app.py

import os
import streamlit as st

from macg.llm_openai import OpenAIResponsesLLM

openai_key = os.getenv("OPENAI_API_KEY")
if not openai_key:
    st.error("OPENAI_API_KEY not found. Add it in HF Space Settings → Variables and secrets.")
    st.stop()

llm = OpenAIResponsesLLM(
    model=st.sidebar.text_input("OpenAI model", value="gpt-5"),
    api_key=openai_key,
    temperature=st.sidebar.slider("Temperature", 0.0, 1.0, 0.2, 0.05),
    max_output_tokens=900,
)

st.set_page_config(page_title="Multi-Agent Codegen (HF)", layout="wide")

st.title("🤖 Multi-Agent Codegen + Review + Testing (Hugging Face)")
st.caption("Coder → Reviewer → Tester loop with pytest verification.")

# --- Secrets / token handling (Streamlit Cloud uses st.secrets) ---
hf_token = None
if "HF_TOKEN" in st.secrets:
    hf_token = st.secrets["HF_TOKEN"]
else:
    hf_token = os.getenv("HF_TOKEN")

with st.sidebar:
    st.header("Settings")
    model = st.text_input(
        "HF model (Inference API)",
        value="Qwen/Qwen2.5-Coder-7B-Instruct",
        help="You can change to another hosted model if you want."
    )
    max_iters = st.slider("Max iterations", 1, 6, 3)
    temperature = st.slider("Temperature", 0.0, 1.0, 0.2, 0.05)

    st.divider()
    st.subheader("HF Token")
    if hf_token:
        st.success("HF_TOKEN found (env or secrets).")
    else:
        st.warning("HF_TOKEN not found. Add it in Streamlit Secrets or environment.")
        st.info("Streamlit Cloud: Settings → Secrets → add HF_TOKEN='...'")

default_task = (
    "Implement a function fizzbuzz(n: int) -> list[str] that returns strings for 1..n.\n"
    "- Multiples of 3 -> 'Fizz'\n"
    "- Multiples of 5 -> 'Buzz'\n"
    "- Multiples of both -> 'FizzBuzz'\n"
    "Return the list of length n.\n"
    "Edge cases: n <= 0 should return an empty list."
)

task = st.text_area("Task", value=default_task, height=180)

colA, colB = st.columns([1, 1])
run_btn = colA.button("Run Agents", type="primary", use_container_width=True)
clear_btn = colB.button("Clear Output", use_container_width=True)

if clear_btn:
    st.session_state.pop("result", None)

def build_orchestrator() -> Orchestrator:
    if not hf_token:
        raise RuntimeError("HF_TOKEN missing. Add it to environment or Streamlit secrets.")

    llm = HuggingFaceInferenceLLM(
        model=model,
        token=hf_token,
        temperature=float(temperature),
        max_new_tokens=900,
    )
    coder = CoderAgent(llm)
    reviewer = ReviewerAgent(llm)
    tester = TesterAgent(llm)
    return Orchestrator(coder=coder, reviewer=reviewer, tester=tester)

if run_btn:
    try:
        orch = build_orchestrator()
        with st.spinner("Running Coder → Reviewer → Tester..."):
            result = orch.run(task=task, max_iters=int(max_iters))
        st.session_state["result"] = result
    except Exception as e:
        st.error(str(e))

result = st.session_state.get("result")

if result:
    top1, top2, top3 = st.columns([1, 1, 1])
    top1.metric("Passed", "✅ Yes" if result.passed else "❌ No")
    top2.metric("Iterations", str(result.iteration))
    top3.metric("Module", result.module_name)

    st.divider()

    left, right = st.columns([1, 1])

    with left:
        st.subheader("Generated Code")
        st.code(result.code or "", language="python")

        st.subheader("Review Notes")
        st.text(result.review_notes or "")

    with right:
        st.subheader("Generated Tests")
        st.code(result.tests or "", language="python")

        st.subheader("Test Report")
        st.text(result.test_report or "")