start

app.py

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+import gradio as gr
+from datasets import load_dataset, Dataset, concatenate_datasets
+from datetime import datetime
+import requests
+import os
+
+# Load your private Hugging Face dataset
+DATASET_NAME = "andito/technical_interview_internship_2025"
+TOKEN = os.environ.get("HF_TOKEN")
+dataset = load_dataset(DATASET_NAME, split="train")
+
+# Function to log download data to the HF Dataset
+def log_to_hf_dataset(username):
+    if not username:
+        return "Please enter your username to proceed.", None
+
+    # Get current timestamp
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+    # Append new data to the dataset
+    new_entry = Dataset.from_dict({
+        "username": [username],
+        "timestamp": [timestamp],
+    })
+    updated_dataset = concatenate_datasets([dataset, new_entry])
+    updated_dataset.push_to_hub(DATASET_NAME, token=TOKEN)
+
+    # Provide file for download
+    return "Thank you! Your download is ready.", "exercise.pdf"  # Replace with your file path
+
+# Gradio interface
+with gr.Blocks() as demo:
+    username = gr.Textbox(label="Enter your username", placeholder="Your Hugging Face username")
+    download_button = gr.Button("Download Exercise")
+    output = gr.Text()
+    file = gr.File()
+    
+    download_button.click(log_to_hf_dataset, inputs=[username], outputs=[output, file])
+
+# Launch the app
+demo.launch()
+