demo init

app.py

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+import gradio as gr
+import random
+from datasets import load_dataset
+
+
+# # Sample dataset with unique 10-digit IDs
+# qa_dataset = {
+#     "1234567890": {
+#         "question": "What is the capital of France?",
+#         "choices": ["A. Berlin", "B. Madrid", "C. Paris", "D. Lisbon"],
+#         "answer": "C. Paris"
+#     },
+#     "0987654321": {
+#         "question": "What is the largest planet in our solar system?",
+#         "choices": ["A. Earth", "B. Jupiter", "C. Saturn", "D. Mars", "E. Venus"],
+#         "answer": "B. Jupiter"
+#     },
+#     # Add more questions with unique IDs as needed
+# }
+
+truth_data = load_dataset("commonsense-index-dev/commonsense-candidates", "iter6-0520-error", split="train")
+
+qa_dataset = {}
+for item in truth_data:
+    qa_dataset[item["id"]] = {
+        "question": item["task"],
+        "choices": item["choices"],
+        "answer": item["answer"]
+    }
+
+def get_random_question():
+    question_id = random.choice(list(qa_dataset.keys()))
+    question_data = qa_dataset[question_id]
+    return question_id, question_data["question"], question_data["choices"]
+
+def get_question_by_id(question_id):
+    if question_id in qa_dataset:
+        question_data = qa_dataset[question_id]
+        return question_id, question_data["question"], question_data["choices"]
+    else:
+        return None, "Invalid question ID", []
+
+def check_answer(question_id, choice):
+    correct_answer = qa_dataset[question_id]["answer"]
+    return "Correct!" if choice == correct_answer else f"Incorrect. The correct answer is {correct_answer}."
+
+def load_question(question_id=None):
+    if question_id:
+        question_id, question, choices = get_question_by_id(question_id)
+    else:
+        question_id, question, choices = get_random_question()
+    
+    question = f"## {question}"
+    choices_markdown = "\n".join(choices)
+    return question_id, question, choices_markdown, gr.update(visible=True), gr.update(value="", visible=True)
+
+def show_buttons(choices_markdown):
+    choices = choices_markdown.split("\n")
+    visibility = [gr.update(visible=False)] * 10
+    for i in range(len(choices)):
+        visibility[i] = gr.update(visible=True, value=choices[i])
+    return visibility
+
+with gr.Blocks() as app:
+    gr.Markdown("# Multiple Choice QA Dataset Viewer")
+
+    question_id_input = gr.Textbox(label="Enter Question ID", placeholder="leave empty for random sampling")
+    random_button = gr.Button("Retrieve or Random Sample")
+    question_display = gr.Markdown(visible=True)
+    choices_markdown = gr.Markdown(visible=False)
+    choice_buttons = [gr.Button(visible=False) for _ in range(10)]
+    result_display = gr.Markdown(visible=True)
+
+    question_id = gr.State()
+
+    question_id_input.submit(fn=load_question, inputs=question_id_input, outputs=[question_id, question_display, choices_markdown, result_display])
+    random_button.click(fn=load_question, outputs=[question_id, question_display, choices_markdown, result_display])
+    choices_markdown.change(fn=show_buttons, inputs=choices_markdown, outputs=choice_buttons)
+
+    for i, button in enumerate(choice_buttons):
+        button.click(fn=check_answer, inputs=[question_id, button], outputs=result_display)
+
+app.launch()