formalise

app.py

@@ -562,36 +562,21 @@ def _stars_for_score(score: float) -> str:
     return "★" * n + "☆" * (5 - n)
 
 
-def run_query(requirements: str, use_mcp: bool, debug_mode: bool):
-    debug_text = ""
-    if debug_mode:
-        logger.info(
-            f"RUN start use_mcp={use_mcp} keyword={keyword!r} min_prize={min_prize} days_ahead={days_ahead}"
-        )
+def run_query(requirements: str):
     # Defaults for agent flow
     min_prize = 0.0
     days_ahead = 90
 
     # Extract compact keywords from requirements
-    keyword, kw_logs = _extract_keywords(requirements, debug=debug_mode)
-
-    if use_mcp:
-        items, err, dbg = _mcp_list_challenges(debug=debug_mode)
-        debug_text = dbg or ""
-        if kw_logs:
-            debug_text = (debug_text + "\n\n" + kw_logs).strip()
-        if err:
-            items = FALLBACK_DATA
-            status = f"MCP fallback: {err}"
-            if debug_mode:
-                logger.info(f"RUN fallback reason: {err}")
-        else:
-            status = "MCP OK"
-            if debug_mode:
-                logger.info("RUN MCP OK")
-    else:
+    keyword, _ = _extract_keywords(requirements, debug=False)
+
+    # Always use MCP in production UI; fallback to local sample on error
+    items, err, _ = _mcp_list_challenges(debug=False)
+    if err:
         items = FALLBACK_DATA
-        status = "Local sample"
+        status = f"MCP fallback: {err}"
+    else:
+        status = "MCP OK"
 
     # Apply time window first, then keyword/prize filtering
     items = _filter_by_days(items, days_ahead)
@@ -600,11 +585,10 @@ def run_query(requirements: str, use_mcp: bool, debug_mode: bool):
     filtered = shortlist(items, "", min_prize)
     # Enforce LLM config for scoring
     ok, cfg_msg = _require_llm_config()
-    score_logs = ""
     if not ok:
         status = f"{status} | LLM config required: {cfg_msg}"
     else:
-        scored, score_logs = _score_items(filtered, keyword, debug=debug_mode)
+        scored, _ = _score_items(filtered, keyword, debug=False)
         if scored:
             scored.sort(key=lambda t: t[1], reverse=True)
             filtered = [c for c, _, _ in scored]
@@ -612,7 +596,7 @@ def run_query(requirements: str, use_mcp: bool, debug_mode: bool):
     if not filtered and items:
         filtered = items
         status = f"{status} (no matches; showing unfiltered)"
-    plan_text, plan_logs = _generate_plan(filtered, keyword, min_prize, days_ahead, debug=debug_mode) if filtered else ("", "")
+    plan_text, _ = _generate_plan(filtered, keyword, min_prize, days_ahead, debug=False) if filtered else ("", "")
 
     # Build a map from id to (score, reason) for star display
     id_to_score_reason: dict[str, tuple[float, str]] = {}
@@ -633,33 +617,22 @@ def run_query(requirements: str, use_mcp: bool, debug_mode: bool):
             (r[:160] + ("…" if len(r) > 160 else "")) if r else "",
             c.id,
         ])
-    # Append model logs into debug output when enabled and also log to console
-    if debug_mode:
-        merged_logs = (debug_text + "\n\n" + score_logs + "\n\n" + plan_logs).strip()
-        debug_text = merged_logs
-        if merged_logs:
-            logger.info(merged_logs)
-    return rows, status, debug_text, plan_text
+    return rows, status, plan_text
 
 
 with gr.Blocks(title="Topcoder Challenge Scout") as demo:
     gr.Markdown("**Topcoder Challenge Scout** — agent picks tools, you provide requirements")
-    with gr.Row():
-        requirements = gr.Textbox(label="Requirements", placeholder="e.g. Looking for recent active LLM development challenges with web UI", lines=3)
-        use_mcp = gr.Checkbox(label="Use MCP (recommended)", value=True)
-        debug_mode = gr.Checkbox(label="Debug mode", value=False)
+    requirements = gr.Textbox(label="Requirements", placeholder="e.g. Looking for recent active LLM development challenges with web UI", lines=3)
     gr.Markdown("Default filters: within last 90 days, active status. The agent extracts minimal keywords automatically.")
     run_btn = gr.Button("Find challenges")
     status = gr.Textbox(label="Status", interactive=False)
     table = gr.Dataframe(headers=["Title", "Prize", "Deadline", "Tags", "Recommend", "AI Reason", "Id"], wrap=True)
     plan_md = gr.Markdown("", label="Plan")
-    with gr.Accordion("Debug output", open=False):
-        debug_out = gr.Textbox(label="Logs", interactive=False, lines=8)
 
     run_btn.click(
         fn=run_query,
-        inputs=[requirements, use_mcp, debug_mode],
-        outputs=[table, status, debug_out, plan_md],
+        inputs=[requirements],
+        outputs=[table, status, plan_md],
     )