Update app.py

app.py

@@ -2,11 +2,19 @@ import gradio as gr
 import pandas as pd
 from datetime import datetime
 import os
+from huggingface_hub import HfApi, HfFolder, Repository
+import os
+HF_TOKEN = os.getenv("HF_TOKEN")
 
-# Path for local storage on Hugging Face Space
+# Constants for Hugging Face
+REPO_ID = "Abu1998/DataCollection"  # Replace with your dataset repository name
 STORAGE_PATH = "appointments.csv"
 
-# Check if the storage file exists; if not, create it
+# Authenticate with Hugging Face Hub
+api = HfApi()
+HfFolder.save_token(HF_TOKEN)
+
+# Ensure the CSV file exists
 if not os.path.exists(STORAGE_PATH):
     df = pd.DataFrame(columns=[
         "Date", "Appointment", "Appointment Timing", "Services", "Products",
@@ -14,16 +22,28 @@ if not os.path.exists(STORAGE_PATH):
     ])
     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):
+# Function to upload CSV to Hugging Face Hub
+def upload_to_huggingface():
+    repo = Repository(local_dir="temp_repo", clone_from=REPO_ID, use_auth_token=HF_TOKEN)
+    repo.git_pull()  # Ensure the latest version
+    os.makedirs("temp_repo", exist_ok=True)
+    df = pd.read_csv(STORAGE_PATH)
+    df.to_csv(os.path.join("temp_repo", "appointments.csv"), index=False)
+    repo.git_add("appointments.csv")
+    repo.git_commit("Updated appointments data")
+    repo.git_push()
+    return "Data uploaded to Hugging Face successfully!"
+
+# Function to save 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):
     # Load existing data
     df = pd.read_csv(STORAGE_PATH)
-    
-    # Auto-detect current date and appointment number
+
+    # Auto-detect date and appointment ID
     date = datetime.now().strftime("%Y-%m-%d")
-    appointment = len(df) + 1  # Generate appointment ID
-    
-    # Create a new row as a DataFrame
+    appointment = len(df) + 1
+
+    # Add a new row
     new_row = pd.DataFrame([{
         "Date": date,
         "Appointment": appointment,
@@ -37,17 +57,17 @@ def save_to_csv(appointment_timing, services, products, contact, customer_name,
         "Key-points": key_points,
         "Price": price
     }])
-    
-    # Concatenate the new row with the existing DataFrame
+
+    # Append and save locally
     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}!"
+    df.to_csv(STORAGE_PATH, index=False)
 
-# Function to provide the download file
-def get_csv_file():
-    return STORAGE_PATH
+    # Upload to Hugging Face
+    upload_to_huggingface()
 
-# Define Gradio interface
+    return f"Appointment {appointment} saved and uploaded successfully!"
+
+# Gradio Interface
 with gr.Blocks() as app:
     gr.Markdown("# Appointment Data Storage Application")
     appointment_timing = gr.Textbox(label="Appointment Timing")
@@ -65,18 +85,14 @@ with gr.Blocks() as app:
     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,
+        save_and_upload_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
+# Launch the app
 app.launch(share=True)