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""" Enhanced Multi-LLM Agent Evaluation Runner - CORRECTED VERSION"""
import os
import gradio as gr
import requests
import pandas as pd
from langchain_core.messages import HumanMessage
from veryfinal import build_graph
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
# --- Enhanced Agent Definition ---
class EnhancedMultiLLMAgent:
"""A multi-provider LangGraph agent with proper response handling."""
def __init__(self):
print("Enhanced Multi-LLM Agent initialized.")
try:
self.graph = build_graph(provider="groq")
print("Multi-LLM Graph built successfully.")
except Exception as e:
print(f"Error building graph: {e}")
self.graph = None
def __call__(self, question: str) -> str:
print(f"Agent received question: {question[:100]}...")
if self.graph is None:
return "Error: Agent not properly initialized"
# Create complete state structure
state = {
"messages": [HumanMessage(content=question)],
"query": question, # Critical: this must match the question
"agent_type": "",
"final_answer": "",
"perf": {},
"agno_resp": ""
}
# Always provide the required config with thread_id
config = {"configurable": {"thread_id": f"eval_{hash(question)}"}}
try:
result = self.graph.invoke(state, config)
# CORRECTED: Proper response extraction
if isinstance(result, dict):
# First try to get final_answer from the state
if 'final_answer' in result and result['final_answer']:
answer = result['final_answer']
# Fallback to messages if final_answer is empty
elif 'messages' in result and result['messages']:
last_message = result['messages'][-1]
if hasattr(last_message, 'content'):
answer = last_message.content
else:
answer = str(last_message)
else:
answer = str(result)
else:
answer = str(result)
# Clean the answer
answer = answer.strip()
# CRITICAL FIX: Ensure we don't return the question as answer
if answer == question or answer.startswith(question):
return "Information not available"
# Extract final answer if present
if "FINAL ANSWER:" in answer:
answer = answer.split("FINAL ANSWER:")[-1].strip()
# Additional validation
if not answer or len(answer.strip()) == 0:
return "No answer generated"
return answer
except Exception as e:
error_msg = f"Error: {str(e)}"
print(error_msg)
return error_msg
def run_and_submit_all(profile: gr.OAuthProfile | None):
"""Fetch questions, run agent, and submit answers."""
space_id = os.getenv("SPACE_ID")
if profile:
username = f"{profile.username}"
print(f"User logged in: {username}")
else:
print("User not logged in.")
return "Please Login to Hugging Face with the button.", None
api_url = DEFAULT_API_URL
questions_url = f"{api_url}/questions"
submit_url = f"{api_url}/submit"
# 1. Instantiate Agent
try:
agent = EnhancedMultiLLMAgent()
if agent.graph is None:
return "Error: Failed to initialize agent properly", None
except Exception as e:
print(f"Error instantiating agent: {e}")
return f"Error initializing agent: {e}", None
agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "No space ID available"
print(f"Agent code URL: {agent_code}")
# 2. Fetch Questions
print(f"Fetching questions from: {questions_url}")
try:
response = requests.get(questions_url, timeout=15)
response.raise_for_status()
questions_data = response.json()
if not questions_data:
print("Fetched questions list is empty.")
return "Fetched questions list is empty or invalid format.", None
print(f"Fetched {len(questions_data)} questions.")
except Exception as e:
print(f"Error fetching questions: {e}")
return f"Error fetching questions: {e}", None
# 3. Run your Agent
results_log = []
answers_payload = []
print(f"Running Enhanced Multi-LLM agent on {len(questions_data)} questions...")
for i, item in enumerate(questions_data):
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
print(f"Processing question {i+1}/{len(questions_data)}: {task_id}")
try:
submitted_answer = agent(question_text)
# Additional validation to prevent question repetition
if submitted_answer == question_text or submitted_answer.startswith(question_text):
submitted_answer = "Information not available"
answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
results_log.append({
"Task ID": task_id,
"Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
"Submitted Answer": submitted_answer[:200] + "..." if len(submitted_answer) > 200 else submitted_answer
})
except Exception as e:
error_msg = f"AGENT ERROR: {e}"
print(f"Error running agent on task {task_id}: {e}")
answers_payload.append({"task_id": task_id, "submitted_answer": error_msg})
results_log.append({
"Task ID": task_id,
"Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
"Submitted Answer": error_msg
})
if not answers_payload:
print("Agent did not produce any answers to submit.")
return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
# 4. Prepare Submission
submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
status_update = f"Enhanced Multi-LLM Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
print(status_update)
# 5. Submit
print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
try:
response = requests.post(submit_url, json=submission_data, timeout=60)
response.raise_for_status()
result_data = response.json()
final_status = (
f"Submission Successful!\n"
f"User: {result_data.get('username')}\n"
f"Overall Score: {result_data.get('score', 'N/A')}% "
f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
f"Message: {result_data.get('message', 'No message received.')}"
)
print("Submission successful.")
results_df = pd.DataFrame(results_log)
return final_status, results_df
except Exception as e:
status_message = f"Submission Failed: {e}"
print(status_message)
results_df = pd.DataFrame(results_log)
return status_message, results_df
# --- Build Gradio Interface ---
with gr.Blocks() as demo:
gr.Markdown("# Enhanced Multi-LLM Agent - CORRECTED VERSION")
gr.Markdown(
"""
**Instructions:**
1. Log in to your Hugging Face account using the button below.
2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
**FIXES APPLIED:**
- βœ… Proper response extraction from graph state
- βœ… Prevention of question repetition as answer
- βœ… Enhanced prompt engineering for better responses
- βœ… Improved error handling and validation
- βœ… Search-enhanced processing for information retrieval
"""
)
gr.LoginButton()
run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
run_button.click(
fn=run_and_submit_all,
outputs=[status_output, results_table]
)
if __name__ == "__main__":
print("\n" + "-"*30 + " Enhanced Multi-LLM Agent CORRECTED Starting " + "-"*30)
demo.launch(debug=True, share=False)