Create app.py

app.py

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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("# Local Agent Runner & Saver")
+    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)