test3

app.py

@@ -1,6 +1,6 @@
 import os
-import datetime
 import requests
+import datetime
 import pandas as pd
 import gradio as gr
 from openai import OpenAI
@@ -9,158 +9,137 @@ class ToolEnhancedAgent:
     def __init__(self):
         api_key = os.getenv("OPENAI_API_KEY")
         if not api_key:
-            raise ValueError("OPENAI_API_KEY not found in environment variables.")
+            raise ValueError("OPENAI_API_KEY is not set.")
         self.client = OpenAI(api_key=api_key)
-        print("ToolEnhancedAgent initialized.")
+        print("ToolEnhancedAgent initialized with GPT + CoT + Tools.")
 
     def use_tool(self, tool_name: str, input_text: str) -> str:
         try:
             if tool_name == "calculator":
-                # Sangat dasar dan raw eval, hati2 di produksi
                 return str(eval(input_text))
             elif tool_name == "date":
                 return str(datetime.datetime.now().date())
             elif tool_name == "wikipedia":
                 return self.search_wikipedia(input_text)
             else:
-                return "[Unknown tool]"
+                return "[Tool Error: Unknown Tool]"
         except Exception as e:
-            return f"[Tool error: {e}]"
+            return f"[Tool Error: {e}]"
 
-    def search_wikipedia(self, query: str) -> str:
+    def search_wikipedia(self, query):
         try:
-            url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{query}"
-            res = requests.get(url)
+            res = requests.get(f"https://en.wikipedia.org/api/rest_v1/page/summary/{query}")
             if res.status_code == 200:
                 return res.json().get("extract", "No summary found.")
             else:
-                return f"No Wikipedia summary for '{query}'."
+                return f"No Wikipedia summary for {query}."
         except Exception as e:
-            return f"Wikipedia API error: {e}"
+            return f"Wiki error: {e}"
 
     def __call__(self, question: str) -> str:
-        # Prompt dengan chain of thought agar GPT berpikir langkah demi langkah
         prompt = (
-            "You are an assistant that solves problems step-by-step, and you can use tools like calculator, date, and wikipedia if needed.\n"
+            "You are a helpful AI assistant. Use tools when necessary. "
+            "Think step-by-step before answering. Respond clearly.\n\n"
             f"Question: {question}\n"
-            "Answer (think step-by-step and use tools if helpful):"
+            "Answer (show thinking steps):"
         )
 
         try:
             response = self.client.chat.completions.create(
-                model="gpt-4o-mini",  # model yang lebih ringan dan biasanya cukup
+                model="gpt-4o-mini",
                 messages=[
-                    {"role": "system", "content": "You are a helpful assistant that thinks step-by-step and can use tools."},
+                    {"role": "system", "content": "You are a smart assistant that can use tools and think step-by-step."},
                     {"role": "user", "content": prompt}
                 ],
-                temperature=0.2,
+                temperature=0.3,
                 max_tokens=700,
             )
             answer = response.choices[0].message.content.strip()
-            print(f"Generated answer (preview): {answer[:100]}...")
+            print(f"Answer generated: {answer[:100]}...")
             return answer
         except Exception as e:
-            print(f"Agent error: {e}")
-            return f"[Agent error: {e}]"
-
+            print(f"[Agent Error]: {e}")
+            return f"[Agent Error: {e}]"
 
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    if profile is None:
-        return "Please login to Hugging Face to submit answers.", None
+def run_and_submit_all(profile):
+    if not profile:
+        return "Please login to Hugging Face first.", None
 
     username = profile.username
     api_url = "https://agents-course-unit4-scoring.hf.space"
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
+    # Instantiate agent
     try:
         agent = ToolEnhancedAgent()
     except Exception as e:
         return f"Agent initialization error: {e}", None
 
+    # Fetch questions
     try:
         response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
         questions = response.json()
-        if not isinstance(questions, list) or len(questions) == 0:
-            return "Failed to fetch questions or empty list.", None
+        if not questions:
+            return "No questions fetched.", None
     except Exception as e:
         return f"Error fetching questions: {e}", None
 
-    answers = []
+    answers_payload = []
     results_log = []
 
-    for item in questions:
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        if not task_id or question_text is None:
+    for q in questions:
+        task_id = q.get("task_id")
+        question = q.get("question")
+        if not task_id or question is None:
             continue
         try:
-            answer = agent(question_text)
-            answers.append({"task_id": task_id, "submitted_answer": answer})
-            results_log.append({
-                "Task ID": task_id,
-                "Question": question_text,
-                "Submitted Answer": answer
-            })
+            answer = agent(question)
+            answers_payload.append({"task_id": task_id, "submitted_answer": answer})
+            results_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}]"
-            })
+            results_log.append({"Task ID": task_id, "Question": question, "Submitted Answer": f"Error: {e}"})
 
-    if not answers:
-        return "Agent did not produce answers.", pd.DataFrame(results_log)
+    if not answers_payload:
+        return "No answers generated.", pd.DataFrame(results_log)
 
-    agent_code_url = f"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}/tree/main"
-
-    submission_payload = {
+    submission_data = {
         "username": username,
-        "agent_code": agent_code_url,
-        "answers": answers
+        "agent_code": f"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}/tree/main",
+        "answers": answers_payload,
     }
 
     try:
-        submit_response = requests.post(submit_url, json=submission_payload, timeout=60)
-        submit_response.raise_for_status()
-        result = submit_response.json()
-
-        final_status = (
-            f"Submission Success!\n"
+        resp = requests.post(submit_url, json=submission_data, timeout=60)
+        resp.raise_for_status()
+        result = resp.json()
+        status = (
+            f"Submission Successful!\n"
             f"User: {result.get('username')}\n"
             f"Score: {result.get('score', 'N/A')}%\n"
             f"Correct: {result.get('correct_count', '?')}/{result.get('total_attempted', '?')}\n"
-            f"Message: {result.get('message', 'No message')}"
+            f"Message: {result.get('message', '')}"
         )
-        return final_status, pd.DataFrame(results_log)
-
+        results_df = pd.DataFrame(results_log)
+        return status, results_df
     except Exception as e:
-        return f"Submission error: {e}", pd.DataFrame(results_log)
-
+        results_df = pd.DataFrame(results_log)
+        return f"Submission failed: {e}", results_df
 
 with gr.Blocks() as demo:
-    gr.Markdown("# GAIA Benchmark - Tool Enhanced Agent")
-
-    gr.Markdown(
-        """
-        1. Login with your Hugging Face account.
-        2. Click 'Run Evaluation & Submit All Answers' to run the agent on GAIA tasks.
-        3. View your results and submission status.
-        """
-    )
+    gr.Markdown("# ToolEnhancedAgent for GAIA Benchmark")
+    login_btn = gr.LoginButton()
+    run_btn = gr.Button("Run Evaluation & Submit All Answers")
 
-    login_button = gr.LoginButton()
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-    status_box = gr.Textbox(label="Status / Submission Result", lines=7)
-    results_table = gr.DataFrame(label="Questions and Agent Answers")
+    status_output = gr.Textbox(label="Status / Result", lines=6, interactive=False)
+    results_table = gr.DataFrame(headers=["Task ID", "Question", "Submitted Answer"], label="Agent Answers", wrap=True)
 
-    run_button.click(
+    run_btn.click(
         fn=run_and_submit_all,
-        inputs=login_button,
-        outputs=[status_box, results_table]
+        inputs=[login_btn],
+        outputs=[status_output, results_table]
     )
 
-
 if __name__ == "__main__":
     demo.launch()