Update app.py

app.py

@@ -2,7 +2,6 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
-# 2026 standard: InferenceClientModel is the robust way to access free models
 from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel, VisitWebpageTool
 
 # --- Constants ---
@@ -11,35 +10,32 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # --- Agent Definition ---
 class AgentArchitect:
     def __init__(self):
-        # SECURE: Fetches the token from your Space Secrets
         hf_token = os.getenv("HF_TOKEN")
         
-        if not hf_token:
-            print("CRITICAL: HF_TOKEN is missing. Please add it to your Space Secrets!")
-
-        # InferenceClientModel is the 2026 gateway for free Serverless Inference.
-        # We use Qwen 2.5 Coder 32B because it has the highest reasoning-to-speed ratio.
+        # We use the 7B Coder model. It is almost certainly on the FREE Serverless tier.
+        # This avoids hitting the 'nscale' or other paid Inference Providers.
         self.model = InferenceClientModel(
-            model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
+            model_id="Qwen/Qwen2.5-Coder-7B-Instruct", 
             token=hf_token
         )
         
-        # This will now work perfectly because 'ddgs' is in your requirements.txt
+        # Essential tools for GAIA: Search + Deep Scrapping
         self.tools = [DuckDuckGoSearchTool(), VisitWebpageTool()]
         
+        # CodeAgent is the best scaffold for technical tasks
         self.agent = CodeAgent(
             tools=self.tools,
             model=self.model,
-            add_base_tools=True 
+            add_base_tools=True # Gives it Python for math/data processing
         )
 
     def __call__(self, question: str) -> str:
         try:
-            # We enforce a concise output format to satisfy the grader's 'Exact Match' logic.
+            # We add a strict formatting instruction to help with Exact Match scoring
             prompt = (
                 f"{question}\n\n"
-                f"Instructions: Think step-by-step. Solve the problem using your tools. "
-                f"Provide ONLY the final, concise answer."
+                "Final Answer Requirement: Use your tools to find the information. "
+                "Provide ONLY the final answer concisely (e.g., just the name, number, or list)."
             )
             result = self.agent.run(prompt)
             return str(result)
@@ -59,7 +55,6 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
 
     try:
         agent_instance = AgentArchitect()
-        
         response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
         questions_data = response.json()
@@ -67,22 +62,17 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         results_log = []
         answers_payload = []
         
+        # The agent will now process the questions using the free tier model
         for item in questions_data:
             task_id = item.get("task_id")
             question_text = item.get("question")
-            
-            # The agent now has the 'ddgs' muscles to perform the search
             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})
 
+        # Submission Logic
         agent_code_link = f"https://huggingface.co/spaces/{space_id}/tree/main"
-        submission_data = {
-            "username": username.strip(), 
-            "agent_code": agent_code_link, 
-            "answers": answers_payload
-        }
+        submission_data = {"username": username.strip(), "agent_code": agent_code_link, "answers": answers_payload}
         
         submit_response = requests.post(submit_url, json=submission_data, timeout=60)
         submit_response.raise_for_status()
@@ -91,20 +81,20 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         final_status = (
             f"Submission Successful!\n"
             f"User: {result_data.get('username')}\n"
-            f"Final Score: {result_data.get('score')}% \n"
+            f"Score: {result_data.get('score')}% \n"
             f"Message: {result_data.get('message')}"
         )
         return final_status, pd.DataFrame(results_log)
         
     except Exception as e:
-        return f"Submission Failed: {e}", None
+        return f"Error during run: {e}", None
 
 # --- Gradio UI ---
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🚀 Professional Agent Evaluator")
+    gr.Markdown("# 🚀 Professional Agent Evaluator (Free Tier Optimized)")
     gr.LoginButton()
     run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
-    status_output = gr.Textbox(label="Leaderboard Status", lines=4)
+    status_output = gr.Textbox(label="Submission Result", lines=5)
     results_table = gr.DataFrame(label="Agent Reasoning Trace", wrap=True)
 
     run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])