Create app.py

app.py

@@ -0,0 +1,106 @@
+import os
+import streamlit as st
+
+from macg.llm_hf import HuggingFaceInferenceLLM
+from macg.agents.coder import CoderAgent
+from macg.agents.reviewer import ReviewerAgent
+from macg.agents.tester import TesterAgent
+from macg.orchestrator import Orchestrator
+
+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 "")