Update app.py

app.py

@@ -1,49 +1,40 @@
 import os
-from dotenv import load_dotenv
 import gradio as gr
 import requests
-import time
+import inspect
 import pandas as pd
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel, VisitWebpageTool
 
-from state_graph import compiled_graph
-from langfuse.callback import CallbackHandler
+from dotenv import load_dotenv
 
-# env variables test
 load_dotenv()
 
-os.environ["LANGFUSE_PUBLIC_KEY"] = os.getenv("LANGFUSE_PUBLIC_KEY")
-os.environ["LANGFUSE_SECRET_KEY"] = os.getenv("LANGFUSE_SECRET_KEY")
+
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-
-
 class BasicAgent:
     def __init__(self):
-        self.langfuse_handler = CallbackHandler()
         print("BasicAgent initialized.")
-
-    def __call__(self, q) -> str:
-        print(f"Agent received question (first 50 chars): {q['question'][:50]}...")
-        fixed_answer = compiled_graph.invoke(
-                        input={
-                            "task_id": q['task_id'],
-                            "question": q['question'],
-                            "level": q['Level'],
-                            "file_name": q['file_name'],
-                            "question_category": None,
-                            "context": None,
-                            "expected_answer": None,
-                            "messages": []
-                        },
-                        config={"callbacks": [self.langfuse_handler]}
-                    )
-        print(f"Agent returning fixed answer: {fixed_answer['expected_answer']}")
-        return fixed_answer['expected_answer']
-
+        model = HfApiModel(
+                "Qwen/Qwen2.5-72B-Instruct",
+                provider="together", # Choose a specific inference provider
+                max_tokens=4096,
+                temperature=0.1
+            )
+        self.agent = CodeAgent(
+            tools=[DuckDuckGoSearchTool(), VisitWebpageTool()], 
+            model=model,
+            additional_authorized_imports=["pandas", "numpy","re"])
+        
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        fixed_answer = self.agent.run(question)
+        print(f"Agent returning fixed answer: {fixed_answer}")
+        return fixed_answer
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -51,7 +42,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     and displays the results.
     """
     # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID")  # Get the SPACE_ID for sending link to the code
+    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
 
     if profile:
         username= f"{profile.username}"
@@ -70,8 +61,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase
-    # ( useful for others so please keep it public)
+    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)
 
@@ -101,14 +91,13 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     answers_payload = []
     print(f"Running agent on {len(questions_data)} questions...")
     for item in questions_data:
-        time.sleep(10)
         task_id = item.get("task_id")
         question_text = item.get("question")
         if not task_id or question_text is None:
             print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
-            submitted_answer = agent(item)
+            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:
@@ -173,16 +162,14 @@ with gr.Blocks() as demo:
     gr.Markdown("# Basic Agent Evaluation Runner")
     gr.Markdown(
         """
-    **Instructions:**
-        
-    1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
-    2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-    3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-    
+        **Instructions:**
+        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
+        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
+        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
         ---
-    **Disclaimers:**
-    Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-    This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
+        **Disclaimers:**
+        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
+        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
         """
     )
 
@@ -203,7 +190,7 @@ if __name__ == "__main__":
     print("\n" + "-"*30 + " App Starting " + "-"*30)
     # Check for SPACE_HOST and SPACE_ID at startup for information
     space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID")  # Get SPACE_ID at startup
+    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
 
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")