Update app.py

app.py

@@ -1,10 +1,8 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
 import re
-import json
 import textwrap
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
@@ -73,6 +71,7 @@ class BasicAgent:
         print(f"💡 Agent raw: '{raw_answer[:80]}' → clean: '{clean_ans}'")
         return clean_ans
 
+
 # --- Test Questions & Expected Answers ---
 QUESTIONS_AND_ANSWERS = [
     {"question": "What is the capital of France?", "expected": "Paris"},
@@ -82,6 +81,7 @@ QUESTIONS_AND_ANSWERS = [
     {"question": "Who wrote 'Romeo and Juliet'?", "expected": "Shakespeare"},
 ]
 
+
 # --- Submission Function ---
 def submit_answers(answers: list, token: str) -> dict:
     """
@@ -109,38 +109,35 @@ def submit_answers(answers: list, token: str) -> dict:
     except Exception as e:
         return {"success": False, "message": str(e)}
 
-# --- Main Run Function ---
-def run_and_submit_all(profile: gr.OAuthProfile | None = None, logged_in: bool | None = None):
-    if not profile:
-        return "❌ You must log in first.", pd.DataFrame()
-    
-    # Try to get the token using multiple approaches
-    token = None
-    # Try direct attributes
-    if hasattr(profile, '_oauth_token'):
-        token = profile._oauth_token
-    elif hasattr(profile, 'oauth_token'):
-        token_obj = profile.oauth_token
-        if hasattr(token_obj, 'access_token'):
-            token = token_obj.access_token
-        elif isinstance(token_obj, dict) and 'access_token' in token_obj:
-            token = token_obj['access_token']
-        else:
-            token = token_obj
-    
+
+# --- Main Run Function (fixed for HF_TOKEN) ---
+def run_and_submit_all(profile: gr.OAuthProfile | None = None, *_):
+    """
+    Runs the local agent and submits answers.
+    Uses HF_TOKEN from environment variables instead of Gradio OAuth token.
+    """
+    # --- Step 1: Detect user login ---
+    if profile and hasattr(profile, "name"):
+        print(f"👤 Logged in as: {profile.name}")
+    else:
+        print("⚠️ No OAuth profile detected (this is fine if HF_TOKEN is set).")
+
+    # --- Step 2: Get token from environment variable ---
+    token = os.getenv("HF_TOKEN")
     if not token:
-        # Debug: print what attributes the profile actually has
-        attrs = [attr for attr in dir(profile) if not attr.startswith('__')]
-        print(f"🔍 Profile attributes: {attrs}")
-        return f"❌ No token found. Profile type: {type(profile).__name__}. Available attributes: {', '.join(attrs[:10])}", pd.DataFrame()
-    
-    # Instantiate the agent
+        return (
+            "❌ No token found. Please set your Hugging Face token as an environment variable named `HF_TOKEN`.\n"
+            "In your Space: Settings → Repository secrets → Add new secret → Name: HF_TOKEN, Value: your_token_here",
+            pd.DataFrame(),
+        )
+
+    # --- Step 3: Instantiate the agent ---
     try:
         agent = BasicAgent()
     except Exception as e:
         return f"❌ Error instantiating agent: {e}", pd.DataFrame()
-    
-    # Collect answers
+
+    # --- Step 4: Collect answers ---
     results = []
     for qa in QUESTIONS_AND_ANSWERS:
         q = qa["question"]
@@ -150,14 +147,14 @@ def run_and_submit_all(profile: gr.OAuthProfile | None = None, logged_in: bool |
         except Exception as e:
             answer = f"[Error: {e}]"
         results.append({"question": q, "answer": answer, "expected": expected})
-    
-    # Build DataFrame
+
+    # --- Step 5: Build DataFrame ---
     df = pd.DataFrame(results)
-    
-    # Submit
+
+    # --- Step 6: Submit ---
     answers_list = [r["answer"] for r in results]
     submission_result = submit_answers(answers_list, token)
-    
+
     if submission_result.get("success"):
         msg = submission_result.get("message", "✅ Submission successful")
         return f"✅ {msg}", df
@@ -165,24 +162,32 @@ def run_and_submit_all(profile: gr.OAuthProfile | None = None, logged_in: bool |
         msg = submission_result.get("message", "Unknown error")
         return f"❌ Submission failed: {msg}", df
 
+
 # --- Gradio Interface ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("# 🧠 Basic Agent Evaluation Runner")
     gr.Markdown(
         """
         **Instructions:**
         1. Log in to your Hugging Face account.
-        2. Click 'Run Evaluation & Submit All Answers'.
+        2. Add your Hugging Face token as a secret named `HF_TOKEN` in your Space settings.
+        3. Click **Run Evaluation & Submit All Answers**.
+
         ---
         The agent now runs *locally* inside the Space instead of using the API.
         """
     )
     login_button = gr.LoginButton()
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
+    run_button = gr.Button("🚀 Run Evaluation & Submit All Answers")
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-    
-    run_button.click(fn=run_and_submit_all, inputs=[login_button], outputs=[status_output, results_table])
+
+    run_button.click(
+        fn=run_and_submit_all,
+        inputs=[login_button],
+        outputs=[status_output, results_table],
+    )
+
 
 # --- Launch ---
 if __name__ == "__main__":
@@ -206,4 +211,4 @@ if __name__ == "__main__":
     
     print("-" * (60 + len(" App Starting ")) + "\n")
     print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=False)