Update app.py

app.py

@@ -5,20 +5,13 @@ import pandas as pd
 import math
 
 from smolagents import ToolCallingAgent, tool
-from instructor import OpenAIChat
+from smolagents.models import OpenAIServerModel
 from duckduckgo_search import DDGS
 
 # --- Tools ---
 @tool
 def web_search(query: str) -> str:
-    """Performs a web search using DuckDuckGo.
-
-    Args:
-        query: The search query string.
-
-    Returns:
-        A formatted string with search results.
-    """
+    """Performs a web search using DuckDuckGo."""
     try:
         with DDGS() as ddgs:
             results = ddgs.text(query, max_results=3)
@@ -27,114 +20,76 @@ def web_search(query: str) -> str:
                 for r in results
             ) if results else "No results found."
     except Exception as e:
-        return f"Search error: {str(e)}"
+        return f"Search error: {e}"
 
 @tool
 def calculate(expression: str) -> str:
-    """Evaluates mathematical expressions.
-
-    Args:
-        expression: The math expression to evaluate.
-
-    Returns:
-        The result as a string or error message.
-    """
+    """Evaluates mathematical expressions."""
     try:
         safe_dict = {k: v for k, v in math.__dict__.items() if not k.startswith("__")}
-        safe_dict.update({
-            '__builtins__': None,
-            'abs': abs,
-            'round': round
-        })
+        safe_dict.update({'abs': abs, 'round': round})
         result = eval(expression, {"__builtins__": None}, safe_dict)
         return str(result)
     except Exception as e:
-        return f"Calculation error: {str(e)}"
+        return f"Calculation error: {e}"
 
 # --- Agent ---
 class GAIAAgent:
     def __init__(self):
-        self.system_prompt = """You are an AI assistant that answers questions using tools:
-- Use web_search for factual queries
-- Use calculate for math problems
-- Be concise and accurate"""
-
-        try:
-            self.agent = ToolCallingAgent(
-                name="GAIA_Agent",
-                description=self.system_prompt,
-                tools=[web_search, calculate],
-                model=OpenAIChat(model="gpt-3.5-turbo"),  # ✅ Fixed model wrapper
-                planning_interval=3
-            )
-            print("✅ Agent initialized successfully")
-        except Exception as e:
-            raise RuntimeError(f"Agent init failed: {str(e)}")
+        model = OpenAIServerModel(model_id="gpt-3.5-turbo", api_key=os.getenv("OPENAI_API_KEY"))
+        self.agent = ToolCallingAgent(
+            name="GAIA_Agent",
+            description="AI assistant using web_search and calculate tools.",
+            tools=[web_search, calculate],
+            model=model,
+        )
 
     def __call__(self, question: str) -> str:
         try:
-            response = self.agent.run(question)
-            return str(response) if response else "No answer generated"
+            return self.agent.run(
+                question,
+                planning_interval=3,
+                system_prompt=(
+                    "You are an AI assistant. Use web_search for factual queries "
+                    "and calculate for math problems. Be concise and accurate."
+                )
+            )
         except Exception as e:
-            return f"Error: {str(e)}"
+            return f"Error: {e}"
 
-# --- Gradio Submission Logic ---
+# --- Gradio UI + Submission ---
 def submit_answers(profile: gr.OAuthProfile | None):
     if not profile:
         return "Please login to Hugging Face", None
 
-    try:
-        agent = GAIAAgent()
-        response = requests.get("https://agents-course-unit4-scoring.hf.space/questions", timeout=20)
-        questions = response.json() or []
-
-        answers = []
-        results = []
-
-        for item in questions[:15]:  # Limit to 15 for speed
-            task_id = item.get("task_id", "")
-            question = item.get("question", "")
-            answer = agent(question)
+    agent = GAIAAgent()
+    resp = requests.get("https://agents-course-unit4-scoring.hf.space/questions", timeout=20)
+    questions = resp.json() or []
 
-            answers.append({
-                "task_id": task_id,
-                "submitted_answer": answer[:1000]
-            })
-            results.append({
-                "Task ID": task_id,
-                "Question": question[:100],
-                "Answer": answer[:200]
-            })
+    answers, rows = [], []
+    for item in questions[:15]:
+        tid, q = item.get("task_id"), item.get("question")
+        ans = agent(q)
+        answers.append({"task_id": tid, "submitted_answer": ans[:1000]})
+        rows.append({"Task ID": tid, "Question": q[:100], "Answer": ans[:200]})
 
-        submit_response = requests.post(
-            "https://agents-course-unit4-scoring.hf.space/submit",
-            json={
-                "username": profile.username,
-                "agent_code": f"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}",
-                "answers": answers
-            },
-            timeout=60
-        )
-        data = submit_response.json()
-
-        return (
-            f"Submitted {len(answers)} answers\n"
-            f"Score: {data.get('score', 'N/A')}%\n"
-            f"Correct: {data.get('correct_count', 0)}/{len(answers)}",
-            pd.DataFrame(results)
-        )
+    post = requests.post("https://agents-course-unit4-scoring.hf.space/submit", json={
+        "username": profile.username,
+        "agent_code": f"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}",
+        "answers": answers
+    }, timeout=60)
+    data = post.json()
 
-    except Exception as e:
-        return f"Error: {str(e)}", None
+    out = f"Submitted {len(answers)} answers\nScore: {data.get('score', 'N/A')}%\nCorrect: {data.get('correct_count',0)}/{len(answers)}"
+    return out, pd.DataFrame(rows)
 
-# --- Gradio Interface ---
 with gr.Blocks() as demo:
-    gr.Markdown("# GAIA Agent with DuckDuckGo + Calculator")
+    gr.Markdown("# GAIA Agent")
     gr.LoginButton()
     btn = gr.Button("Run Evaluation", variant="primary")
     status = gr.Textbox(label="Results")
-    df = gr.DataFrame(label="Evaluation Details")
+    df = gr.DataFrame(label="Details")
     btn.click(submit_answers, outputs=[status, df])
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()