huggingface database

app.py

@@ -1,14 +1,18 @@
 import base64
 import gradio as gr
 import pandas as pd
+from datasets import Dataset, DatasetDict
 
-# Load the dataset
-data = pd.read_csv('MovieDatabase.csv')
+# Load the dataset from Hugging Face Datasets
+data = Dataset.from_dataset('moizmoizmoizmoiz/MovieRatingDB')
+
+# Convert dataset to a pandas DataFrame for easier manipulation
+data_df = data.to_pandas()
 
 # Add a default column for ratings if not already present
 for profile in ['moiz', 'udisha', 'musab']:
-    if profile not in data.columns:
-        data[profile] = ""  # Default rating is empty
+    if profile not in data_df.columns:
+        data_df[profile] = ""  # Default rating is empty
 
 # Save the current index and selected profile
 current_index = [0]
@@ -28,13 +32,13 @@ def display_movie(action, profile):
     current_profile[0] = profile
 
     # Update the index based on action
-    if action == "next" and current_index[0] < len(data) - 1:
+    if action == "next" and current_index[0] < len(data_df) - 1:
         current_index[0] += 1
     elif action == "prev" and current_index[0] > 0:
         current_index[0] -= 1
 
     # Extract movie details
-    movie = data.iloc[current_index[0]]
+    movie = data_df.iloc[current_index[0]]
     
     # Get the IMDb ID from the 'id' column
     movie_id = movie.get('id', 'Unknown')  # Use 'Unknown' if 'id' doesn't exist
@@ -70,7 +74,7 @@ def display_movie(action, profile):
         **Box Office:** {movie['BoxOffice']}
         """,
         "current_rating": f"Your Rating: {movie[profile]}",
-        "index_display": f'<span class="index-display">{current_index[0] + 1}/{len(data)}</span>'  # Class applied for index
+        "index_display": f'<span class="index-display">{current_index[0] + 1}/{len(data_df)}</span>'  # Class applied for index
     }
     return details["title"], details["poster_placeholder"], details["ratings"], details["details"], details["current_rating"], details["index_display"]
 
@@ -78,10 +82,11 @@ def submit_rating(rating):
     # Update the rating for the current profile and movie
     movie_index = current_index[0]
     profile = current_profile[0]
-    data.at[movie_index, profile] = rating
+    data_df.at[movie_index, profile] = rating
 
-    # Save the changes to the CSV
-    data.to_csv('MovieDatabase.csv', index=False)
+    # Save the changes to the dataset
+    updated_data = Dataset.from_pandas(data_df)
+    updated_data.push_to_hub("moizmoizmoizmoiz/MovieRatingDB")
 
     return display_movie("stay", profile)
 
@@ -89,10 +94,11 @@ def not_watched():
     # Mark the movie as "N/W" for the current profile
     movie_index = current_index[0]
     profile = current_profile[0]
-    data.at[movie_index, profile] = "N/W"
+    data_df.at[movie_index, profile] = "N/W"
 
-    # Save the changes to the CSV
-    data.to_csv('MovieDatabase.csv', index=False)
+    # Save the changes to the dataset
+    updated_data = Dataset.from_pandas(data_df)
+    updated_data.push_to_hub("moizmoizmoizmoiz/MovieRatingDB")
 
     return display_movie("stay", profile)