Update app.py

app.py

@@ -1,83 +1,21 @@
-# app.py (Gradio version with LangChain agent)
+# app.py
 
 import os
-import requests
+import asyncio
 import pandas as pd
+import requests
 import gradio as gr
-from typing import List
-
-from langchain.agents import initialize_agent, AgentType, Tool
-from langchain_community.tools import DuckDuckGoSearchRun
-from langchain_community.tools.wikipedia.tool import WikipediaQueryRun
-from langchain_experimental.tools.python.tool import PythonREPLTool
-from langchain_community.tools.youtube.search import YouTubeSearchTool
-from langchain_community.document_loaders import YoutubeLoader
-from langchain_openai import ChatOpenAI
-from langchain.tools import tool
-
-# --- LangChain LLM and Tools Setup --- #
-llm = ChatOpenAI(model="gpt-4o", temperature=0)
-
-@tool
-def get_yt_transcript(url: str) -> str:
-    loader = YoutubeLoader.from_youtube_url(url, add_video_info=False)
-    docs = loader.load()
-    return " ".join(doc.page_content for doc in docs)
-
-@tool
-def reverse_sentence_logic(sentence: str) -> str:
-    return sentence[::-1]
-
-@tool
-def commutativity_counterexample(_: str) -> str:
-    return "a, b, c"
-
-@tool
-def malko_winner(_: str) -> str:
-    return "Uroš"
+from agent import answer_question
 
-@tool
-def ray_actor_answer(_: str) -> str:
-    return "Filip"
-
-@tool
-def chess_position_hint(_: str) -> str:
-    return "Qd1+"
-
-@tool
-def default_award_number(_: str) -> str:
-    return "80NSSC21K1030"
-
-# Add your LangChain tools here
-langchain_tools: List[Tool] = [
-    DuckDuckGoSearchRun(),
-    WikipediaQueryRun(api_wrapper=None),
-    YouTubeSearchTool(),
-    Tool(name="youtube_transcript", func=get_yt_transcript, description="Transcribe YouTube video from URL"),
-    PythonREPLTool(),
-    reverse_sentence_logic,
-    commutativity_counterexample,
-    malko_winner,
-    ray_actor_answer,
-    chess_position_hint,
-    default_award_number,
-]
-
-agent = initialize_agent(tools=langchain_tools, llm=llm, agent=AgentType.OPENAI_MULTI_FUNCTIONS, verbose=False)
-
-# --- Hugging Face Evaluation Integration --- #
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-class LangChainAgent:
+class GAIALlamaAgent:
     def __init__(self):
-        print("LangChainAgent initialized.")
+        print("Initialized LlamaIndex Agent")
 
     def __call__(self, question: str) -> str:
-        print(f"Running agent on: {question[:60]}")
-        try:
-            return agent.run(question)
-        except Exception as e:
-            return f"[ERROR] {str(e)}"
+        print(f"Received question: {question[:60]}...")
+        return asyncio.run(answer_question(question))
 
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     space_id = os.getenv("SPACE_ID")
@@ -88,7 +26,6 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else ""
     api_url = DEFAULT_API_URL
 
-    # Fetch questions
     try:
         response = requests.get(f"{api_url}/questions", timeout=15)
         response.raise_for_status()
@@ -98,44 +35,45 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
 
     answers_payload = []
     results_log = []
-    bot = LangChainAgent()
+    agent = GAIALlamaAgent()
 
     for item in questions_data:
         q = item.get("question")
         task_id = item.get("task_id")
         try:
-            a = bot(q)
+            a = agent(q)
         except Exception as e:
             a = f"ERROR: {e}"
         answers_payload.append({"task_id": task_id, "submitted_answer": a})
         results_log.append({"Task ID": task_id, "Question": q, "Submitted Answer": a})
 
-    submission_data = {"username": username, "agent_code": agent_code, "answers": answers_payload}
+    submission_data = {
+        "username": username,
+        "agent_code": agent_code,
+        "answers": answers_payload
+    }
 
-    # Submit answers
     try:
         response = requests.post(f"{api_url}/submit", json=submission_data, timeout=60)
         response.raise_for_status()
         result_data = response.json()
-        final_status = (
-            f"Submission Successful!\n"
+        status = (
+            f"✅ Submission Successful!\n"
             f"User: {result_data.get('username')}\n"
             f"Score: {result_data.get('score')}%\n"
             f"Correct: {result_data.get('correct_count')}/{result_data.get('total_attempted')}\n"
             f"Message: {result_data.get('message')}"
         )
-        return final_status, pd.DataFrame(results_log)
+        return status, pd.DataFrame(results_log)
     except Exception as e:
         return f"Submission failed: {e}", pd.DataFrame(results_log)
 
-# --- Gradio UI --- #
 with gr.Blocks() as demo:
-    gr.Markdown("# LangChain GAIA Agent – Evaluation Portal")
+    gr.Markdown("# LlamaIndex GAIA Agent – Evaluation Portal")
     gr.LoginButton()
     run_btn = gr.Button("Run Evaluation & Submit All Answers")
     status_box = gr.Textbox(label="Status", lines=5)
     result_table = gr.DataFrame(label="Agent Answers")
-
     run_btn.click(fn=run_and_submit_all, outputs=[status_box, result_table])
 
 demo.launch(debug=True)