Update app.py

app.py

@@ -7,123 +7,94 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Advanced Agent Logic ---
+# --- Basic Agent Logic ---
 class BasicAgent:
     def __init__(self):
         print("BasicAgent initialized.")
-        self.client = None  # Placeholder for Groq or another API client
-        self.agent_prompt = (
-            """You are a general AI assistant. I will ask you a question. Report your thoughts, and
-            finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]."""
-        )
-        
-        # Assuming some model for queries
         self.llm = AutoModelForCausalLM.from_pretrained("gpt2")
         self.tokenizer = AutoTokenizer.from_pretrained("gpt2")
-        
-    def query_groq(self, question: str) -> str:
-        # Placeholder for Groq query handling
-        return f"FINAL ANSWER: {question}"
-
-    def query_tools(self, question: str) -> str:
-        # Placeholder for using tools
-        return f"FINAL ANSWER: {question}"
+        self.agent_prompt = (
+            "You are a general AI assistant. I will ask you a question. "
+            "Finish your answer with the format: FINAL ANSWER: [YOUR FINAL ANSWER]."
+        )
 
     def __call__(self, question: str) -> str:
-        # Decide based on the question type, here we use placeholder logic
-        if "use tools" in question.lower():
-            return self.query_tools(question)
-        return self.query_groq(question)
+        input_text = f"{self.agent_prompt}\n\nQuestion: {question}"
+        inputs = self.tokenizer(input_text, return_tensors="pt")
+        outputs = self.llm.generate(**inputs)
+        decoded = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        final = decoded.split("FINAL ANSWER:")[-1].strip()
+        return f"FINAL ANSWER: {final}" if final else "FINAL ANSWER: UNKNOWN"
 
-# --- Evaluation and Submission Logic ---
-def run_and_submit_all(profile):
-    space_id = os.getenv("SPACE_ID")
-    if profile:
-        username = profile
-        print(f"User logged in: {username}")
-    else:
-        return "Please provide a username.", None
+# --- Submission Function ---
+def run_and_submit_all(username):
+    space_id = os.getenv("SPACE_ID", "your-username/your-space")  # fallback
+    if not username.strip():
+        return "Username is required for submission.", None
 
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    agent = BasicAgent()
 
-    try:
-        agent = BasicAgent()
-    except Exception as e:
-        return f"Error initializing agent: {e}", None
+    questions_url = f"{DEFAULT_API_URL}/questions"
+    submit_url = f"{DEFAULT_API_URL}/submit"
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
 
     try:
         response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
         questions_data = response.json()
-        if not questions_data:
-            return "Fetched questions list is empty or invalid format.", None
     except Exception as e:
-        return f"Error fetching questions: {e}", None
+        return f"Failed to fetch questions: {e}", None
 
-    results_log = []
-    answers_payload = []
+    answers = []
+    log = []
 
     for item in questions_data:
         task_id = item.get("task_id")
-        question_text = item.get("question")
-        if not task_id or question_text is None:
+        question = item.get("question")
+        if not task_id or not question:
             continue
         try:
-            submitted_answer = agent(question_text)
-            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+            answer = agent(question)
+            answers.append({"task_id": task_id, "submitted_answer": answer})
+            log.append({"Task ID": task_id, "Question": question, "Submitted Answer": answer})
         except Exception as e:
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+            log.append({"Task ID": task_id, "Question": question, "Submitted Answer": f"ERROR: {e}"})
 
-    if not answers_payload:
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+    if not answers:
+        return "No answers submitted.", pd.DataFrame(log)
 
-    submission_data = {
+    payload = {
         "username": username.strip(),
         "agent_code": agent_code,
-        "answers": answers_payload
+        "answers": answers
     }
 
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response = requests.post(submit_url, json=payload, timeout=30)
         response.raise_for_status()
-        result_data = response.json()
-        final_status = (
+        result = response.json()
+        status = (
             f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
+            f"User: {result.get('username')}\n"
+            f"Score: {result.get('score', 'N/A')}% "
+            f"({result.get('correct_count', '?')}/{result.get('total_attempted', '?')} correct)\n"
+            f"Message: {result.get('message', '')}"
         )
-        return final_status, pd.DataFrame(results_log)
+        return status, pd.DataFrame(log)
     except Exception as e:
-        return f"Submission Failed: {e}", pd.DataFrame(results_log)
+        return f"Submission failed: {e}", pd.DataFrame(log)
 
 # --- Gradio UI ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        **Instructions:**
-        1. Clone and customize your agent logic.
-        2. Log in with Hugging Face.
-        3. Click the button to run evaluation and submit your answers.
-        """
-    )
-    profile_input = gr.Textbox(label="Enter Username", placeholder="Enter your username", interactive=True)
-    run_button = gr.Button("Run Evaluation")
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+    gr.Markdown("## 🚀 Basic Agent Evaluation & Submission")
+    gr.Markdown("Enter your Hugging Face username and press **Run and Submit** to evaluate your agent and submit your results.")
     
-    run_button.click(
-        fn=run_and_submit_all,
-        inputs=[profile_input],
-        outputs=[status_output, results_table]
-    )
+    username_input = gr.Textbox(label="Hugging Face Username", placeholder="e.g. your-hf-username")
+    run_button = gr.Button("Run and Submit")
+    status_output = gr.Textbox(label="Submission Status", lines=4, interactive=False)
+    results_table = gr.DataFrame(label="Submitted Answers")
+
+    run_button.click(fn=run_and_submit_all, inputs=[username_input], outputs=[status_output, results_table])
 
 if __name__ == "__main__":
-    print("Launching Gradio Interface...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True)