Update app.py

app.py

@@ -6,154 +6,113 @@ from transformers import pipeline
 
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-HF_MODEL_NAME = "facebook/bart-large-mnli"  # Smaller, free model that works well in Spaces
+HF_MODEL_NAME = "facebook/bart-large-mnli"  # Free model that works in Spaces
 
-# --- Enhanced Agent Definition ---
+# --- Agent Definition ---
 class BasicAgent:
-    def __init__(self, hf_token=None):
-        print("Initializing LLM Agent...")
-        self.hf_token = hf_token
-        self.llm = None
-        
+    def __init__(self):
+        print("Initializing Agent...")
         try:
-            # Using a smaller model that works better in Spaces
             self.llm = pipeline(
                 "text-generation",
                 model=HF_MODEL_NAME,
-                token=hf_token,
                 device_map="auto"
             )
-            print("LLM initialized successfully")
         except Exception as e:
-            print(f"Error initializing LLM: {e}")
-            # Fallback to simple responses if LLM fails
+            print(f"LLM initialization failed: {e}")
             self.llm = None
 
     def __call__(self, question: str) -> str:
         if not self.llm:
-            return "This is a default answer (LLM not available)"
-            
+            return "Default answer (LLM not available)"
+        
         try:
-            print(f"Generating answer for: {question[:50]}...")
-            response = self.llm(
-                question,
-                max_length=100,
-                do_sample=True,
-                temperature=0.7
-            )
+            response = self.llm(question, max_length=100)
             return response[0]['generated_text']
         except Exception as e:
-            print(f"Error generating answer: {e}")
-            return f"Error generating answer: {e}"
+            return f"Error: {str(e)}"
+
+def run_and_submit_all():
+    """Simplified version that works with Gradio auth"""
+    # Get username from Gradio's auth system
+    username = os.getenv("GRADIO_AUTH_USERNAME")
+    if not username:
+        return "Please login first", None
 
-def run_and_submit_all(request: gr.Request):
-    """
-    Modified to work with Gradio's auth system
-    """
-    # Get username from auth
-    if not request.username:
-        return "Please login with Hugging Face account", None
-    
-    username = request.username
     space_id = os.getenv("SPACE_ID")
     api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-
-    # 1. Instantiate Agent
-    try:
-        agent = BasicAgent(hf_token=os.getenv("HF_TOKEN"))
-    except Exception as e:
-        return f"Error initializing agent: {e}", None
+    agent = BasicAgent()
     
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-
-    # 2. Fetch Questions
+    # Fetch questions
     try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-        if not questions_data:
-            return "No questions received from server", None
+        response = requests.get(f"{api_url}/questions", timeout=15)
+        questions = response.json()
     except Exception as e:
-        return f"Error fetching questions: {e}", None
+        return f"Failed to get questions: {str(e)}", None
 
-    # 3. Process Questions
-    results_log = []
-    answers_payload = []
-    for item in questions_data:
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        if not task_id or not question_text:
-            continue
-            
+    # Process questions
+    results = []
+    answers = []
+    for q in questions:
         try:
-            answer = agent(question_text)
-            answers_payload.append({
-                "task_id": task_id,
+            answer = agent(q.get("question", ""))
+            answers.append({
+                "task_id": q.get("task_id"),
                 "submitted_answer": answer
             })
-            results_log.append({
-                "Task ID": task_id,
-                "Question": question_text,
-                "Submitted Answer": answer
+            results.append({
+                "Task ID": q.get("task_id"),
+                "Question": q.get("question"),
+                "Answer": answer
             })
         except Exception as e:
-            results_log.append({
-                "Task ID": task_id,
-                "Question": question_text,
-                "Submitted Answer": f"ERROR: {str(e)}"
+            results.append({
+                "Task ID": q.get("task_id"),
+                "Question": q.get("question"),
+                "Answer": f"Error: {str(e)}"
             })
 
-    if not answers_payload:
-        return "No valid answers generated", pd.DataFrame(results_log)
-
-    # 4. Submit Answers
-    submission_data = {
-        "username": username,
-        "agent_code": agent_code,
-        "answers": answers_payload
-    }
-    
+    # Submit answers
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
+        response = requests.post(
+            f"{api_url}/submit",
+            json={
+                "username": username,
+                "agent_code": f"https://huggingface.co/spaces/{space_id}",
+                "answers": answers
+            },
+            timeout=60
+        )
         result = response.json()
-        
-        status = (
-            f"Submission Successful!\n"
-            f"User: {result.get('username')}\n"
-            f"Score: {result.get('score', 'N/A')}% "
-            f"({result.get('correct_count', '?')}/{result.get('total_attempted', '?')})\n"
-            f"Message: {result.get('message', '')}"
+        return (
+            f"Success! Score: {result.get('score', 'N/A')}%\n"
+            f"Correct: {result.get('correct_count', 0)}/{result.get('total_attempted', 0)}",
+            pd.DataFrame(results)
         )
-        return status, pd.DataFrame(results_log)
     except Exception as e:
-        return f"Submission failed: {str(e)}", pd.DataFrame(results_log)
+        return f"Submission failed: {str(e)}", pd.DataFrame(results)
 
 # --- Gradio Interface ---
 with gr.Blocks() as demo:
-    gr.Markdown("# LLM Agent Evaluation Runner")
-    gr.Markdown("""
-        **Instructions:**
-        1. Log in with your Hugging Face account
-        2. Click 'Run Evaluation'
-        3. View your results
-    """)
-
+    gr.Markdown("# LLM Agent Evaluation")
+    
+    with gr.Accordion("Instructions", open=False):
+        gr.Markdown("""
+        1. Click the login button
+        2. Authorize with your Hugging Face account
+        3. Click 'Run Evaluation'
+        """)
+    
     gr.LoginButton()
     
-    with gr.Row():
-        run_btn = gr.Button("Run Evaluation & Submit Answers", variant="primary")
+    run_btn = gr.Button("Run Evaluation", variant="primary")
+    status = gr.Textbox(label="Status")
+    results = gr.DataFrame(label="Results", wrap=True)
     
-    status_output = gr.Textbox(label="Status", interactive=False)
-    results_table = gr.DataFrame(label="Results", wrap=True)
-
     run_btn.click(
         fn=run_and_submit_all,
-        inputs=[],
-        outputs=[status_output, results_table]
+        outputs=[status, results]
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(auth_message="Please login with your Hugging Face account")