully")
                
            except Exception as e:
                print(f"⚠️ Error initializing smolagents: {e}")
                self.available = False
        else:
            self.available = False
    
    def __call__(self, question: str) -> str:
        """
        Main agent execution - just pass the question to CodeAgent.
        The CodeAgent will Think-Act-Observe automatically and format the answer properly.
        """
        print(f"Agent processing: {question[:80]}...")
        
        if not self.available:
            return self._fallback_response(question)
        
        try:
            # Let the CodeAgent handle everything with ReAct pattern
            # We'll include formatting instructions in our prompt
            formatted_question = f"""Please answer this question with just the direct answer needed (no prefixes like "The answer is"):

{question}

Important: 
- For numbers, provide just the number
- For names/words, provide just the name/word  
- For yes/no questions, answer just "Yes" or "No"
- Use web search when you need current information
- Write Python code for calculations or data processing"""

            result = self.agent.run(formatted_question)
            
            final_answer = str(result).strip()
            print(f"Agent answer: {final_answer}")
            return final_answer
            
        except Exception as e:
            print(f"Agent error: {e}")
            return "Unable to process question"
    
    def _fallback_response(self, question: str) -> str:
        """
        Fallback if smolagents is not available.
        """
        print("Smolagents not available - using basic fallback")
        return "Smolagents required but not available"


def run_and_submit_all( profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    # --- Determine HF Space Runtime URL and Repo URL ---
    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code

    if profile:
        username= f"{profile.username}"
        print(f"User logged in: {username}")
    else:
        print("User not logged in.")
        return "Please Login to Hugging Face with the button.", None

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    # 1. Instantiate Agent ( modify this part to create your agent)
    try:
        agent = BasicAgent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None
    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(agent_code)

    # 2. Fetch Questions
    print(f"Fetching questions from: {questions_url}")
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
             print("Fetched questions list is empty.")
             return "Fetched questions list is empty or invalid format.", None
        print(f"Fetched {len(questions_data)} questions.")
    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None
    except requests.exceptions.JSONDecodeError as e:
         print(f"Error decoding JSON response from questions endpoint: {e}")
         print(f"Response text: {response.text[:500]}")
         return f"Error decoding server response for questions: {e}", None
    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return f"An unexpected error occurred fetching questions: {e}", None

    # 3. Run your Agent
    results_log = []
    answers_payload = []
    print(f"Running agent on {len(questions_data)} questions...")
    for item in questions_data:
        task_id = item.get("task_id")
        question_text = item.get("question")
        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue
        try:
            submitted_answer = agent(question_text)
            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
        except Exception as e:
             print(f"Error running agent on task {task_id}: {e}")
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})

    if not answers_payload:
        print("Agent did not produce any answers to submit.")
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    # 4. Prepare Submission 
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
    print(status_update)

    # 5. Submit
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        response.raise_for_status()
        result_data = response.json()
        final_status = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"Overall Score: {result_data.get('score', 'N/A')}% "
            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
            f"Message: {result_data.get('message', 'No message received.')}"
        )
        print("Submission successful.")
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except requests.exceptions.HTTPError as e:
        error_detail = f"Server responded with status {e.response.status_code}."
        try:
            error_json = e.response.json()
            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
        except requests.exceptions.JSONDecodeError:
            error_detail += f" Response: {e.response.text[:500]}"
        status_message = f"Submission Failed: {error_detail}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except requests.exceptions.Timeout:
        status_message = "Submission Failed: The request timed out."
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except requests.exceptions.RequestException as e:
        status_message = f"Submission Failed: Network error - {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except Exception as e:
        status_message = f"An unexpected error occurred during submission: {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df


# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    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).
        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
        """
    )

    gr.LoginButton()

    run_button = gr.Button("Run Evaluation & Submit All Answers")

    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    # Removed max_rows=10 from DataFrame constructor
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    # Check for SPACE_HOST and SPACE_ID at startup for information
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup

    if space_host_startup:
        print(f"✅ SPACE_HOST found: {space_host_startup}")
        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")

    if space_id_startup: # Print repo URLs if SPACE_ID is found
        print(f"✅ SPACE_ID found: {space_id_startup}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
    else:
        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")

    print("-"*(60 + len(" App Starting ")) + "\n")

    print("Launching Gradio Interface for Basic Agent Evaluation...")
    demo.launch(debug=True, share=False)