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import os
import gradio as gr
import requests
import inspect
import pandas as pd
import tempfile
from dotenv import load_dotenv
from typing import Optional
# Load environment variables
load_dotenv()
# Import your LangGraph agent
from graph.graph_builder import graph
from langchain_core.messages import HumanMessage
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
# --- File Download Helper Function ---
def download_file(task_id: str, api_url: str) -> Optional[str]:
"""Download file associated with a task_id from the evaluation API"""
try:
file_url = f"{api_url}/files/{task_id}"
print(f"๐ Downloading file for task {task_id}")
response = requests.get(file_url, timeout=30)
response.raise_for_status()
# Get filename from headers or create one
content_disposition = response.headers.get('Content-Disposition', '')
if 'filename=' in content_disposition:
filename = content_disposition.split('filename=')[1].strip('"')
else:
content_type = response.headers.get('Content-Type', '')
if 'image' in content_type:
extension = '.jpg'
elif 'audio' in content_type:
extension = '.mp3'
elif 'video' in content_type:
extension = '.mp4'
else:
extension = '.txt'
filename = f"task_{task_id}_file{extension}"
# Save to temporary file
temp_dir = tempfile.gettempdir()
file_path = os.path.join(temp_dir, filename)
with open(file_path, 'wb') as f:
f.write(response.content)
print(f"โ
File downloaded: {file_path}")
return file_path
except Exception as e:
print(f"โ Error downloading file for task {task_id}: {e}")
return None
# --- Your LangGraph Agent Definition ---
class BasicAgent:
def __init__(self):
"""Initialize the LangGraph agent"""
print("LangGraph Agent initialized with multimodal, search, math, and YouTube tools.")
if not os.getenv("OPENROUTER_API_KEY"):
raise ValueError("OPENROUTER_API_KEY not found in environment variables")
self.graph = graph
print("โ
Agent ready with tools: multimodal, search, math, YouTube")
def __call__(self, question: str, file_path: Optional[str] = None) -> str:
"""
Process a question using the LangGraph agent and return just the answer
Args:
question: The question to answer
file_path: Optional path to associated file (image, audio, etc.)
Returns:
str: The final answer (formatted for evaluation)
"""
print(f"๐ค Processing question: {question[:50]}...")
try:
# CRITICAL: Only modify the prompt if there's actually a valid file
if file_path and os.path.exists(file_path):
enhanced_question = f"{question}\n\nFile provided: {file_path}"
print(f"๐ Processing with file: {file_path}")
else:
# Keep EXACTLY the same behavior as before for non-file questions
enhanced_question = question
# Create initial state with the question (enhanced or original)
initial_state = {"messages": [HumanMessage(content=enhanced_question)]}
# Run the LangGraph agent
result = self.graph.invoke(initial_state)
# Extract the final message content
final_message = result["messages"][-1]
answer = final_message.content
# Clean up the answer for evaluation (UNCHANGED from original)
if isinstance(answer, str):
answer = answer.strip()
prefixes_to_remove = [
"The answer is: ",
"Answer: ",
"The result is: ",
"Result: ",
"The final answer is: ",
]
for prefix in prefixes_to_remove:
if answer.startswith(prefix):
answer = answer[len(prefix):].strip()
break
print(f"โ
Agent answer: {answer}")
return answer
except Exception as e:
error_msg = f"Error processing question: {str(e)}"
print(f"โ {error_msg}")
return error_msg
def run_and_submit_all(profile: gr.OAuthProfile | None):
"""
Fetches all questions, downloads associated files, runs the BasicAgent on them,
submits all answers, and displays the results.
"""
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 = BasicAgent()
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"
print(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 requests.exceptions.RequestException as e:
print(f"Error fetching questions: {e}")
return f"Error fetching questions: {e}", None
except Exception as e:
print(f"An unexpected error occurred fetching questions: {e}")
return f"An unexpected error occurred fetching questions: {e}", None
# 3. Process Questions (with minimal changes)
results_log = []
answers_payload = []
downloaded_files = [] # Track files for cleanup
print(f"Running agent on {len(questions_data)} questions...")
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
# Download file if it exists
downloaded_file_path = None
if file_name:
print(f"๐ Task {task_id} has file: {file_name}")
downloaded_file_path = download_file(task_id, api_url)
if downloaded_file_path:
downloaded_files.append(downloaded_file_path) # Track for cleanup
try:
# Call agent (behavior unchanged for non-file questions)
submitted_answer = agent(question_text, downloaded_file_path)
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:
print(f"โ Error running agent on task {task_id}: {e}")
results_log.append({
"Task ID": task_id,
"Question": question_text,
"Submitted Answer": f"AGENT ERROR: {e}"
})
# Cleanup downloaded files AFTER all processing is complete
for file_path in downloaded_files:
try:
if os.path.exists(file_path):
os.remove(file_path)
print(f"๐งน Cleaned up: {file_path}")
except Exception as e:
print(f"โ ๏ธ Could not clean up {file_path}: {e}")
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 (UNCHANGED)
submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
print(status_update)
# 5. Submit (UNCHANGED)
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 requests.exceptions.HTTPError as e:
error_detail = f"Server responded with status {e.response.status_code}."
try:
error_json = e.response.json()
error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
except requests.exceptions.JSONDecodeError:
error_detail += f" Response: {e.response.text[:500]}"
status_message = f"Submission Failed: {error_detail}"
print(status_message)
results_df = pd.DataFrame(results_log)
return status_message, results_df
except requests.exceptions.Timeout:
status_message = "Submission Failed: The request timed out."
print(status_message)
results_df = pd.DataFrame(results_log)
return status_message, results_df
except requests.exceptions.RequestException as e:
status_message = f"Submission Failed: Network error - {e}"
print(status_message)
results_df = pd.DataFrame(results_log)
return status_message, results_df
except Exception as e:
status_message = f"An unexpected error occurred during submission: {e}"
print(status_message)
results_df = pd.DataFrame(results_log)
return status_message, results_df
# --- Build Gradio Interface (UNCHANGED) ---
with gr.Blocks() as demo:
gr.Markdown("# LangGraph Agent Evaluation Runner")
gr.Markdown(
"""
**Instructions:**
This space uses a LangGraph agent with multimodal, search, math, and YouTube tools powered by OpenRouter.
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.
**Agent Capabilities:**
- ๐จ **Multimodal**: Analyze images, extract text (OCR), process audio transcripts
- ๐ **Search**: Web search using multiple providers (DuckDuckGo, Tavily, SerpAPI)
- ๐งฎ **Math**: Basic arithmetic, complex calculations, percentages, factorials
- ๐บ **YouTube**: Extract captions, get video information
---
**Note:** Processing all questions may take some time as the agent carefully analyzes each question and uses appropriate tools.
"""
)
gr.LoginButton()
run_button = gr.Button("Run Evaluation & Submit All Answers")
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 + " App Starting " + "-"*30)
space_host_startup = os.getenv("SPACE_HOST")
space_id_startup = os.getenv("SPACE_ID")
if space_host_startup:
print(f"โ
SPACE_HOST found: {space_host_startup}")
print(f" Runtime URL should be: https://{space_host_startup}.hf.space")
else:
print("โน๏ธ SPACE_HOST environment variable not found (running locally?).")
if space_id_startup:
print(f"โ
SPACE_ID found: {space_id_startup}")
print(f" Repo URL: https://huggingface.co/spaces/{space_id_startup}")
print(f" Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
else:
print("โน๏ธ SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
print("-"*(60 + len(" App Starting ")) + "\n")
print("Launching Gradio Interface for LangGraph Agent Evaluation...")
demo.launch(debug=True, share=True)
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