app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from agent import build_graph
from langchain_core.messages import HumanMessage
import time

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

# --- Improved Agent Definition ---
class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
        try:
            self.graph = build_graph()
            print("Graph built successfully.")
        except Exception as e:
            print(f"Error building graph: {e}")
            raise e
        
    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 100 chars): {question[:100]}...")
        
        try:
            # Clean the question
            question = question.strip()
            
            # Wrap the question in a HumanMessage
            messages = [HumanMessage(content=question)]
            
            # Invoke the graph with retry mechanism
            max_retries = 3
            for attempt in range(max_retries):
                try:
                    result = self.graph.invoke({"messages": messages})
                    
                    if 'messages' in result and result['messages']:
                        answer = result['messages'][-1].content
                        
                        # Clean up the answer
                        if isinstance(answer, str):
                            # Remove the "FINAL ANSWER: " prefix if it exists
                            if "FINAL ANSWER:" in answer:
                                answer = answer.split("FINAL ANSWER:")[-1].strip()
                            
                            # Additional cleanup
                            answer = answer.replace("Assistant: ", "").strip()
                            
                            print(f"Agent answer (first 100 chars): {answer[:100]}...")
                            return answer
                        else:
                            return str(answer)
                    else:
                        return "No response generated"
                        
                except Exception as e:
                    print(f"Attempt {attempt + 1} failed: {e}")
                    if attempt == max_retries - 1:
                        return f"Error processing question: {str(e)}"
                    time.sleep(1)  # Brief pause before retry
            
        except Exception as e:
            print(f"Error in agent call: {e}")
            return f"Agent error: {str(e)}"


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:
        print("Initializing agent...")
        agent = BasicAgent()
        print("Agent initialized successfully.")
    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
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(f"Agent code URL: {agent_code}")

    # 2. Fetch Questions
    print(f"Fetching questions from: {questions_url}")
    try:
        response = requests.get(questions_url, timeout=30)
        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 with better error handling
    results_log = []
    answers_payload = []
    print(f"Running agent on {len(questions_data)} questions...")
    
    for i, item in enumerate(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
            
        print(f"Processing question {i+1}/{len(questions_data)}: {task_id}")
        
        try:
            # Add timeout and better error handling for individual questions
            start_time = time.time()
            submitted_answer = agent(question_text)
            end_time = time.time()
            
            print(f"Question {i+1} completed in {end_time - start_time:.2f} seconds")
            
            # Validate the answer
            if not submitted_answer or submitted_answer.strip() == "":
                submitted_answer = "No answer generated"
            
            answers_payload.append({
                "task_id": task_id, 
                "submitted_answer": str(submitted_answer).strip()
            })
            
            results_log.append({
                "Task ID": task_id, 
                "Question": question_text[:200] + "..." if len(question_text) > 200 else question_text, 
                "Submitted Answer": str(submitted_answer).strip()
            })
            
        except Exception as e:
             print(f"Error running agent on task {task_id}: {e}")
             error_answer = f"AGENT ERROR: {str(e)}"
             answers_payload.append({
                 "task_id": task_id, 
                 "submitted_answer": error_answer
             })
             results_log.append({
                 "Task ID": task_id, 
                 "Question": question_text[:200] + "..." if len(question_text) > 200 else question_text, 
                 "Submitted Answer": error_answer
             })

    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 with better error handling
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    try:
        response = requests.post(submit_url, json=submission_data, timeout=120)
        response.raise_for_status()
        result_data = response.json()
        
        final_status = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username', 'Unknown')}\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.")
        print(f"Score: {result_data.get('score', 'N/A')}%")
        
        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("# Enhanced Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. Please clone this space, then modify the code to define your agent's logic, tools, and necessary packages.
        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.
        
        **Improvements in this version:**
        - Enhanced mathematical tools (factorial, gcd, lcm, compound interest, etc.)
        - Better search tools with error handling
        - Improved HuggingFace model integration
        - Better answer processing and cleanup
        - Enhanced error handling and retry mechanisms
        
        ---
        **Note:** The evaluation process may take some time as the agent processes all questions systematically.
        """
    )

    gr.LoginButton()

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

    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    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 + " Enhanced App Starting " + "-"*30)
    
    # Check for environment variables
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")
    hf_token = os.getenv("HUGGINGFACE_INFERENCE_TOKEN")

    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(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?).")
        
    if hf_token:
        print("✅ HUGGINGFACE_INFERENCE_TOKEN found")
    else:
        print("⚠️  HUGGINGFACE_INFERENCE_TOKEN not found - this may cause issues")

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

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