app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

from crewai_agent import LangChainAgent

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

# --- CrewAI Agent Definition ---
# ----- THIS IS WHERE YOU CAN BUILD WHAT YOU WANT ------
class CrewAIBasicAgent:
    """Wrapper class for CrewAI agent to match the expected interface."""
    
    def __init__(self, provider: str = "groq"):
        print(f"CrewAI Agent initialized with provider: {provider}")
        try:
            self.agent = LangChainAgent(provider=provider)
            print("CrewAI Agent successfully initialized.")
        except Exception as e:
            print(f"Error initializing CrewAI Agent: {e}")
            # Fallback to simple agent
            self.agent = None
    
    def __call__(self, question: str) -> str:
        """Process question using CrewAI agent."""
        print(f"CrewAI Agent received question (first 50 chars): {question[:50]}...")
        
        if self.agent is None:
            # Fallback response if agent failed to initialize
            fallback_answer = "CrewAI agent initialization failed. Using fallback response."
            print(f"Using fallback answer: {fallback_answer}")
            return fallback_answer
        
        try:
            # Use the CrewAI agent to process the question
            answer = self.agent(question)
            print(f"CrewAI Agent generated answer (first 100 chars): {answer[:100]}...")
            return answer
        except Exception as e:
            error_msg = f"Error in CrewAI agent processing: {str(e)}"
            print(error_msg)
            return error_msg

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 = CrewAIBasicAgent(provider="groq")  # Using Groq Llama instead of Google Gemini
    except Exception as e:
        print(f"Error instantiating CrewAI agent: {e}")
        return f"Error initializing CrewAI 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("# CrewAI Multi-Purpose Agent Evaluation Runner (Powered by Groq Llama)")
    gr.Markdown(
        """
        **CrewAI Agent Features:**
        - **Powered by Groq Llama 3.3 70B**: Ultra-fast inference with state-of-the-art language model
        - **Multi-Agent Architecture**: Specialized agents for different types of tasks
        - **Research Capabilities**: Search Wikipedia, ArXiv papers, and web sources
        - **Mathematical Operations**: Perform complex calculations and solve mathematical problems
        - **Intelligent Task Routing**: Automatically selects the best tools for each question
        - **Advanced Memory Management**: Smart conversation memory with summarization for better context
        
        **Instructions:**
        1.  Please clone this space, then modify the code to define your CrewAI agent's logic, tools, and packages
        2.  Add your API keys to the `.env` file (Groq, Tavily, etc.)
        3.  Log in to your Hugging Face account using the button below
        4.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your CrewAI agent, and see the score
        ---
        **Note:** The CrewAI agent uses Groq's ultra-fast Llama model with multiple specialized tools working together 
        to provide comprehensive and accurate answers. This combination provides both speed and intelligence.
        """
    )

    gr.LoginButton()

    run_button = gr.Button("Run CrewAI 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 CrewAI Agent Answers", wrap=True)

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

if __name__ == "__main__":
    print("\n" + "-"*30 + " CrewAI 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(" CrewAI App Starting ")) + "\n")

    print("Launching Gradio Interface for CrewAI Multi-Purpose Agent...")
    demo.launch(debug=True, share=False)