app.py

import os
import gradio as gr
import requests
import pandas as pd
from dotenv import load_dotenv
from langchain import hub
from langchain_openai import ChatOpenAI 
from langchain.agents import create_react_agent, AgentExecutor
from langchain_core.prompts import PromptTemplate
from langchain_core.messages import AIMessage, HumanMessage
from tools.web_searcher import web_search_tool
from tools.calculator import calculator_tool
from tools.file_reader import read_file_tool
from tools.code_review import code_reviewer
from tools.web_scraper import web_scraper_tool
import time

load_dotenv()

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

# === Tools ===
tools = [
    web_search_tool,
    calculator_tool,
    read_file_tool,
    code_reviewer,
    web_scraper_tool
]

# === Model ===
model = ChatOpenAI(model="gpt-4o", api_key=os.getenv("OPENAI_API_KEY"))

# === ReAct Prompt ===
template = '''Answer the following questions as best you can. You have access to the following tools:

{tools}

Strict rules to follow:
1️⃣ Use tools when needed: web_search_tool for external information, file_reader for file data, calculator for math, code_reviewer for code questions, web_scraper for web content.
2️⃣ Combine tools logically for multi-step problems.
3️⃣ Format answers exactly as requested: single name, city, code, or number—no extra text.
4️⃣ If a link is provided, use web_search_tool or web_scraper to extract the information.
5️⃣ Do not guess; if information is unavailable, say: 'No answer found.'
6️⃣ Be precise, factual, and avoid hallucination. Verify using tools.

Use the following format for every question:

Question: the input question you must answer
Thought: reasoning about what to do
Action: the action to take, one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action

Use the following format:

Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question

Begin!

Question: {input}
Thought:{agent_scratchpad}'''

react_prompt = PromptTemplate.from_template(template)

# === ReAct Agent ===
agent = create_react_agent(model, tools, react_prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# === Gradio Interface ===
def process_question(profile: gr.OAuthProfile | None):
    if profile is None:
        return "Please log in first."

    username = profile.username
    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"
    agent_code = f"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}/tree/main"

    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
    except Exception as e:
        return f"Error fetching questions: {e}"

    answers_payload = []
    results_log = []

    for item in questions_data:
        task_id = item.get("task_id")
        question_text = item.get("question")
        if not task_id or not question_text:
            continue

        try:
            result = agent_executor.invoke({"input": question_text})
            final_answer = result["output"] if isinstance(result, dict) else result
            answers_payload.append({"task_id": task_id, "submitted_answer": final_answer})
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": final_answer})
        except Exception as e:
            answers_payload.append({"task_id": task_id, "submitted_answer": f"[ERROR: {e}]"})
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"[ERROR: {e}]"})
        time.sleep(1)  # slight delay to avoid rate limits

    submission_data = {"username": username, "agent_code": agent_code, "answers": answers_payload}

    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.')}"
        )
        return final_status
    except Exception as e:
        return f"Submission Failed: {e}"

with gr.Blocks() as demo:
    gr.Markdown("# GAIA Agent Evaluation")
    gr.Markdown("Login, run evaluation, and submit your answers below.")
    gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit All Answers")
    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)

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

if __name__ == "__main__":
    print("Launching GAIA Agent ReAct Evaluation App")
    demo.launch()