Create app.py

app.py

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+import pandas as pd
+import numpy as np
+import sklearn
+from sklearn.metrics.pairwise import cosine_similarity
+
+# Load movie metadata into a pandas DataFrame
+movie_data = pd.read_csv('/content/drive/MyDrive/kaggle/movies_metadata.csv', usecols=['title', 'budget', 'revenue', 'vote_count', 'vote_average'])
+
+# Load movie ratings into a pandas DataFrame
+ratings_data = pd.read_csv('/content/drive/MyDrive/kaggle/ratings_small.csv')
+
+# Collaborative Filtering
+def collaborative_filtering(user_id, num_recommendations=5):
+    user_ratings = ratings_data[ratings_data['userId'] == user_id]
+    user_ratings = user_ratings.merge(movie_data[['title']], left_on='movieId', right_index=True)
+    user_ratings = user_ratings.drop_duplicates(subset=['title'], keep='first')
+
+    # Get the mean ratings for each movie
+    movie_ratings_mean = ratings_data.groupby('movieId')['rating'].mean().reset_index()
+
+    # Merge with movie data to get movie titles
+    movie_ratings_mean = movie_ratings_mean.merge(movie_data[['title']], left_on='movieId', right_index=True)
+    movie_ratings_mean = movie_ratings_mean.drop_duplicates(subset=['title'], keep='first')
+
+    # Sort movies by mean rating in descending order
+    top_movies = movie_ratings_mean.sort_values(by='rating', ascending=False)['title'][:num_recommendations]
+
+    return top_movies.tolist()
+import gradio as gr
+
+with gr.Blocks() as demo:
+    gr.Markdown("Flip text or image files using this demo.")
+    title="Movie Recommender System"
+    user_id = gr.Textbox(placeholder='user_id')
+
+    #input2 = gr.Textbox(placeholder='second film name...')
+
+    #input3 = gr.Textbox(placeholder='third film name...')
+
+    text_output=gr.Textbox( )
+
+
+    text_button = gr.Button("Submit")
+
+    text_button.click(collaborative_filtering, inputs=user_id, outputs=text_output)
+demo.launch()