src/components/tasks.py

"""
Task description components for the leaderboard application.
"""
import streamlit as st
from src.utils.config import tasks_info
from src.utils.task_mapping import get_display_name, get_original_name

def render_task_descriptions():
    """
    Render the benchmark details section
    """
    # Display the MLRC-BENCH image
    st.image("Assests/MLRC_Bench_overview.png", use_column_width=True)
    
    # Display the MLRC-BENCH information
    st.markdown("""
   ## MLRC-BENCH: Can Language Agents Solve ML Research Challenges?

Recent advances in large language models (LLMs) have motivated a critical question in the machine learning community: can AI agents not only propose novel research ideas but also translate them into effective implementations? **MLRC-BENCH** is introduced as a new benchmark to investigate this question by rigorously evaluating the capacity of LLM-based research agents to address contemporary ML competition tasks.

---

### Benchmark Overview

MLRC-BENCH seeks to assess AI-driven research workflows in two primary dimensions:

- **Idea Proposal**: Generating plausible and potentially innovative methods for addressing current ML research problems.  
- **Code Implementation**: Translating these ideas into executable solutions that measurably improve performance over a baseline.

This design contrasts with prior benchmarks that emphasize either (1) full end-to-end paper generation assessed by subjective human or LLM reviews, or (2) isolated code-generation tasks that focus on engineering challenges. By dividing the problem into idea proposal and implementation, MLRC-BENCH provides a clearer measure of how well agents can form and operationalize research insights.

---

### Evaluation Criteria

For each agent on a given task, MLRC-BENCH measures performance relative to a **baseline** method and a **top human** benchmark. We report two primary metrics, each taken from the maximum result across all experimental runs for a task-model pair:

- **Relative Improvement to Human**  
  How effectively the agent closes the gap between the baseline and the best human solution.

- **Absolute Improvement to Baseline**  
  How much better the agent performs compared to the baseline, expressed as a percentage gain.
---

### Significance

MLRC-BENCH emphasizes rigorous and reproducible evaluations, focusing on tasks drawn from recent machine learning conferences and workshops to ensure that tested methods are both **meaningful** and **nontrivial**. This dynamic approach allows the benchmark to grow as new competition tasks arise, enabling continuous monitoring of progress in agent-driven research. Through its emphasis on objective success criteria, MLRC-BENCH fosters the development of AI agents that more effectively balance conceptual innovation with practical impact.

---

### Future Directions

While current results suggest that LLM-based research agents still fall short of human capabilities in creativity and code implementation, MLRC-BENCH provides a **scalable mechanism** to track and accelerate progress. As AI methods advance—and potentially branch into high-stakes domains such as healthcare and climate modeling—this benchmark could serve as a critical resource for aligning agent innovation with **reliability** and **safety**.

    """)
    
    st.markdown("""
    <div class="card">
        <div class="card-title"><span class="card-title-icon">🔍</span> Tasks in the Benchmark</div>
        <p style="margin-bottom: 20px;">
            Click on any task to learn more.
        </p>
    </div>
    """, unsafe_allow_html=True)

    # Task links mapping - using original task names
    original_task_links = {
        "Backdoor Trigger Recovery": "https://www.llmagentsafetycomp24.com/tracks/#backdoor_model",
        "Machine Unlearning": "https://unlearning-challenge.github.io/",
        "Perception Temporal Action Loc": "https://ptchallenge-workshop.github.io",
        "Product Recommendation": "https://www.aicrowd.com/challenges/amazon-kdd-cup-23-multilingual-recommendation-challenge",
        "Meta Learning": "https://metalearning.chalearn.org/",
        "Llm Merging": "https://llm-merging.github.io"
    }
    
    # Update links mapping to use display names as keys
    task_links = {get_display_name(task): link for task, link in original_task_links.items()}

    # Create two columns
    col1, col2 = st.columns(2)
    
    # Split tasks between the two columns with better styling
    task_items = list(tasks_info.items())
    mid_point = len(task_items) // 2
    
    with col1:
        for task, description in task_items[:mid_point]:
            link = task_links.get(task, "#")
            st.markdown(f"""
            <a href="{link}" target="_blank" style="text-decoration: none; color: inherit;">
                <div class="task-card" style="cursor: pointer; transition: transform 0.2s, box-shadow 0.2s; padding: 12px; margin-bottom: 15px; height: auto;" onmouseover="this.style.transform='translateY(-5px)'; this.style.boxShadow='0 8px 15px rgba(0, 0, 0, 0.2)';" onmouseout="this.style.transform='translateY(0)'; this.style.boxShadow='0 4px 6px rgba(0, 0, 0, 0.15)';">
                    <div class="task-title" style="text-align: center;">{task} <span style="font-size: 14px; opacity: 0.7;">🔗</span></div>
                </div>
            </a>
            """, unsafe_allow_html=True)
    
    with col2:
        for task, description in task_items[mid_point:]:
            link = task_links.get(task, "#")
            st.markdown(f"""
            <a href="{link}" target="_blank" style="text-decoration: none; color: inherit;">
                <div class="task-card" style="cursor: pointer; transition: transform 0.2s, box-shadow 0.2s; padding: 12px; margin-bottom: 15px; height: auto;" onmouseover="this.style.transform='translateY(-5px)'; this.style.boxShadow='0 8px 15px rgba(0, 0, 0, 0.2)';" onmouseout="this.style.transform='translateY(0)'; this.style.boxShadow='0 4px 6px rgba(0, 0, 0, 0.15)';">
                    <div class="task-title" style="text-align: center;">{task} <span style="font-size: 14px; opacity: 0.7;">🔗</span></div>
                </div>
            </a>
            """, unsafe_allow_html=True)