final

README.md

@@ -12,7 +12,6 @@ short_description: Multi-Agent Multi-Modal Multi-Model AI Platform
 tags:
 - agent-demo-track
 - mcp-server-track
-hf_oauth: true
 ---
 
 

app.py

@@ -2,90 +2,98 @@ import os
 import requests
 import pandas as pd
 import gradio as gr
-from crew import run_crew      # <-- ο multi-agent κώδικας σου
+from crew import run_crew          # ← your multi-agent logic
 
-API_URL = "https://agents-course-unit4-scoring.hf.space"   # endpoint αξιολόγησης
+API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 
-# ------------------------- Agent Wrapper ------------------------- #
+# ─── AGENT WRAPPER ──────────────────────────────────────────────────────────────
 class CrewAgent:
-    """Καλεί το run_crew και επιστρέφει την απάντηση."""
     def __call__(self, question: str) -> str:
-        return run_crew(question, file_path="")   # δεν περνάμε file στην αξιολόγηση
-
+        return run_crew(question, file_path="")   # It MUST use your real crew logic!
 
 agent = CrewAgent()
 
 
-# ------------------------ Main Submit Fn ------------------------- #
-def evaluate_and_submit():
-    """Τρέχει τον agent σε όλα τα benchmark Qs & στέλνει υποβολή."""
-    username = os.getenv("HF_SPACE_USER")
+# ─── MAIN HANDLER ───────────────────────────────────────────────────────────────
+def evaluate_and_submit(username: str):
+    """Runs the agent on benchmark questions and submits answers, with debug logging."""
+    username = username.strip()
     if not username:
-        return "❌ Πρέπει να είστε συνδεδεμένος στο Hugging Face.", None
+        return "❌  Please enter your Hugging Face username.", None
 
     space_id   = os.getenv("SPACE_ID", "")
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else ""
 
-    # 1. Φέρνουμε τις ερωτήσεις
+    # 1) Fetch questions
     try:
         questions = requests.get(f"{API_URL}/questions", timeout=30).json()
     except Exception as e:
-        return f"❌ Αποτυχία λήψης ερωτήσεων: {e}", None
+        return f"❌  Failed to fetch questions: {e}", None
 
-    # 2. Απαντάμε
+    # 2) Answer questions, logging every result
     answers, log = [], []
     for item in questions:
-        qid, qtext = item["task_id"], item["question"]
+        qid, qtxt = item["task_id"], item["question"]
         try:
-            ans = agent(qtext)
+            ans = agent(qtxt)
+            # Debug print:
+            print(f"QID: {qid} | Q: {qtxt[:60]}... | Agent Answer: {ans}")
+            # Add warning if placeholder detected
+            if ans.strip().lower() in ["this is a default answer.", "", "n/a"]:
+                print(f"⚠️ Warning: Agent returned a default/empty answer for QID {qid}.")
         except Exception as e:
             ans = f"AGENT ERROR: {e}"
+            print(f"⚠️ Agent error on QID {qid}: {e}")
         answers.append({"task_id": qid, "submitted_answer": ans})
-        log.append({"Task ID": qid, "Question": qtext, "Answer": ans})
+        log.append({"Task ID": qid, "Question": qtxt, "Answer": ans})
+
+    # Show part of the DataFrame in the console for debugging
+    try:
+        df = pd.DataFrame(log)
+        print("=== First 5 results ===")
+        print(df.head())
+    except Exception as e:
+        print(f"DataFrame print error: {e}")
 
     if not answers:
-        return "⚠️ Δεν δημιουργήθηκαν απαντήσεις.", pd.DataFrame(log)
+        return "⚠️  No answers generated.", pd.DataFrame(log)
 
-    # 3. Υποβολή
+    # 3) Submit
     try:
         resp = requests.post(
             f"{API_URL}/submit",
-            json={
-                "username":     username,
-                "agent_code":   agent_code,
-                "answers":      answers,
-            },
-            timeout=60
+            json={"username": username, "agent_code": agent_code, "answers": answers},
+            timeout=60,
         )
         resp.raise_for_status()
-        data   = resp.json()
+        data = resp.json()
         status = (
-            f"✅ Υποβολή επιτυχής!\n"
-            f"Σκορ: {data.get('score')} % "
+            "✅  Submission successful!\n"
+            f"Score: {data.get('score')} % "
             f"({data.get('correct_count')}/{data.get('total_attempted')})\n"
-            f"Μήνυμα: {data.get('message')}"
+            f"Message: {data.get('message')}"
         )
     except Exception as e:
-        status = f"❌ Αποτυχία υποβολής: {e}"
+        status = f"❌  Submission failed: {e}"
 
     return status, pd.DataFrame(log)
 
 
-# ------------------------ Gradio UI ------------------------------ #
+
+# ─── GRADIO UI ────────────────────────────────────────────��─────────────────────
 demo = gr.Interface(
     fn=evaluate_and_submit,
-    inputs=[],   # καμία είσοδος απ’ τον χρήστη
+    inputs=gr.Textbox(label="Hugging Face username", placeholder="e.g. john-doe"),
     outputs=[
-        gr.Textbox(label="Κατάσταση", lines=6),
-        gr.DataFrame(label="Απαντήσεις που υποβλήθηκαν")
+        gr.Textbox(label="Status", lines=6),
+        gr.DataFrame(label="Submitted Answers"),
     ],
     title="GAIA Agent Submission",
     description=(
-        "Πατήστε το κουμπί για να τρέξετε τον agent σας "
-        "σε όλα τα benchmark ερωτήματα και να στείλετε τις απαντήσεις."
-        " Απαιτείται να είστε συνδεδεμένος στον λογαριασμό Hugging Face."
-    )
+        "Enter your Hugging Face username and click **Run Evaluation & Submit**. "
+        "The app will run your agent on all benchmark questions and send the answers."
+    ),
 )
 
 if __name__ == "__main__":