test3

app.py

@@ -2,131 +2,110 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
-import datetime
-from openai import OpenAI
 
+# --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-class ToolEnhancedAgent:
+# --- Agent Definition (replace with your own logic) ---
+class BasicAgent:
     def __init__(self):
-        api_key = os.getenv("OPENAI_API_KEY")
-        if not api_key:
-            raise ValueError("OPENAI_API_KEY is not set.")
-        self.client = OpenAI(api_key=api_key)
-        print("ToolEnhancedAgent initialized.")
-
-    def use_tool(self, tool_name: str, input_text: str) -> str:
-        try:
-            if tool_name == "calculator":
-                return str(eval(input_text))
-            elif tool_name == "date":
-                return str(datetime.datetime.now().date())
-            elif tool_name == "wikipedia":
-                res = requests.get(f"https://en.wikipedia.org/api/rest_v1/page/summary/{input_text}")
-                if res.status_code == 200:
-                    return res.json().get("extract", "No summary found.")
-                else:
-                    return "No summary found."
-            else:
-                return "[Unknown Tool]"
-        except Exception as e:
-            return f"[Tool Error: {e}]"
+        print("✅ BasicAgent initialized.")
 
     def __call__(self, question: str) -> str:
-        try:
-            prompt = f"""
-You are an advanced AI assistant with access to tools like calculator, date lookup, and Wikipedia.
-Follow this format:
-Step 1: Think step-by-step.
-Step 2: Use tool if needed.
-Step 3: Final answer.
-
-Question: {question}
-Answer:
-"""
-            messages = [
-                {"role": "system", "content": "You are a helpful assistant that uses tools and thinks step-by-step."},
-                {"role": "user", "content": prompt}
-            ]
-            response = self.client.chat.completions.create(
-                model="gpt-4",
-                messages=messages,
-                temperature=0.3,
-                max_tokens=700
-            )
-            return response.choices[0].message.content.strip()
-        except Exception as e:
-            return f"[Agent Error: {e}]"
+        print(f"📥 Question received: {question[:60]}...")
+        return "Paris" if "capital of France" in question else "42"
 
-def run_and_submit_all():
-    profile_username = os.getenv("HF_USER") or "anonymous"
-    space_id = os.getenv("SPACE_ID")
+# --- Evaluation Function ---
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    space_id = os.getenv("SPACE_ID") or "your-username/your-space"  # fallback
+
+    if not profile:
+        return "⚠️ Please log in to Hugging Face to submit.", None
+
+    username = profile.username
+    print(f"🔐 Logged in as: {username}")
+
+    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"
-    
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
 
-    try:
-        agent = ToolEnhancedAgent()
-    except Exception as e:
-        return f"Failed to initialize agent: {e}", pd.DataFrame()
+    # Instantiate your agent
+    agent = BasicAgent()
 
+    # Fetch questions
     try:
-        res = requests.get(questions_url, timeout=20)
-        res.raise_for_status()
-        questions_data = res.json()
+        response = requests.get(questions_url, timeout=15)
+        response.raise_for_status()
+        questions = response.json()
+        print(f"📦 {len(questions)} questions fetched.")
     except Exception as e:
-        return f"Error fetching questions: {e}", pd.DataFrame()
+        return f"❌ Failed to fetch questions: {e}", None
 
+    # Process answers
     answers_payload = []
-    log = []
-
-    for q in questions_data:
+    logs = []
+    for q in questions:
         task_id = q.get("task_id")
-        question = q.get("question")
-        if not task_id or not question:
+        question_text = q.get("question")
+        if not task_id or not question_text:
             continue
         try:
-            answer = agent(question)
+            answer = agent(question_text)
         except Exception as e:
-            answer = f"[Error]: {e}"
-        answers_payload.append({"task_id": task_id, "submitted_answer": answer})
-        log.append({"Task ID": task_id, "Question": question, "Submitted Answer": answer})
+            answer = f"AGENT ERROR: {e}"
 
-    if not answers_payload:
-        return "No answers submitted.", pd.DataFrame(log)
+        answers_payload.append({"task_id": task_id, "submitted_answer": answer})
+        logs.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": answer})
+        print(f"✅ Task ID: {task_id} | Answer: {answer}")
 
+    # Submit answers
     submission = {
-        "username": profile_username,
+        "username": username,
         "agent_code": agent_code,
-        "answers": answers_payload
+        "answers": answers_payload,
     }
 
     try:
-        res = requests.post(submit_url, json=submission, timeout=30)
+        res = requests.post(submit_url, json=submission, timeout=60)
         res.raise_for_status()
         result = res.json()
-        msg = (
-            f"✅ Submission Successful!\n"
-            f"User: {result.get('username')}\n"
-            f"Score: {result.get('score')}%\n"
-            f"Correct: {result.get('correct_count')}/{result.get('total_attempted')}\n"
-            f"Message: {result.get('message')}"
+        status = (
+            f"🎉 Submission Successful!\n"
+            f"User: {result.get('username', 'N/A')}\n"
+            f"Score: {result.get('score', 0)}%\n"
+            f"Correct: {result.get('correct_count', 0)}/{result.get('total_attempted', 0)}\n"
+            f"Message: {result.get('message', '')}"
         )
-        return msg, pd.DataFrame(log)
     except Exception as e:
-        return f"❌ Submission Failed: {e}", pd.DataFrame(log)
+        status = f"❌ Submission failed: {e}"
 
-with gr.Blocks() as demo:
-    gr.Markdown("# 🔍 GAIA Agent Evaluator")
-    gr.Markdown("Log in, then click the button to evaluate your agent.")
-    run_button = gr.Button("▶️ Run Evaluation & Submit All Answers")
-    status = gr.Textbox(label="Status", lines=4)
-    table = gr.DataFrame(label="Agent Logs")
+    return status, pd.DataFrame(logs)
 
-    run_button.click(fn=run_and_submit_all, outputs=[status, table])
+# --- Gradio UI ---
+with gr.Blocks() as demo:
+    gr.Markdown("# 🤖 GAIA Evaluation Agent")
+    gr.Markdown("""
+    1. Log in with your Hugging Face account below.
+    2. Click the button to evaluate and submit answers.
+    3. Your score and submission details will appear below.
+    """)
+
+    profile_input = gr.OAuthProfile()
+    run_button = gr.Button("🚀 Run Evaluation & Submit All Answers")
+    status_output = gr.Textbox(label="Submission Result", lines=6, interactive=False)
+    results_table = gr.DataFrame(label="Answer Log")
+
+    run_button.click(
+        fn=run_and_submit_all,
+        inputs=[profile_input],
+        outputs=[status_output, results_table]
+    )
 
 if __name__ == "__main__":
-    print("Launching GAIA App...")
-    demo.launch()
+    print("\n================== GAIA Agent App Starting ==================")
+    if os.getenv("SPACE_ID"):
+        print(f"📦 SPACE_ID = {os.getenv('SPACE_ID')}")
+    else:
+        print("ℹ️ No SPACE_ID found. Using fallback link.")
+
+    demo.launch(debug=True)