Update app.py

app.py

@@ -2,7 +2,8 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
-from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel, VisitWebpageTool
+# Use LiteLLMModel instead of HfApiModel to avoid the import error
+from smolagents import CodeAgent, DuckDuckGoSearchTool, LiteLLMModel, VisitWebpageTool
 
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
@@ -14,31 +15,25 @@ class AgentArchitect:
         hf_token = os.getenv("HF_TOKEN")
         
         if not hf_token:
-            print("Warning: HF_TOKEN is missing. Please add it to your Space Secrets.")
+            print("Warning: HF_TOKEN is missing in Space Secrets.")
 
-        # We use HfApiModel to stay on the free 'Serverless Inference' tier.
-        # Qwen 2.5 Coder 32B is highly capable of writing the Python logic 
-        # needed for botany sorting and Excel math.
-        self.model = HfApiModel(
-            model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
-            token=hf_token
+        # LiteLLMModel is the more robust class in the latest smolagents
+        # We add the 'huggingface/' prefix to tell it exactly where to go
+        self.model = LiteLLMModel(
+            model_id="huggingface/Qwen/Qwen2.5-Coder-32B-Instruct",
+            api_key=hf_token
         )
         
-        # Tools are the agent's 'hands'
-        # DuckDuckGo for search, VisitWebpage for reading deep into sites
         self.tools = [DuckDuckGoSearchTool(), VisitWebpageTool()]
         
-        # CodeAgent is the 'Brain' - it can write Python code to solve problems
         self.agent = CodeAgent(
             tools=self.tools,
             model=self.model,
-            add_base_tools=True  # Provides built-in math and basic logic tools
+            add_base_tools=True 
         )
 
     def __call__(self, question: str) -> str:
         try:
-            # We enforce conciseness. The GAIA benchmark grades on 'Exact Match'.
-            # If the answer is 'India' and the agent says 'The winner is India', it fails.
             prompt = (
                 f"{question}\n\n"
                 f"Instructions: Think step-by-step. Use tools if needed. "
@@ -47,7 +42,6 @@ class AgentArchitect:
             result = self.agent.run(prompt)
             return str(result)
         except Exception as e:
-            print(f"Agent Runtime Error: {e}")
             return f"Error: {e}"
 
 def run_and_submit_all(profile: gr.OAuthProfile | None):
@@ -62,11 +56,9 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     submit_url = f"{api_url}/submit"
 
     try:
-        # Initialize the Agent
         agent_instance = AgentArchitect()
         
-        # 1. Fetch Questions from the course server
-        print("Fetching questions...")
+        # 1. Fetch Questions
         response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
         questions_data = response.json()
@@ -74,19 +66,17 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         results_log = []
         answers_payload = []
         
-        # 2. Loop through questions and generate answers
-        # This will take 10-15 minutes to finish all 20.
+        # 2. Run Agent
         for item in questions_data:
             task_id = item.get("task_id")
             question_text = item.get("question")
             
-            print(f"Processing Task: {task_id}")
             submitted_answer = agent_instance(question_text)
             
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
 
-        # 3. Submit results back for scoring
+        # 3. Submit
         agent_code_link = f"https://huggingface.co/spaces/{space_id}/tree/main"
         submission_data = {
             "username": username.strip(), 
@@ -94,7 +84,6 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
             "answers": answers_payload
         }
         
-        print("Submitting answers...")
         submit_response = requests.post(submit_url, json=submission_data, timeout=60)
         submit_response.raise_for_status()
         result_data = submit_response.json()
@@ -113,18 +102,12 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
 # --- Gradio UI ---
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("# 🚀 Professional Agent Evaluator")
-    gr.Markdown("Click 'Login' first, then click the 'Run' button. This may take up to 15 minutes.")
-    
     gr.LoginButton()
     run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
-    
     status_output = gr.Textbox(label="Status", lines=4)
     results_table = gr.DataFrame(label="Agent Reasoning Trace", wrap=True)
 
-    run_button.click(
-        fn=run_and_submit_all, 
-        outputs=[status_output, results_table]
-    )
+    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
 
 if __name__ == "__main__":
     demo.launch()