Update app.py

app.py

@@ -1,84 +1,8 @@
-import os
-import gradio as gr
-import requests
-import pandas as pd
-from smolagents import CodeAgent, DuckDuckGoSearchTool, LiteLLMModel
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
 
-def create_agent():
-    # Try smaller free model
-    model = LiteLLMModel(model_id="huggingface/HuggingFaceH4/zephyr-7b-beta")
-    search_tool = DuckDuckGoSearchTool()
-    agent = CodeAgent(
-        tools=[search_tool],
-        model=model,
-        additional_authorized_imports=["requests", "pandas", "numpy", "time", "re", "math"]
-    )
-    return agent
+# 1. Define the tool (the agent's "eyes")
+search_tool = DuckDuckGoSearchTool()
 
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    space_id = os.getenv("SPACE_ID")
-    
-    if profile:
-        username = f"{profile.username}"
-    else:
-        return "Please Login to Hugging Face with the button.", None
-    
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-    
-    # 1. Instantiate the real Agent
-    try:
-        smol_agent = create_agent()
-    except Exception as e:
-        return f"Error initializing agent: {e}", None
-    
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    
-    # 2. Fetch Questions
-    try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-    except Exception as e:
-        return f"Error fetching questions: {e}", None
-    
-    # 3. Run your Agent
-    results_log = []
-    answers_payload = []
-    
-    for item in questions_data:
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        
-        try:
-            # Here the agent actually "thinks" and returns a result
-            submitted_answer = smol_agent.run(question_text)
-            answers_payload.append({"task_id": task_id, "submitted_answer": str(submitted_answer)})
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": str(submitted_answer)})
-        except Exception as e:
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"ERROR: {e}"})
-    
-    # 4. Submit
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    
-    try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = f"Submission Successful!\nScore: {result_data.get('score')}%"
-        return final_status, pd.DataFrame(results_log)
-    except Exception as e:
-        return f"Submission Failed: {e}", pd.DataFrame(results_log)
-
-# --- Gradio Interface ---
-with gr.Blocks() as demo:
-    gr.Markdown("# 🤖 My Custom Agent Runner")
-    gr.LoginButton()
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-    status_output = gr.Textbox(label="Status", lines=5)
-    results_table = gr.DataFrame(label="Results", wrap=True)
-    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
-
-if __name__ == "__main__":
-    demo.launch()
+# 2. Define the model (the agent's "brain")
+# We use a standard open model so you don't need a paid API key
+model = HfApiModel()