Create app.py

app.py

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+#!pip install sqlite3 # Install the sqlite3 library if not already installed
+import sqlite3
+
+# Define the database file path
+DB_FILE = "./reviews.db"
+# Connect to the SQLite database
+db = sqlite3.connect(DB_FILE)
+
+# Attempt to create the 'reviews' table if it doesn't exist
+try:
+    # Try to select all rows from the 'reviews' table
+    db.execute("SELECT * FROM reviews").fetchall()
+    # Close the database connection if the table exists
+    db.close()
+except sqlite3.OperationalError:
+    # If the table doesn't exist, create it
+    db.execute(
+        '''
+        CREATE TABLE reviews (id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,
+                              created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP NOT NULL,
+                              name TEXT, review INTEGER, comments TEXT)
+                                                                                    '''
+    )
+    # Commit the changes to the database
+    db.commit()
+    # Close the database connection
+    db.close()
+
+# Function to retrieve the latest reviews from the database
+def get_latest_reviews(db: sqlite3.Connection):
+    # Execute a query to get the 10 most recent reviews, ordered by id descending
+    reviews = db.execute("SELECT * FROM reviews ORDER BY id DESC limit 10").fetchall()
+    # Get the total number of reviews in the database
+    total_reviews = db.execute("Select COUNT(id) from reviews").fetchone()[0]
+    # Convert the reviews to a pandas DataFrame for easier handling
+    reviews = pd.DataFrame(
+        reviews,
+        columns=["id", "date_created", "name", "review", "comments"]
+    )
+    # Return the reviews DataFrame and the total number of reviews
+    return reviews, total_reviews
+
+# Function to add a new review to the database
+def add_review(name: str, review: int, comments: str):
+    # Connect to the database
+    db = sqlite3.connect(DB_FILE)
+    # Create a cursor object to execute SQL commands
+    cursor = db.cursor()
+    # Insert the new review into the database
+    cursor.execute("INSERT INTO reviews(name, review, comments) VALUES(?,?,?)",
+     [name, review, comments])
+    # Commit the changes to the database
+    db.commit()
+    # Retrieve the updated list of reviews and total count
+    reviews, total_reviews = get_latest_reviews(db)
+    # Close the database connection
+    db.close()
+    # Return the updated reviews and total count
+    return reviews, total_reviews
+
+def load_data():
+    db = sqlite3.connect(DB_FILE)
+    reviews, total_reviews = get_latest_reviews(db)
+    db.close()
+    return reviews, total_reviews
+
+import gradio as gr
+
+# Create a Gradio Blocks interface
+with gr.Blocks() as demo:
+    # Create a row to organize elements horizontally
+    with gr.Row():
+        # Create a column for input elements
+        with gr.Column():
+             # Create a text input for the user's name
+             name = gr.Textbox(label="Name", placeholder="What is your name?")
+             # Create a radio button group for rating satisfaction
+             review = gr.Radio(label="How satisfied are you with using gradio?",
+                               choices=[1, 2, 3, 4, 5])
+             # Create a multi-line text input for comments
+             comments = gr.Textbox(
+                 label="Comments",
+                 lines=10,
+                 placeholder="Do you have any feedback on gradio?"
+             )
+             # Create a submit button
+             submit = gr.Button(value="Submit Feedback")
+        # Create a column for output elements
+        with gr.Column():
+            # Create a dataframe to display the most recent 10 reviews
+            data = gr.Dataframe(label="Most recently created 10 rows")
+            # Create a number display for the total review count
+            count = gr.Number(label="Total number of reviews")
+    # Define the action when the submit button is clicked
+    submit.click(add_review,
+     [name, review, comments],
+     [data, count])
+    # Define the action when the demo is loaded
+    demo.load(load_data, None, [data, count])
+
+from google.colab import userdata
+TOKEN = userdata.get('token')
+
+# Retrieve the Hugging Face Hub token from environment variables
+TOKEN = TOKEN 
+
+# Create a Repository object for interacting with a Hugging Face dataset
+repo = huggingface_hub.Repository(
+    # Specify the local directory where the repository will be cloned
+    local_dir="data",
+    # Set the repository type to "dataset"
+    repo_type="dataset",
+    # Specify the name of the dataset to clone from Hugging Face Hub
+    clone_from="Kilos1/my-reviews",
+    # Use the authentication token for accessing the repository
+    use_auth_token=TOKEN
+)
+
+# Pull the latest changes from the remote repository
+repo.git_pull()
+
+# Check if the file exists in the expected location
+import os
+if os.path.exists("./data/reviews.db"):
+    # Copy the reviews database file from the cloned repository to the local DB_FILE location
+    shutil.copyfile("./data/reviews.db", DB_FILE)
+else:
+    print("File 'reviews.db' not found in the repository. Please check the file path.")
+    # If the file is not in the expected location, you may need to adjust the path
+    # based on its actual location in the repository.
+    # For example, if the file is in a subdirectory called 'database', you would use:
+    # shutil.copyfile("./data/database/reviews.db", DB_FILE)
+
+# Import the BackgroundScheduler from APScheduler library
+from apscheduler.schedulers.background import BackgroundScheduler
+import pandas as pd
+import datetime
+
+# Define a function to backup the database
+def backup_db():
+    # Copy the current database file to the data directory
+    shutil.copyfile(DB_FILE, "./data/reviews.db")
+
+    # Connect to the database
+    db = sqlite3.connect(DB_FILE)
+
+    # Fetch all reviews from the database
+    reviews = db.execute("SELECT * FROM reviews").fetchall()
+
+    # Convert the reviews to a pandas DataFrame and save as CSV
+    pd.DataFrame(reviews).to_csv("./data/reviews.csv", index=False)
+
+    # Print a message indicating the update is in progress
+    print("updating db")
+
+    # Push the updated data to the Hugging Face Hub
+    repo.push_to_hub(blocking=False,
+                    commit_message=f"Updating data at {datetime.datetime.now()}")
+
+# Create a BackgroundScheduler instance
+scheduler = BackgroundScheduler()
+
+# Add a job to run the backup_db function every 60 seconds
+scheduler.add_job(func=backup_db,
+                  trigger="interval",
+                  seconds=60)
+# Start the scheduler
+scheduler.start()
+
+demo.launch()