Update app.py

app.py

@@ -3,20 +3,19 @@ import gradio as gr
 import requests
 import pandas as pd
 from smolagents import CodeAgent, InferenceClientModel
-from huggingface_hub import HfFolder
 
 # --- Constants ---
-# API-URL deines Spaces (mit /api)
-DEFAULT_API_URL = "https://pmeyhoefer-final-assignment-template.hf.space/api"
-# Modell-ID und HF-Token
+# API-URL deines Spaces (ohne "/api"-Suffix)
+DEFAULT_API_URL = "https://pmeyhoefer-final-assignment-template.hf.space"
+# Modell-ID und HF-Token (bitte hier deinen HF Access Token einfügen)
 MODEL_ID = os.getenv("SMOL_MODEL_ID", "meta-llama/Llama-3.3-70B-Instruct")
-HF_TOKEN = HfFolder.get_token()
+HF_TOKEN = "<DEIN_HF_HUB_TOKEN>"  # Ersetze durch deinen echten Hugging Face Token
 
 # --- Agent-Implementierung mit smolagents ---
 class BasicAgent:
     def __init__(self):
-        if not HF_TOKEN:
-            raise ValueError("Kein HF_HUB_TOKEN gesetzt! Lege ihn in den Space-Secrets an. ")
+        if not HF_TOKEN or HF_TOKEN.startswith("<"):
+            raise ValueError("Kein gültiger HF_HUB_TOKEN im Code gesetzt!")
         # InferenceClientModel initialisieren
         self.model = InferenceClientModel(
             model_id=MODEL_ID,
@@ -37,21 +36,23 @@ class BasicAgent:
 
 # --- Evaluation & Submission ---
 def run_and_submit_all(profile: gr.OAuthProfile | None):
+    # 1. Authentifizierung
     if not profile:
         return "Bitte logge dich zuerst bei Hugging Face ein.", None
     username = profile.username
     space_id = os.getenv("SPACE_ID")
 
+    # 2. Endpunkte
     questions_url = f"{DEFAULT_API_URL}/questions"
-    submit_url = f"{DEFAULT_API_URL}/submit"
+    submit_url    = f"{DEFAULT_API_URL}/submit"
 
-    # Agent instanziieren
+    # 3. Agent instanziieren
     try:
         agent = BasicAgent()
     except Exception as e:
         return f"Fehler beim Initialisieren des Agents: {e}", None
 
-    # Fragen abrufen
+    # 4. Fragen abrufen
     try:
         resp = requests.get(questions_url, timeout=15)
         resp.raise_for_status()
@@ -59,26 +60,33 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     except Exception as e:
         return f"Fehler beim Abrufen der Fragen: {e}", None
 
-    # Antworten generieren
+    # 5. Antworten generieren
     records = []
     answers = []
     for item in questions:
-        task_id = item.get("task_id")
-        question_text = item.get("question") or item.get("instruction", "")
-        if not task_id or not question_text:
+        task_id      = item.get("task_id")
+        question_txt = item.get("question") or item.get("instruction", "")
+        if not task_id or not question_txt:
             continue
-        ans = agent(question_text)
-        answers.append({"task_id": task_id, "submitted_answer": ans})
-        records.append({"Task ID": task_id, "Question": question_text, "Antwort": ans})
+        ans = agent(question_txt)
+        answers.append({
+            "task_id": task_id,
+            "submitted_answer": ans
+        })
+        records.append({
+            "Task ID": task_id,
+            "Question": question_txt,
+            "Antwort": ans
+        })
 
     if not answers:
         return "Der Agent hat keine Antworten produziert.", pd.DataFrame(records)
 
-    # Submission
+    # 6. Submission
     submission = {
-        "username": username.strip(),
+        "username":   username.strip(),
         "agent_code": f"https://huggingface.co/spaces/{space_id}/tree/main",
-        "answers": answers
+        "answers":    answers
     }
     try:
         resp = requests.post(submit_url, json=submission, timeout=60)
@@ -87,7 +95,8 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         status = (
             f"Erfolgreich eingereicht!\n"
             f"User: {result.get('username')}\n"
-            f"Score: {result.get('score')}% ({result.get('correct_count')}/{result.get('total_attempted')})\n"
+            f"Score: {result.get('score')}% "
+            f"({result.get('correct_count')}/{result.get('total_attempted')})\n"
             f"Nachricht: {result.get('message')}"
         )
     except Exception as e:
@@ -99,27 +108,19 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
 # --- Gradio UI ---
 with gr.Blocks() as demo:
     gr.Markdown("# GAIA Agent Evaluation Runner")
-    gr.Markdown(
-        """
-1. Lege in den Space-Secrets deinen `HF_HUB_TOKEN` an.
+    gr.Markdown("""
+1. Füge in den Space-Secrets deinen `HF_HUB_TOKEN` ein (oder setze ihn direkt im Code oben).
 2. Optional: Lege `SMOL_MODEL_ID` in den Secrets an (Standard: meta-llama/Llama-3.3-70B-Instruct).
 3. Aktualisiere `requirements.txt` mit:
-   ```
-   smolagents
-   huggingface-hub
-   gradio
-   requests
-   pandas
-   ```
 4. Commit & Push, warte auf Deployment.
-5. Logge dich ein und klicke auf **Run Evaluation & Submit All Answers**.
-        """
-    )
-    gr.LoginButton()
-    run_btn = gr.Button("Run Evaluation & Submit All Answers")
-    status_out = gr.Textbox(label="Status / Ergebnis", lines=5, interactive=False)
-    result_table = gr.DataFrame(label="Fragen & Antworten", wrap=True)
-    run_btn.click(fn=run_and_submit_all, inputs=[], outputs=[status_out, result_table])
+5. Logge dich mit dem Hugging Face Button ein.
+6. Klicke auf **Run Evaluation & Submit All Answers**.
+ """)
+ gr.LoginButton()
+ run_btn      = gr.Button("Run Evaluation & Submit All Answers")
+ status_out   = gr.Textbox(label="Status / Ergebnis", lines=5, interactive=False)
+ result_table = gr.DataFrame(label="Fragen & Antworten", wrap=True)
+ run_btn.click(fn=run_and_submit_all, inputs=[], outputs=[status_out, result_table])
 
 if __name__ == "__main__":
-    demo.launch(debug=True)
+ demo.launch(debug=True)