Update app.py

app.py

@@ -7,10 +7,17 @@ import requests
 import pandas as pd
 import gradio as gr
 
-# --- Config from Env ---
-API_URL      = os.getenv("API_URL", "https://agents-course-unit4-scoring.hf.space")
-MODEL_ID     = os.getenv("MODEL_ID", "meta-llama/Llama-2-7b-instruct")
-HF_TOKEN_ENV = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+# ─── Configuration ──────────────────────────────────────────────────────────
+API_URL        = os.getenv("API_URL", "https://agents-course-unit4-scoring.hf.space")
+MODEL_ID       = os.getenv("MODEL_ID", "meta-llama/Llama-2-7b-instruct")
+HF_TOKEN       = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+if not HF_TOKEN:
+    raise RuntimeError(
+        "❌ Please set HUGGINGFACEHUB_API_TOKEN in your Space Secrets."
+    )
+
+HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}
 
 WELCOME = """
 ## GAIA Benchmark Runner 🎉
@@ -19,14 +26,26 @@ Build your agent, score **≥30%** to earn your Certificate,
 and see where you land on the Student Leaderboard!
 """
 
+# ─── Utility to fetch your HF username from the token ────────────────────────
+def get_hf_username():
+    try:
+        resp = requests.get("https://huggingface.co/api/whoami-v2", headers=HEADERS, timeout=10)
+        resp.raise_for_status()
+        data = resp.json()
+        # V2 returns {"user": { "id": ..., "username": ... }, ...}
+        return data.get("user", {}).get("username") or data.get("name")
+    except Exception as e:
+        print("[DEBUG] whoami failed:", e)
+        return None
+
+# ─── Simple HF-Inference Agent ─────────────────────────────────────────────
 class GAIAAgent:
-    def __init__(self, model_id: str, token: str):
-        print(f"[DEBUG] Initializing GAIAAgent with model={model_id}")  # debug
+    def __init__(self, model_id: str):
+        print(f"[DEBUG] Initializing with model {model_id}")
         self.model_id = model_id
-        self.headers = {"Authorization": f"Bearer {token}"}
+        self.headers = HEADERS
 
     def answer(self, prompt: str) -> str:
-        print(f"[DEBUG] Sending prompt of length {len(prompt)} to HF Inference")  # debug
         payload = {
             "inputs": prompt,
             "parameters": {"max_new_tokens": 512, "temperature": 0.2}
@@ -35,65 +54,50 @@ class GAIAAgent:
         resp = requests.post(url, headers=self.headers, json=payload, timeout=60)
         resp.raise_for_status()
         data = resp.json()
-        print(f"[DEBUG] Got response from model: {data!r}")  # debug
         if isinstance(data, list) and data and "generated_text" in data[0]:
             return data[0]["generated_text"].strip()
         return str(data)
 
-def run_and_submit_all(profile: gr.OAuthProfile | None):
+# ─── Gradio callback ────────────────────────────────────────────────────────
+def run_and_submit_all():
     try:
-        print("[DEBUG] run_and_submit_all called")  # debug
-        if profile is None:
-            print("[DEBUG] No profile provided")  # debug
-            return ("⚠️ Please log in with your Hugging Face account.", pd.DataFrame())
-        print(f"[DEBUG] Logged in as: {profile.username}")  # debug
-        username = profile.username
-
-        hf_token = HF_TOKEN_ENV or getattr(profile, "access_token", None)
-        print(f"[DEBUG] Using HF token from {'env' if HF_TOKEN_ENV else 'profile'}")  # debug
-        if not hf_token:
-            print("[DEBUG] No HF token found")  # debug
-            return (
-                "❌ No Hugging Face token found.\n"
-                "Set HUGGINGFACEHUB_API_TOKEN in Secrets or log in via the button.",
-                pd.DataFrame()
-            )
+        # 0) Resolve username
+        username = get_hf_username()
+        if not username:
+            return "❌ Could not fetch your HF username. Check your token.", pd.DataFrame()
 
         # 1) Fetch GAIA questions
-        print(f"[DEBUG] Fetching questions from {API_URL}/questions")  # debug
         q_resp = requests.get(f"{API_URL}/questions", timeout=15)
         q_resp.raise_for_status()
         questions = q_resp.json() or []
-        print(f"[DEBUG] Received {len(questions)} questions")  # debug
         if not questions:
-            return ("❌ No questions found. Check your API_URL.", pd.DataFrame())
+            return "❌ No questions returned; check your API_URL.", pd.DataFrame()
 
         # 2) Init agent
-        agent = GAIAAgent(MODEL_ID, hf_token)
+        agent = GAIAAgent(MODEL_ID)
 
         # 3) Answer each
         results = []
         payload = []
         for item in questions:
-            print(f"[DEBUG] Processing task_id={item.get('task_id')}")  # debug
             tid  = item.get("task_id")
             qtxt = item.get("question", "")
             try:
                 ans = agent.answer(qtxt)
             except Exception as e:
                 ans = f"ERROR: {e}"
-                print(f"[DEBUG] Error answering: {e}")  # debug
             results.append({"Task ID": tid, "Question": qtxt, "Answer": ans})
             payload.append({"task_id": tid, "submitted_answer": ans})
             time.sleep(0.5)
 
-        # 4) Submit
-        print(f"[DEBUG] Submitting payload with {len(payload)} answers")  # debug
-        submission = {"username": username, "answers": payload}
+        # 4) Submit all answers
+        submission = {
+            "username":   username,
+            "answers":    payload
+        }
         s_resp = requests.post(f"{API_URL}/submit", json=submission, timeout=60)
         s_resp.raise_for_status()
         data = s_resp.json()
-        print(f"[DEBUG] Submission response: {data!r}")  # debug
 
         # 5) Build status text
         status = (
@@ -108,18 +112,18 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     except Exception as e:
         tb = traceback.format_exc()
         print("[ERROR] Unhandled exception:\n", tb)
-        return (f"❌ Unexpected error:\n{e}", pd.DataFrame())
+        return (f"❌ Unexpected error:\n{e}\n\nSee logs for details."), pd.DataFrame()
 
+# ─── Gradio UI ──────────────────────────────────────────────────────────────
 with gr.Blocks() as demo:
     gr.Markdown(WELCOME)
-    login    = gr.LoginButton()
     run_btn  = gr.Button("▶️ Run GAIA Benchmark")
     status   = gr.Markdown()
     table_df = gr.Dataframe(headers=["Task ID", "Question", "Answer"], wrap=True)
 
     run_btn.click(
         fn=run_and_submit_all,
-        inputs=[login],
+        inputs=[],
         outputs=[status, table_df]
     )