| import streamlit as st |
| from src.main import run_sandbox |
|
|
|
|
| st.set_page_config( |
| page_title="Agentic Coding Sandbox", |
| page_icon=":zap:", |
| layout="wide", |
| ) |
|
|
| st.title("Self-Correcting Agentic Coding Sandbox") |
| st.markdown( |
| "Submit a coding task. The system generates, executes, and autonomously debugs " |
| "Python code in an isolated sandbox environment." |
| ) |
|
|
| with st.sidebar: |
| st.header("About") |
| st.markdown( |
| """ |
| **Architecture:** |
| 1. **Coder Agent** - generates Python code via LLM |
| 2. **Sandbox Executor** - runs code in isolated environment |
| 3. **Critic Agent** - analyzes errors and suggests fixes |
| 4. **Retry Loop** - up to 3 self-healing attempts |
| """ |
| ) |
| st.caption("Running on Hugging Face Spaces") |
|
|
| col1, col2 = st.columns([3, 2]) |
|
|
| with col1: |
| prompt = st.text_area( |
| "Describe what you want the code to do:", |
| height=150, |
| key="prompt_input", |
| placeholder="e.g., Plot a bar chart of the top 5 most frequent words in this text.", |
| ) |
|
|
| if st.button("Run", type="primary", disabled=not prompt): |
| with st.spinner("Generating code..."): |
| try: |
| result = run_sandbox(prompt) |
| st.session_state["result"] = result |
| except Exception as e: |
| st.error(f"Error: {e}") |
|
|
| with col2: |
| st.subheader("Result") |
|
|
| if "result" in st.session_state: |
| result = st.session_state["result"] |
| retries = result.get("retries_used", 0) |
|
|
| if result.get("success"): |
| st.success(f"Succeeded ({retries} retries)") |
| else: |
| st.error(f"Failed ({retries} retries)") |
|
|
| files = result.get("files", {}) |
| if files: |
| for name, b64 in files.items(): |
| st.image(f"data:image/png;base64,{b64}", caption=name, use_container_width=True) |
|
|
| output = result.get("output") or result.get("error") or "No output" |
| if output.strip(): |
| st.code(output, language="text", line_numbers=True) |
| else: |
| st.info("Submit a prompt to see results here.") |
|
|
| st.divider() |
|
|
| st.subheader("Execution Trace") |
|
|
| if "result" in st.session_state: |
| trace = st.session_state["result"].get("trace", []) |
| if trace: |
| for i, entry in enumerate(trace): |
| node = entry.get("node", "?") |
| retry = entry.get("retry", 0) |
|
|
| with st.expander(f"Attempt {retry + 1} - {node}", expanded=True): |
| st.json(entry) |
| else: |
| st.caption("No trace data available.") |
| else: |
| st.caption("Run a prompt to see the execution trace.") |
|
|
| st.divider() |
|
|
| st.subheader("Try a Sample Prompt") |
|
|
| PRESET_PROMPTS = [ |
| "Use asyncio and aiohttp to fetch JSON from https://jsonplaceholder.typicode.com/todos/1 and print the title field.", |
| "Generate a NumPy array of shape (5, 5) filled with random integers between 0 and 100, then compute the row-wise means.", |
| "Plot a sine wave and a cosine wave on the same chart using matplotlib, add a legend, and save it to /tmp/waves.png.", |
| ] |
|
|
| def _set_prompt(value: str): |
| st.session_state.prompt_input = value |
|
|
| for i, p in enumerate(PRESET_PROMPTS): |
| is_selected = st.session_state.get("prompt_input") == p |
| border = "1px solid #6366f1" if is_selected else "1px solid #e2e8f0" |
| st.markdown( |
| f"<div style='border:{border};border-radius:8px;padding:12px 16px;margin-bottom:8px;display:flex;align-items:center;justify-content:space-between;'>" |
| f"<span style='font-size:14px;flex:1;margin-right:12px;'>{p}</span>" |
| f"</div>", |
| unsafe_allow_html=True, |
| ) |
| use_col1, use_col2, _ = st.columns([1, 1, 4]) |
| with use_col1: |
| st.button( |
| "Use this prompt", |
| key=f"use_prompt_{i}", |
| use_container_width=True, |
| on_click=_set_prompt, |
| args=(p,), |
| ) |
|
|
