app.py

import gradio as gr
import pandas as pd
from datetime import datetime
import os
from huggingface_hub import Repository, HfFolder

# Store your Hugging Face token securely (avoid hardcoding it in your app.py)
HF_TOKEN = os.getenv("HF_TOKEN")  # Retrieve token from environment variables

# Save Hugging Face token to the Hugging Face folder for authentication
HfFolder.save_token(HF_TOKEN)

# Define constants for the dataset path and local storage
STORAGE_PATH = "appointments.csv"
REPO_ID = "/huggingface.co/datasets/Abu1998/DataCollection"  # This is your dataset repository on Hugging Face

# Function to upload CSV to Hugging Face
def upload_to_huggingface():
    try:
        # Clone the dataset repository from Hugging Face
        repo = Repository(local_dir="temp_repo", clone_from=REPO_ID, use_auth_token=HF_TOKEN)
        repo.git_pull()  # Ensure the latest version
        
        # Make sure the local repository folder exists
        os.makedirs("temp_repo", exist_ok=True)
        
        # Load the data and save it in the temp repository directory
        df = pd.read_csv(STORAGE_PATH)
        df.to_csv(os.path.join("temp_repo", "appointments.csv"), index=False)
        
        # Commit and push the updated CSV file to Hugging Face
        repo.git_add("appointments.csv")
        repo.git_commit("Updated appointments data")
        repo.git_push()
        
        return "Data uploaded to Hugging Face successfully!"
    except Exception as e:
        return f"Error uploading data: {e}"

# Function to save appointment data locally and upload to Hugging Face
def save_and_upload_to_csv(appointment_timing, services, products, contact, customer_name, rating, location, key_points, price):
    # Check if the CSV exists, if not, create a new DataFrame
    if not os.path.exists(STORAGE_PATH):
        df = pd.DataFrame(columns=[
            "Date", "Appointment", "Appointment Timing", "Services", "Products",
            "Contact", "Customer Name", "Rating", "Location", "Key-points", "Price"
        ])
        df.to_csv(STORAGE_PATH, index=False)
    else:
        # Load the existing CSV data
        df = pd.read_csv(STORAGE_PATH)

    # Auto-detect date and appointment ID
    date = datetime.now().strftime("%Y-%m-%d")
    appointment = len(df) + 1

    # Add a new row of appointment data
    new_row = pd.DataFrame([{
        "Date": date,
        "Appointment": appointment,
        "Appointment Timing": appointment_timing,
        "Services": services,
        "Products": products,
        "Contact": contact,
        "Customer Name": customer_name,
        "Rating": rating,
        "Location": location,
        "Key-points": key_points,
        "Price": price
    }])

    # Append new data to the existing DataFrame
    df = pd.concat([df, new_row], ignore_index=True)
    df.to_csv(STORAGE_PATH, index=False)  # Save updated data locally

    # Upload the updated data to Hugging Face
    return upload_to_huggingface()

# Gradio Interface
with gr.Blocks() as app:
    gr.Markdown("# Appointment Data Storage Application")
    appointment_timing = gr.Textbox(label="Appointment Timing")
    services = gr.Dropdown(
        label="Services", 
        choices=["Full arm Rica", "Full leg", "Underarms", "Eyebrow", "Upper lips"],
        multiselect=True
    )
    products = gr.Textbox(label="Products")
    contact = gr.Textbox(label="Contact")
    customer_name = gr.Textbox(label="Customer Name")
    rating = gr.Radio(label="Rating", choices=["Very good", "Good", "Normal", "Bad", "Too bad"])
    location = gr.Textbox(label="Location")
    key_points = gr.Textbox(label="Key-points")
    price = gr.Dropdown(label="Price", choices=["999", "1499", "2499", "3499", "4499"])
    submit_button = gr.Button("Submit")
    output = gr.Textbox(label="Output")

    submit_button.click(
        save_and_upload_to_csv,
        inputs=[
            appointment_timing, services, products, contact, customer_name, rating, location, key_points, price
        ],
        outputs=output
    )

# Launch the app
app.launch(share=True)