app.py

import os
import re
import gradio as gr
import requests
import inspect
import pandas as pd
# from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
# from llama_index.core.agent.workflow import AgentWorkflow
# from llama_index.core.tools import FunctionTool
from agent_llama import all_tools
from agent_graph import build_graph
from langchain_core.messages import HumanMessage
from langchain_google_genai import ChatGoogleGenerativeAI

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

# --- Basic Agent Definition ---

# A custom agent class that wraps an LLM and agent workflow from llama index
# class BasicAgent:
#     def __init__(self):
#         print("BasicAgent initialized.")
#         self.llm = HuggingFaceInferenceAPI(model_name="HuggingFaceH4/zephyr-7b-beta")

#         self.agent = AgentWorkflow.from_tools_or_functions(
#             all_tools,  # make sure all_tools are sync functions
#             llm=self.llm,
#             system_prompt="You are a general AI assistant. Think step-by-step, and return only the final answer on the last line."
#         )

#     def __call__(self, question: str) -> str:
#         try:
#             response = self.agent.run(question)  # sync version of arun()
#             return str(response)
#         except Exception as e:
#             return f"Agent error: {e}"

# Using LangGraph
class BasicAgent:
    """A langgraph agent."""
    def __init__(self):
        print("BasicAgent initialized.")
        self.graph = build_graph()

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        user_message = [HumanMessage(content=question)]
        result = self.graph.invoke({"messages": user_message})
        answer = result['messages'][-1].content
         # Use regex to extract only the final answer
        match = re.search(r"FINAL ANSWER:\s*(.*)", answer)
        return match.group(1).strip() if match else answer

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """ 
    Runs agent across GAIA questions, submits the answers and returns the results
    """
    # Retrive HF space ID from enviornment variables
    space_id = os.getenv("SPACE_ID")

    # Check if user is logged in
    if profile: # Populated after log in gr.LoginButton
        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

    # Initialize GAIA question and submission urls
    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    # Initialize agent 
    try:
        agent = BasicAgent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None

    # Initialize agent repository to be used for agent code
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(agent_code)

    # Fetching questions
    print(f"Fetching questions from: {questions_url}")
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        # JSON containing GAIA questions
        questions_data = response.json()

        # Guard clause - Check for empty list or errors
        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 Exception as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None

    #  Initialize empty logs for results and answers to be submitted
    
    answers_payload = [] # Task ID + Submitted Answer - Used for evaluation
    results_log = [] # Task ID + Question + Submitted Answer - Used for display

    # Run agent on questions
    print(f"Running agent on {len(questions_data)} questions...")

    # For loop to pull individual questions as agent input
    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:
            # Submit question to agent
            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)

    # Initialize submission data
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    
    # POST submission data
    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 Exception as e:
        status_message = f"Submission Failed: {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df

# Initialize Gradio app
with gr.Blocks() as demo:

    # Markdown text blocks
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. Log in to your Hugging Face account using the button below.
        2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, and see the score.
        """
    )
    # Adds a login button for authentication
    gr.LoginButton()

    # A button that triggers evaluation logic when clicked
    run_button = gr.Button("Run Evaluation & Submit All Answers")
    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False) # Non interactive textbox to show result status
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True) #Gives 

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

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")

    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?). Repo URL cannot be determined.")

    print("🔧 Running startup checks...\n")

    # Check WikipediaLoader
    try:
        from langchain_community.document_loaders import WikipediaLoader
        print("✅ WikipediaLoader imported successfully.")
        
        # Try fetching a test page
        test_docs = WikipediaLoader(query="Alan Turing", load_max_docs=1).load()
        if test_docs and test_docs[0].page_content.strip():
            print("✅ WikipediaLoader can fetch content.\n")
        else:
            print("⚠️ WikipediaLoader returned no content.\n")
    except Exception as e:
        print("❌ WikipediaLoader failed:", e, "\n")

    # Check Google Gemini LLM
    try:
        from langchain_google_genai import ChatGoogleGenerativeAI
        import os

        if os.getenv("GOOGLE_API_KEY"):
            llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
            print("✅ Google Gemini model instantiated successfully.\n")
        else:
            print("⚠️ GOOGLE_API_KEY not found in environment.\n")
    except Exception as e:
        print("❌ langchain-google-genai or Gemini setup failed:", e, "\n")


    print("-"*(60 + len(" App Starting ")) + "\n")
    print("Launching Gradio Interface for Basic Agent Evaluation...")
    demo.launch(debug=True, share=False)