Update app.py

app.py

@@ -5,42 +5,42 @@ import pandas as pd
 from typing import TypedDict, Annotated, Sequence
 import operator
 from langchain_core.messages import BaseMessage, HumanMessage
-from langchain.tools import tool
-from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain.agents import AgentExecutor
 from langchain_experimental.tools import PythonREPLTool
-from langchain_community.tools.youtube import YouTubeSearchTool
+from langchain_community.tools.Youtube import YouTubeSearchTool # THIS IS THE CORRECTED LINE
+from langchain_community.tools.tavily_search import TavilySearchResults
 from langchain_openai import ChatOpenAI
 from langgraph.graph import StateGraph, END
 from langgraph.prebuilt import ToolNode, tools_condition
 
 # --- Main Application Logic ---
 
-# Agentin muisti
+# Agent's memory
 class AgentState(TypedDict):
     messages: Annotated[Sequence[BaseMessage], operator.add]
 
-# Agentin rakentajafunktio
+# Agent builder function
 def create_langgraph_agent():
     print("Initializing Advanced LangGraph Agent...")
     
-    # 1. Kielimalli (GPT-4o on paras valinta)
+    # 1. Language Model (GPT-4o is the best choice)
     llm = ChatOpenAI(model="gpt-4o", temperature=0)
     
-    # 2. Työkalut: Lisätty PythonREPL ja YouTube
+    # 2. Tools: Tavily, PythonREPL, and YouTube
     tools = [TavilySearchResults(max_results=3), PythonREPLTool(), YouTubeSearchTool()]
     llm_with_tools = llm.bind_tools(tools)
     print("LLM and tools initialized.")
 
-    # 3. Agentin solmu (kutsuu kielimallia)
+    # 3. Agent node (calls the language model)
     def agent_node(state):
         print("Calling agent node...")
         return {"messages": [llm_with_tools.invoke(state["messages"])]}
     
-    # 4. Työkalusolmu
+    # 4. Tool node
     tool_node = ToolNode(tools)
     print("Graph nodes defined.")
 
-    # 5. Graafin määritys
+    # 5. Graph definition
     graph = StateGraph(AgentState)
     graph.add_node("agent", agent_node)
     graph.add_node("tools", tool_node)
@@ -48,32 +48,25 @@ def create_langgraph_agent():
     graph.add_conditional_edges("agent", tools_condition)
     graph.add_edge("tools", "agent")
 
-    # 6. Graafin kääntäminen ja turvarajan asettaminen
-    app = graph.compile(recursion_limit=15) # Nostetaan limiittiä monimutkaisempia tehtäviä varten
+    # 6. Compile graph and set safety limit
+    app = graph.compile(recursion_limit=15)
     print("LangGraph agent compiled and ready.")
     return app
 
-# Agentin suoritusfunktio
+# Agent execution function
 def run_agent(agent_executor, question: str) -> str:
     print(f"Agent received question: {question}")
     final_answer = ""
     try:
         response = agent_executor.invoke(
             {"messages": [HumanMessage(content=question)]},
-            # Lisätään konfiguraatio, joka kertoo mille solmulle aikaraja asetetaan
             config={"recursion_limit": 15}
         )
-        
-        # Otetaan vastaus viimeisestä viestistä
         raw_answer = response['messages'][-1].content
-        
-        # Yritetään poimia vain "FINAL ANSWER" -osa, jos se on olemassa
-        # Tämä parantaa vastausten tarkkuutta.
         if "FINAL ANSWER:" in raw_answer:
             final_answer = raw_answer.split("FINAL ANSWER:")[-1].strip()
         else:
             final_answer = raw_answer
-            
     except Exception as e:
         print(f"Error during agent execution: {e}")
         final_answer = f"Error: Agent failed to execute. {e}"
@@ -81,14 +74,13 @@ def run_agent(agent_executor, question: str) -> str:
     print(f"Agent returning answer: {final_answer}")
     return str(final_answer)
 
-# Evaluaation ajaminen (kuten ennenkin)
+# Evaluation runner
 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-avainten tarkistus
     if not os.getenv("TAVILY_API_KEY") or not os.getenv("OPENAI_API_KEY"):
          return "One or more API keys (TAVILY_API_KEY, OPENAI_API_KEY) are not set.", None
 
@@ -116,7 +108,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
     submit_url = f"https://agents-course-unit4-scoring.hf.space/submit"
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=240) # Pidennetty timeout
+        response = requests.post(submit_url, json=submission_data, timeout=240)
         response.raise_for_status()
         result_data = response.json()
         final_status = (
@@ -130,7 +122,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     except Exception as e:
         return f"Error during submission: {e}", pd.DataFrame(answers_payload)
 
-# Gradio-käyttöliittymä
+# Gradio Interface
 with gr.Blocks() as demo:
     gr.Markdown("# Agent Evaluation Runner (Advanced Tools)")
     gr.LoginButton()