Update app.py

app.py

@@ -24,11 +24,34 @@ llm = HuggingFaceEndpoint(
         huggingfacehub_api_token=os.environ.get("HUGGINGFACEHUB_API_TOKEN"),
 )
 
+system_prompt = """You are an AI assistant taking the GAIA benchmark. You must output ONLY the final answer. Do not include any explanations, conversational text or formatting. If the answer is a number, output just the number. If it is a list, output a comma-separated list."""
 chat = ChatHuggingFace(llm=llm, verbose=True)
 
+# Tool Definition
 search_tool = DuckDuckGoSearchRun()
 
-tools = [search_tool]
+@tool
+def python_repl(code:str) -> str:
+    """
+    Executes Python code and returns the standard output.
+    Use this to read files (like CSVs or Excel), process data with pandas or do math.
+    CRITICAL: You MUST use print() to output the final result so it can be captured.
+    """
+
+    old_stdout = sys.stdout
+    redirected_output = sys.stdout = StringIO()
+
+    try: 
+        # Execute the code in the global namespace
+        exec(code, globals())
+        sys.stdout = old_stdoutoutput = redirected_output.getvalue()
+        return output if output else "Code executed successfully, but printed nothing. Use print() to see output."
+    except Exception as e:
+        sys.stdout = old_stdout
+        return f"Error executing code: {e}"
+
+# Tools Instantiation
+tools = [search_tool, python_repl]
 chat_with_tools = chat.bind_tools(tools)
 
 class AgentState(TypedDict):
@@ -39,11 +62,13 @@ def assistant(state:AgentState):
         "messages": [chat_with_tools.invoke(state["messages"])]
     }
 
-# Graph
+# Graph Instantiation
 builder = StateGraph(AgentState)
+
 # Graph Nodes
 builder.add_node("assistant", assistant)
 builder.add_node("tools", ToolNode(tools))
+
 #Graph Edges
 builder.add_edge(START, "assistant")
 builder.add_conditional_edges(
@@ -51,6 +76,8 @@ builder.add_conditional_edges(
     tools_condition
 )
 builder.add_edge("tools", "assistant")
+
+# Agent Compile
 my_agent = builder.compile()
 
 class BasicAgent:
@@ -58,8 +85,25 @@ class BasicAgent:
         print("BasicAgent initialized.")
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        messages = [HumanMessage(content=question)]
+        
+        # 1. Construct the prompt with the file path if it exists
+        if file_name:
+            # Note: You may need to adjust the path depending on where your space saves downloaded files.
+            # Usually, the grading space provides the file name, and it sits in the same directory.
+            prompt = f"Question: {question}\nAttached File: {file_name}\n\nUse the python_repl tool to read and analyze this file using pandas."
+        else: 
+            prompt = f"Question: {question}"
+
+        # Inject the strict GAIA System Prompt and the Human Prompt
+        messages = [
+            SystemMessage(content=system_prompt),
+            HumanMessage(content=question)
+        ]
+
+        # Invoke Agent
         response_state = my_agent.invoke({"messages": messages})
+
+        # Final answer
         final_answer = response_state["messages"][-1].content
         return final_answer
 
@@ -120,11 +164,13 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     for item in questions_data:
         task_id = item.get("task_id")
         question_text = item.get("question")
+        file_name = item.get("file_name") 
+        
         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(question_text)
+            submitted_answer = agent(question_text, file_name)
             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: