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import os
import gradio as gr
import pandas as pd
import json
import inspect
# --- Basic Agent Definition ---
class BasicAgent:
def __init__(self):
print("BasicAgent initialized.")
def __call__(self, question: str) -> str:
print(f"Agent received question (first 50 chars): {question[:50]}...")
fixed_answer = "This is a default answer."
print(f"Agent returning fixed answer: {fixed_answer}")
return fixed_answer
# --- Main Function: run_and_save ---
def run_and_save(profile: gr.OAuthProfile | None, task_id: int):
"""
Loads questions.json, finds the question by task_id,
runs the agent, and appends the answer to result_log.json.
"""
# --- Authentication Check ---
if not profile:
print("User not logged in.")
return "Please Login to Hugging Face with the button.", None
username = profile.username
print(f"✅ User logged in: {username}")
# --- Instantiate Agent ---
try:
agent = BasicAgent()
except Exception as e:
return f"Error initializing agent: {e}", None
# --- Load Questions ---
questions_path = "questions.json"
if not os.path.exists(questions_path):
return f"❌ {questions_path} not found.", None
try:
with open(questions_path, "r", encoding="utf-8") as f:
questions_data = json.load(f)
except json.JSONDecodeError as e:
return f"Error decoding {questions_path}: {e}", None
if not isinstance(questions_data, list):
return "Invalid format: questions.json must contain a list.", None
# --- Find Question by Task ID ---
question_item = next((q for q in questions_data if q.get("task_id") == task_id), None)
if not question_item:
return f"No question found for task_id {task_id}.", None
question_text = question_item.get("question", "")
print(f"🟦 Running agent for task_id {task_id}...")
# --- Run Agent ---
try:
submitted_answer = agent(question_text)
result = {
"Task ID": task_id,
"Question": question_text,
"Submitted Answer": submitted_answer
}
except Exception as e:
result = {
"Task ID": task_id,
"Question": question_text,
"Submitted Answer": f"AGENT ERROR: {e}"
}
# --- Save to result_log.json ---
result_log_path = "result_log.json"
if os.path.exists(result_log_path):
try:
with open(result_log_path, "r", encoding="utf-8") as f:
result_log = json.load(f)
if not isinstance(result_log, list):
result_log = []
except Exception:
result_log = []
else:
result_log = []
result_log.append(result)
with open(result_log_path, "w", encoding="utf-8") as f:
json.dump(result_log, f, indent=4, ensure_ascii=False)
print(f"✅ Result saved to {result_log_path}")
results_df = pd.DataFrame([result])
return f"✅ Answer saved locally for task_id {task_id}", results_df
# --- Gradio Interface ---
with gr.Blocks() as demo:
gr.Markdown("# JUST ANOTHER AGENT")
gr.Markdown(
"""
**Instructions:**
1. Login to Hugging Face using the button below.
2. Enter the Task ID from your `questions.json`.
3. Click **Run Agent & Save Answer**.
4. The result will be appended to `result_log.json` for manual upload.
"""
)
profile = gr.LoginButton()
task_id_input = gr.Number(label="Enter Task ID", precision=0)
run_button = gr.Button("Run Agent & Save Answer")
status_output = gr.Textbox(label="Status", lines=3, interactive=False)
results_table = gr.DataFrame(label="Result Log (Latest Entry)")
run_button.click(
fn=run_and_save,
inputs=[profile, task_id_input],
outputs=[status_output, results_table]
)
if __name__ == "__main__":
print("Launching Gradio interface...")
demo.launch(debug=True, share=False)
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