Update app.py

app.py

@@ -1,94 +1,72 @@
-# ------------------------------------------------------------
-# fast async app.py  (korrekte Zuordnung + Gemini-Throttle)
-# ------------------------------------------------------------
-import os, asyncio, concurrent.futures, functools, json
-from pathlib import Path
-import gradio as gr, requests, pandas as pd
-from langchain_core.messages import HumanMessage
-from agent import agent_executor
+# app.py  –  Gradio UI + Orchestrierung
+# =====================================
+import os
+import gradio as gr
+import pandas as pd
 
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-MAX_PAR_LLM     = 3                      # 3 gleichz. Requests stay < 15/min
-SEMAPHORE       = asyncio.Semaphore(MAX_PAR_LLM)
+from agent import GaiaAgent              # deine LangChain/LangGraph-Implementierung
+import logic                             # Netzwerk- / Loader- / Submit-Utilities
 
-# ---------- synchroner Agent-Aufruf ---------------------------------
-def run_agent_sync(task_id: str, question: str) -> str:
-    payload = {
-        "messages": [HumanMessage(content=question)],
-        "task_id": task_id,
-    }
-    try:
-        res = agent_executor.invoke(payload)
-        return res["messages"][-1].content.strip()
-    except Exception as e:
-        return f"AGENT ERROR: {e}"
 
-# ---------- async Wrapper + Throttle --------------------------------
-async def run_agent_async(executor, task_id: str, question: str) -> str:
-    loop = asyncio.get_event_loop()
-    async with SEMAPHORE:                       # Gemini-Quota Guard
-        return await loop.run_in_executor(
-            executor, functools.partial(run_agent_sync, task_id, question)
-        )
+# ---------------------------------------------------------------------
+# Callback für den Gradio-Button
+# ---------------------------------------------------------------------
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    # 0) Login prüfen
+    if profile is None:
+        return "⚠️  Please log in with the Hugging Face button.", None
 
-# ---------- Main Gradio Callback ------------------------------------
-async def run_and_submit_all(profile: gr.OAuthProfile | None, progress=gr.Progress()):
-    if not profile:
-        return "Please login with your HF account.", None
     username = profile.username
+    space_id = os.getenv("SPACE_ID", "your-space-id")       # Fallback für lokales Testen
+    agent_code_url = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(f"👤  User: {username} | Repo: {agent_code_url}")
 
-    # 1) Fragen laden
+    # 1) Agent instanziieren
     try:
-        questions = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15).json()
+        gaia_agent = GaiaAgent()
     except Exception as e:
-        return f"Error fetching questions: {e}", None
-
-    progress(0, desc=f"Fetched {len(questions)} questions – processing …")
+        return f"❌ Error initialising GaiaAgent: {e}", None
 
-    answers, logs = [], []
-    work = [(q["task_id"], q["question"]) for q in questions]
-
-    # 2) Parallel-Ausführung mit korrekt gemappten Tasks
-    with concurrent.futures.ThreadPoolExecutor(max_workers=MAX_PAR_LLM) as ex:
-        # korrekte Reihenfolge: Ergebnisse von gather ↔ Reihenfolge in work
-        tasks = [
-            run_agent_async(ex, tid, qst)      # liefert str-Antwort
-            for tid, qst in work
-        ]
-        # gather wartet auf alle, behält Reihenfolge
-        results = await asyncio.gather(*tasks)
-        for idx, answer in enumerate(results):
-            tid, qst = work[idx]
-
-            answers.append({"task_id": tid, "submitted_answer": answer})
-            logs.append({"Task ID": tid, "Question": qst, "Answer": answer})
+    # 2) Fragen (und evtl. Dateien) laden
+    try:
+        questions = logic.fetch_all_questions()
+    except Exception as e:
+        return f"❌ Could not fetch questions: {e}", None
 
-            progress((idx + 1) / len(work), desc=f"{idx+1}/{len(work)} done")
+    # 3) Agent auf alle Fragen loslassen
+    results_log, answers_payload = logic.run_agent(gaia_agent, questions)
+    if not answers_payload:
+        return "⚠️  Agent produced no answers.", pd.DataFrame(results_log)
 
-    # 3) Submit
-    submit_url = f"{DEFAULT_API_URL}/submit"
-    payload = {
-        "username": username,
-        "agent_code": f"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}/tree/main",
-        "answers": answers,
+    # 4) Einsenden & Score abrufen
+    submission = {
+        "username": username.strip(),
+        "agent_code": agent_code_url,
+        "answers": answers_payload,
     }
-    try:
-        res = requests.post(submit_url, json=payload, timeout=60).json()
-        status = (f"Submission OK – Score {res.get('score','?')} % "
-                  f"({res.get('correct_count','?')}/{res.get('total_attempted','?')})")
-    except Exception as e:
-        status = f"Submission failed: {e}"
+    status_msg, results_df = logic.submit_answers(submission, results_log)
+    return status_msg, results_df
 
-    return status, pd.DataFrame(logs)
 
-# ---------- Gradio UI -----------------------------------------------
+# ---------------------------------------------------------------------
+# Gradio-Interface
+# ---------------------------------------------------------------------
 with gr.Blocks() as demo:
-    gr.Markdown("# Fast GAIA Agent Runner  (async × progress)")
+    gr.Markdown("# GAIA Level-1 Agent – Evaluation Runner")
+    gr.Markdown(
+        "1. **Clone** this Space and implement your logic in `agent.py`.\n"
+        "2. **Log in** with your HF account.\n"
+        "3. Click **Run** to fetch questions, run the agent, and submit answers."
+    )
+
     gr.LoginButton()
-    run_btn = gr.Button("Run & Submit")
-    out_status = gr.Textbox(label="Status / Score", lines=3)
-    out_table  = gr.DataFrame(label="Answers", wrap=True)
-    run_btn.click(run_and_submit_all, outputs=[out_status, out_table])
+    run_btn = gr.Button("Run Evaluation & Submit All Answers")
+
+    status_box = gr.Textbox(label="Status / Score", lines=4, interactive=False)
+    results_df = gr.DataFrame(label="Questions & Answers", wrap=True)
+
+    run_btn.click(run_and_submit_all, outputs=[status_box, results_df])
 
+# Standard-Start – auch lokal lauffähig
 if __name__ == "__main__":
-    demo.launch(debug=True, share=False)
+    demo.launch()