Update app.py

app.py

@@ -1,128 +1,110 @@
 import os
 import gradio as gr
-import requests
 import pandas as pd
-import openai
-
-# Load OpenAI API Key from environment
-openai.api_key = os.getenv("OPENAI_API_KEY")
+import requests
 
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Improved Smart Agent ---
-class SmartAgent:
+# --- OpenAI-based Agent ---
+class OpenAIAgent:
     def __init__(self):
-        print("SmartAgent initialized.")
+        import openai
+        self.openai = openai
+        self.api_key = os.getenv("OPENAI_API_KEY")
+        if not self.api_key:
+            raise ValueError("OPENAI_API_KEY environment variable is not set.")
+        self.openai.api_key = self.api_key
+        self.model = "gpt-3.5-turbo"  # Use free-accessible model
 
     def __call__(self, question: str) -> str:
-        print(f"[Agent] Processing question: {question[:60]}...")
         try:
-            response = openai.ChatCompletion.create(
-                model="gpt-4",
-                messages=[
-                    {
-                        "role": "system",
-                        "content": (
-                            "You are a highly logical AI assistant specialized in answering "
-                            "knowledge and reasoning questions precisely and concisely. "
-                            "Provide only the final answer, with no extra commentary or labels."
-                        )
-                    },
-                    {"role": "user", "content": question}
-                ],
-                temperature=0.2,  # Lower temp for more deterministic answers
-                max_tokens=300,
-                top_p=1,
-                frequency_penalty=0,
-                presence_penalty=0,
+            prompt = f"""You are a helpful, knowledgeable AI answering a challenging question. Only return the answer, no explanation or preamble.\n\nQuestion:\n{question}"""
+            response = self.openai.ChatCompletion.create(
+                model=self.model,
+                messages=[{"role": "user", "content": prompt}],
+                temperature=0.2,
+                max_tokens=400
             )
-            answer = response.choices[0].message["content"].strip()
-            print(f"[Agent] Answer: {answer}")
-            return answer
+            return response.choices[0].message["content"].strip()
         except Exception as e:
-            print(f"[Agent Error]: {e}")
+            print("[OpenAI Error]:", e)
             return "Error: Could not generate answer."
 
-# --- Submission Logic ---
+# --- Dummy Agent for Broken Envs ---
+class DummyAgent:
+    def __call__(self, question: str) -> str:
+        return "Error: Could not generate answer."
+
+# --- Main Evaluation Function ---
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     space_id = os.getenv("SPACE_ID", "your_username/your_space_name")
-
-    if profile:
-        username = profile.username
-        print(f"User logged in: {username}")
-    else:
+    if not profile:
         return "❌ Please log in to Hugging Face first.", None
 
+    username = profile.username
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    agent = SmartAgent()
 
+    # Choose agent
+    try:
+        agent = OpenAIAgent()
+    except Exception as e:
+        print("Using dummy agent due to error:", e)
+        agent = DummyAgent()
+
+    # Fetch questions
     try:
         response = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
         response.raise_for_status()
-        questions = response.json()
+        questions_data = response.json()
     except Exception as e:
         return f"❌ Failed to fetch questions: {e}", None
 
-    results_log = []
+    # Generate answers
     answers_payload = []
-
-    for item in questions:
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        if not task_id or not question_text:
+    results_log = []
+    for q in questions_data:
+        task_id = q.get("task_id")
+        question = q.get("question")
+        if not task_id or not question:
             continue
-        try:
-            answer = agent(question_text)
-            answers_payload.append({"task_id": task_id, "submitted_answer": answer})
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": answer})
-        except Exception as e:
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"Error: {e}"})
+        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})
 
-    submission = {
-        "username": username,
-        "agent_code": agent_code,
-        "answers": answers_payload
-    }
+    if not answers_payload:
+        return "❌ No answers were generated by the agent.", pd.DataFrame(results_log)
 
+    # Submit answers
     try:
-        resp = requests.post(f"{DEFAULT_API_URL}/submit", json=submission, timeout=60)
-        resp.raise_for_status()
-        result = resp.json()
-        score = result.get("score", "N/A")
-        correct = result.get("correct_count", "?")
-        total = result.get("total_attempted", "?")
-
-        msg = f"✅ Submission Successful!\nUser: {username}\nScore: {score}% ({correct}/{total} correct)"
+        submission = {
+            "username": username,
+            "agent_code": agent_code,
+            "answers": answers_payload
+        }
+        post_resp = requests.post(f"{DEFAULT_API_URL}/submit", json=submission, timeout=60)
+        post_resp.raise_for_status()
+        result = post_resp.json()
+
+        msg = (
+            f"✅ Submission Successful!\n"
+            f"User: {username}\n"
+            f"Score: {result.get('score', 'N/A')}% "
+            f"({result.get('correct_count', '?')}/{result.get('total_attempted', '?')} correct)"
+        )
         return msg, pd.DataFrame(results_log)
     except Exception as e:
         return f"❌ Submission failed: {e}", pd.DataFrame(results_log)
 
 # --- Gradio UI ---
 with gr.Blocks() as demo:
-    gr.Markdown("## 🤖 GAIA Agent Submission Tool")
+    gr.Markdown("## 🤖 GAIA Benchmark Agent Submission")
     gr.Markdown("Click below to log in and run your agent on the GAIA benchmark.")
     gr.LoginButton()
     run_btn = gr.Button("Run Evaluation & Submit All Answers")
     status = gr.Textbox(label="Status", lines=5)
-    table = gr.DataFrame(label="Results")
+    table = gr.DataFrame(label="Agent Answers")
 
     run_btn.click(fn=run_and_submit_all, outputs=[status, table])
 
-# --- Optional local test for debugging ---
-def local_test():
-    print("Running local test...")
-    agent = SmartAgent()
-    test_questions = [
-        "How many studio albums did Mercedes Sosa publish between 2000 and 2009?",
-        "What is the capital city of France?",
-        "List the vegetables in this list: milk, eggs, flour, whole bean coffee, Oreos, sweet potatoes, fresh basil, plums, green beans, rice, corn, bell pepper, whole allspice, acorns, broccoli, celery, zucchini, lettuce, peanuts."
-    ]
-    for q in test_questions:
-        ans = agent(q)
-        print(f"Q: {q}\nA: {ans}\n{'-'*40}")
-
 if __name__ == "__main__":
-    print("Launching GAIA Agent App")
-    local_test()
     demo.launch()
-