Update app.py

app.py

@@ -2,78 +2,88 @@ import gradio as gr
 import pandas as pd
 from datetime import datetime
 import os
-from huggingface_hub import Repository
+from huggingface_hub import Repository, HfFolder
 
-# Constants
-HF_TOKEN = os.getenv("HF_TOKEN")  # Retrieve token from environment variables
-STORAGE_PATH = "appointments.csv"  # Local CSV file path
-REPO_ID = "/huggingface.co/datasets/Abu1998/DataCollection"  # Your Hugging Face dataset repo
+# Store your Hugging Face token securely (avoid hardcoding it in your app.py)
+HF_TOKEN = "your_huggingface_token_here"  # Make sure to replace this with your Hugging Face token
 
-# Ensure the CSV file exists locally
-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)
+# Save Hugging Face token to the Hugging Face folder for authentication
+HfFolder.save_token(HF_TOKEN)
 
-# Function to upload the CSV to Hugging Face Hub
+# Define constants for the dataset path and local storage
+STORAGE_PATH = "appointments.csv"
+REPO_ID = "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()  # Pull the latest changes
+        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 data locally and upload it
+# 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):
-    try:
-        # Load existing data
+    # 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
+    # Auto-detect date and appointment ID
+    date = datetime.now().strftime("%Y-%m-%d")
+    appointment = len(df) + 1
 
-        # Add a new row
-        new_row = pd.DataFrame([{
-            "Date": date,
-            "Appointment": appointment,
-            "Appointment Timing": appointment_timing,
-            "Services": ', '.join(services) if isinstance(services, list) else services,
-            "Products": products,
-            "Contact": contact,
-            "Customer Name": customer_name,
-            "Rating": rating,
-            "Location": location,
-            "Key-points": key_points,
-            "Price": price
-        }])
+    # 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 and save locally
-        df = pd.concat([df, new_row], ignore_index=True)
-        df.to_csv(STORAGE_PATH, index=False)
+    # 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 to Hugging Face
-        upload_message = upload_to_huggingface()
-        return f"Appointment {appointment} saved locally and uploaded! {upload_message}"
-    except Exception as e:
-        return f"Error saving data: {e}"
+    # 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.CheckboxGroup(
+    services = gr.Dropdown(
         label="Services", 
-        choices=["Full arm Rica", "Full leg", "Underarms", "Eyebrow", "Upper lips"]
+        choices=["Full arm Rica", "Full leg", "Underarms", "Eyebrow", "Upper lips"],
+        multiselect=True
     )
     products = gr.Textbox(label="Products")
     contact = gr.Textbox(label="Contact")