app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from huggingface_hub import InferenceClient
import time
from langchain_core.prompts import PromptTemplate
from ddgs import DDGS
import re

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

# --- Basic Agent Definition ---
class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized with Qwen/Qwen2.5-72B-Instruct")
        self.client = InferenceClient(
            model="Qwen/Qwen2.5-72B-Instruct",
            token=os.getenv("HF_TOKEN")
        )

    def search_web(self, query: str) -> str:
        """Simple DuckDuckGo search helper"""
        try:
            with DDGS() as ddgs:
                results = list(ddgs.text(query, max_results=5))
                if not results:
                    return "No results found."
                formatted = []
                for r in results:
                    title = r.get("title", "N/A")
                    body = r.get("body", "N/A")
                    href = r.get("href", "N/A")
                    formatted.append(f"{title}\n{body}\n{href}")
                return "\n\n".join(formatted)
        except Exception as e:
            return f"Search error: {str(e)[:400]}"

    def __call__(self, question: str) -> str:
        prompt_template = PromptTemplate.from_template("""
You are solving a GAIA Level 1 question. Your goal is to give **only** the exact final answer — no extra words.

Rules:
- Think step by step using format: Thought: ... → Action: web_search[query] → Observation: ... → Thought: ...
- Use the tool **only** when you need external information or verification.
- Tool format must be exactly: Action: web_search[your precise query]
- When confident → output exactly: Final Answer: <short exact answer>
- No punctuation at the end unless part of the answer.
- Keep observations short when reasoning.

Available tool: web_search

Question: {question}

Thought:
""")
        full_prompt_start = prompt_template.format(question=question)
        max_steps = 6
        history = ""
        for step in range(max_steps):
            current_prompt = full_prompt_start + history
            new_text = None
            for attempt in range(3):  # Retry logic
                try:
                    messages = [{"role": "user", "content": current_prompt}]
                    response = self.client.chat_completion(
                        messages=messages,
                        max_tokens=320,
                        temperature=0.12,
                        stop=["Final Answer:", "Observation:"]
                    )
                    new_text = response.choices[0].message.content.strip()
                    break
                except Exception as e:
                    print(f"Attempt {attempt+1} failed: {str(e)}")
                    if attempt < 2:
                        time.sleep(5 * (attempt + 1))  # Backoff
                    else:
                        return "Generation error after retries"
            if new_text is None:
                return "Generation error"
            history += new_text + "\n"
            # Look for tool call
            action_match = re.search(
                r"Action:\s*web_search\s*\[([^\]]+)\]",
                history,
                re.IGNORECASE | re.DOTALL
            )
            if action_match:
                query = action_match.group(1).strip()
                print(f"Tool call → {query}")
                observation = self.search_web(query)
                obs_short = observation[:1400] + "..." if len(observation) > 1400 else observation
                history += f"\nObservation: {obs_short}\nThought: "
            # Look for final answer
            if "Final Answer:" in history:
                parts = history.split("Final Answer:")
                answer_part = parts[-1].strip()
                answer = answer_part.split("\n")[0].strip()
                answer = answer.replace(".", "").replace(",", "").strip()
                print(f"→ Final answer: {answer}")
                return answer
        print("Max steps reached - no final answer")
        return "Unable to answer"

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    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"
    try:
        agent = BasicAgent()
    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)
    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
    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)
    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.')}"
        )
        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("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
        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.
        ---
        **Disclaimers:**
        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
        """
    )
    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)