Update app.py

app.py

@@ -1,82 +1,8 @@
-import gradio as gr
-import pandas as pd
-from datetime import datetime
-import os
+from datasets import load_dataset
 
-# Path for local storage on Hugging Face Space
-STORAGE_PATH = "appointments.csv"
+# Load the dataset from your Hugging Face Space
+dataset = load_dataset("Abu1998/DataCollection/", split="train")
 
-# Check if the storage file exists; if not, create it
-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)
-
-# Function to save data to CSV
-def save_to_csv(appointment_timing, services, products, contact, customer_name, rating, location, key_points, price):
-    # Load existing data
-    df = pd.read_csv(STORAGE_PATH)
-    
-    # Auto-detect current date and appointment number
-    date = datetime.now().strftime("%Y-%m-%d")
-    appointment = len(df) + 1  # Generate appointment ID
-    
-    # Create a new row as a DataFrame
-    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
-    }])
-    
-    # Concatenate the new row with the existing DataFrame
-    df = pd.concat([df, new_row], ignore_index=True)
-    df.to_csv(STORAGE_PATH, index=False)  # Save the updated DataFrame to the CSV file
-    return f"Data saved successfully for Appointment {appointment}!"
-
-# Function to provide the download file
-def get_csv_file():
-    return STORAGE_PATH
-
-# Define 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")
-    download_button = gr.File(label="Download Appointments Data", value=STORAGE_PATH)
-
-    submit_button.click(
-        save_to_csv,
-        inputs=[
-            appointment_timing, services, products, contact, customer_name, rating, location, key_points, price
-        ],
-        outputs=output
-    )
-
-    # Add a file download button
-    download_button.render()
-
-# Launch the application
-app.launch(share=True)
+# Convert to pandas DataFrame and save locally
+df = dataset.to_pandas()
+df.to_csv("appointments.csv", index=False)