app.py

import os
import gradio as gr
import requests
import pandas as pd
import time
import re

from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.agents import AgentExecutor, create_react_agent
from langchain.memory import ConversationSummaryMemory
from typing import List, Optional

# === TOOL IMPORTS ===
from helper import repl_tool, file_saver_tool, audio_transcriber_tool, gemini_multimodal_tool, wikipedia_search_tool2

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

# --- Prompt ---
prompt = PromptTemplate(
    input_variables=["input", "agent_scratchpad", "chat_history", "tool_names"],
    template="""
    You are a smart and helpful AI Agent/Assistant that excels at fact-based reasoning. You are allowed and encouraged to use one or more tools as needed to answer complex questions and perform tasks.
    [ ...cut for brevity: insert your strict format rules and examples here ... ]
    {chat_history}
    New input: {input}
    ---
    {agent_scratchpad}
    """
)

# === AGENT DEFINITION ===
class BasicAgent:
    def __init__(
        self,
        agent, tools: List, verbose: bool = False, handle_parsing_errors: bool = True,
        max_iterations: int = 9, memory: Optional[ConversationSummaryMemory] = None
    ):
        self.agent = agent
        self.tools = tools
        self.verbose = verbose
        self.handle_parsing_errors = handle_parsing_errors
        self.max_iterations = max_iterations
        self.memory = memory
        self.agent_obj = AgentExecutor(
            agent=self.agent,
            tools=self.tools,
            verbose=self.verbose,
            handle_parsing_errors=self.handle_parsing_errors,
            max_iterations=self.max_iterations,
            memory=self.memory
        )

    def __call__(self, question: str) -> str:
        result = self.agent_obj.invoke(
            {"input": question},
            config={"configurable": {"session_id": "test-session"}},
        )
        return result['output']

def run_and_submit_all(profile: gr.OAuthProfile | None):
    space_id = os.getenv("SPACE_ID")

    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"

    # OpenAI API key only!
    openai_api_key = os.getenv("OPENAI_API_KEY")
    if not openai_api_key:
        print("OpenAI API key not found in environment variables.")
        return "OpenAI API key not found. Please set OPENAI_API_KEY environment variable.", None

    # Use GPT-4o (or another allowed OpenAI model)
    llm_client = ChatOpenAI(model='gpt-4o', temperature=0, api_key=openai_api_key)

    # Tools: only offline/tools not requiring other APIs
    tools = [
        repl_tool,
        file_saver_tool,
        audio_transcriber_tool,
        gemini_multimodal_tool,   # If this is purely local or adapted for OpenAI images, otherwise remove!
        wikipedia_search_tool2
    ]

    summary_memory = ConversationSummaryMemory(llm=llm_client, memory_key="chat_history")

    summary_react_agent = create_react_agent(
        llm=llm_client,
        tools=tools,
        prompt=prompt
    )

    # 1. Instantiate Agent
    try:
        agent = BasicAgent(summary_react_agent, tools, True, True, 30, summary_memory)
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None
    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 Exception as e:
        print(f"Error fetching questions: {e}")
        return f"Error 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")
        file_name = item.get("file_name")
        full_question_for_agent = question_text
        if file_name:
            attachment_url = f"{DEFAULT_API_URL}/files/{task_id}"
            full_question_for_agent += f"\n\nAttachment '{file_name}' available at EXACT URL: {attachment_url}"
            print(f"Running agent on task {task_id}: {full_question_for_agent}", flush=True)
        try:
            submitted_answer = agent(full_question_for_agent)
            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})
            time.sleep(2)  # Decrease or remove if not rate-limited!
        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)

    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)

    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.')}"
        )
        cleaned_final_status = re.sub(r'[^\x20-\x7E\n\r\t]+', '', final_status).strip()
        results_df = pd.DataFrame(results_log)
        return cleaned_final_status, results_df
    except Exception as e:
        print(f"Error submitting answers: {e}")
        results_df = pd.DataFrame(results_log)
        return f"Submission Failed: {e}", results_df

# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    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, submit answers, and see the score.
        ---
        **Note:** Only OpenAI API key is needed!
        """
    )

    gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit All Answers")
    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 + " 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("-"*(60 + len(" App Starting ")) + "\n")
    print("Launching Gradio Interface for Basic Agent Evaluation...")
    demo.launch(debug=True, share=False)