Update app.py

app.py

@@ -1,132 +1,110 @@
+import os
 import subprocess
 import sys
 
-# Force install the missing dependency at runtime
 try:
     import duckduckgo_search
 except ImportError:
     subprocess.check_call([sys.executable, "-m", "pip", "install", "duckduckgo-search==6.3.2"])
 
-import os
 import gradio as gr
 import requests
 import pandas as pd
-from smolagents import CodeAgent, LiteLLMModel
+from smolagents import CodeAgent, HfApiModel
+
+try:
+    from smolagents import DuckDuckGoSearchTool
+    HAS_SEARCH = True
+except ImportError:
+    HAS_SEARCH = False
 
-# --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 class BasicAgent:
     def __init__(self):
-        # Using LiteLLM to connect to Qwen
-        self.model = LiteLLMModel(model_id="huggingface/Qwen/Qwen2.5-72B-Instruct")
-        
-        # Initialize search tool
-        search_tool = DuckDuckGoSearchTool()
+        self.model = HfApiModel(model_id="Qwen/Qwen2.5-72B-Instruct")
         
+        tools = []
+        if HAS_SEARCH:
+            try:
+                tools.append(DuckDuckGoSearchTool())
+            except Exception as e:
+                print(f"Could not init search tool: {e}")
+
         self.agent = CodeAgent(
-            tools=[search_tool], 
+            tools=tools, 
             model=self.model,
-            add_base_tools=True 
+            add_base_tools=True # Python interpreter for math/logic
         )
-        print("Advanced smolagent initialized with LiteLLM.")
+        print(f"Agent initialized. Search available: {HAS_SEARCH}")
 
     def __call__(self, question: str) -> str:
         clean_prompt = (
             f"Question: {question}\n\n"
             "Instructions: Solve the question above. Provide ONLY the final answer "
-            "value without any explanation, units, or extra text. "
-            "Do not include the phrase 'FINAL ANSWER' in your response."
+            "value. No explanation, no 'The answer is...', just the value."
         )
-        
         try:
             result = self.agent.run(clean_prompt)
             return str(result).strip()
         except Exception as e:
-            print(f"Error during agent execution: {e}")
-            return "Error solving question"
+            return f"Error: {e}"
 
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     space_id = os.getenv("SPACE_ID")
-
     if not profile:
         return "Please Login to Hugging Face with the button.", None
 
     username = f"{profile.username}"
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-    
-    # Define agent_code link correctly
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "https://huggingface.co/spaces"
+    # Define agent code link
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
 
-    # 1. Instantiate Agent
+    # 1. Fetch Questions
     try:
-        agent = BasicAgent()
-    except Exception as e:
-        return f"Error initializing agent: {e}", None
-
-    # 2. Fetch Questions
-    try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
+        response = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
         questions_data = response.json()
     except Exception as e:
         return f"Error fetching questions: {e}", None
 
-    # 3. Run Agent on Questions
-    results_log = []
+    # 2. Run Agent
+    agent = BasicAgent()
     answers_payload = []
+    results_log = []
     
-    # LIMITING TO FIRST 5 FOR TESTING - Remove [:5] to do all
+    # Process all questions
     for item in questions_data:
         task_id = item.get("task_id")
         question_text = item.get("question")
-        
-        if not task_id or question_text is None:
-            continue
+        if not task_id: continue
 
-        try:
-            submitted_answer = agent(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})
-        except Exception as e:
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"ERROR: {e}"})
+        submitted_answer = agent(question_text)
+        answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
+        results_log.append({"Task ID": task_id, "Answer": submitted_answer})
 
-    # 4. Submit Results
+    # 3. Submit
     submission_data = {
-        "username": username.strip(), 
+        "username": username, 
         "agent_code": agent_code, 
         "answers": answers_payload
     }
 
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
+        response = requests.post(f"{DEFAULT_API_URL}/submit", json=submission_data, timeout=60)
         result_data = response.json()
-        
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Score: {result_data.get('score', 0)}% "
-            f"({result_data.get('correct_count', 0)}/{result_data.get('total_attempted', 0)} correct)"
-        )
-        return final_status, pd.DataFrame(results_log)
+        status = f"Success! Score: {result_data.get('score')}% ({result_data.get('correct_count')}/{result_data.get('total_attempted')})"
+        return 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("# GAIA Agent Evaluation Runner")
+    gr.Markdown("# GAIA Final Solver")
     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="Detailed Results", wrap=True)
-
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
+    run_button = gr.Button("Run Evaluation & Submit", variant="primary")
+    status_output = gr.Textbox(label="Status")
+    results_table = gr.DataFrame(label="Answers Generated")
+
+    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
 
 if __name__ == "__main__":
     demo.launch()