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| import numpy as np | |
| import pandas as pd | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| # Load embeddings and metadata | |
| embeddings = np.load("netflix_embeddings.npy") | |
| metadata = pd.read_csv("netflix_metadata.csv") | |
| # Vector search function | |
| def vector_search(query, model): | |
| query_embedding = model.encode(query) | |
| similarities = cosine_similarity([query_embedding], embeddings)[0] | |
| top_n = 3 | |
| top_indices = similarities.argsort()[-top_n:][::-1] | |
| results = metadata.iloc[top_indices] | |
| # Format results for display | |
| result_text = "\n".join(f"Title: {row['title']}, Description: {row['description']}, Genre: {row['listed_in']}" for _, row in results.iterrows()) | |
| return result_text | |
| # Gradio Interface | |
| import gradio as gr | |
| from sentence_transformers import SentenceTransformer | |
| model = SentenceTransformer("thenlper/gte-large") | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Netflix Recommendation System") | |
| gr.Markdown("Enter a query to receive Netflix show recommendations based on title, description, and genre.") | |
| query = gr.Textbox(label="Enter your query") | |
| output = gr.Textbox(label="Recommendations") | |
| submit_button = gr.Button("Submit") | |
| submit_button.click(fn=lambda q: vector_search(q, model), inputs=query, outputs=output) | |
| demo.launch() | |
| # import gradio as gr | |
| # # def greet(name): | |
| # # return "Hello " + name + "!!" | |
| # from sentence_transformers import SentenceTransformer | |
| # import numpy as np | |
| # from sklearn.metrics.pairwise import cosine_similarity | |
| # from datasets import load_dataset | |
| # # Load pre-trained SentenceTransformer model | |
| # embedding_model = SentenceTransformer("thenlper/gte-large") | |
| # # # Example dataset with genres (replace with your actual data) | |
| # # dataset = load_dataset("hugginglearners/netflix-shows") | |
| # # dataset = dataset.filter(lambda x: x['description'] is not None and x['listed_in'] is not None and x['title'] is not None) | |
| # # data = dataset['train'] # Accessing the 'train' split of the dataset | |
| # # # Convert the dataset to a list of dictionaries for easier indexing | |
| # # data_list = list[data] | |
| # # print(data_list) | |
| # # # Combine description and genre for embedding | |
| # # def combine_description_title_and_genre(description, listed_in, title): | |
| # # return f"{description} Genre: {listed_in} Title: {title}" | |
| # # # Generate embedding for the query | |
| # # def get_embedding(text): | |
| # # return embedding_model.encode(text) | |
| # # # Vector search function | |
| # # def vector_search(query): | |
| # # query_embedding = get_embedding(query) | |
| # # # Generate embeddings for the combined description and genre | |
| # # embeddings = np.array([get_embedding(combine_description_title_and_genre(item["description"], item["listed_in"],item["title"])) for item in data_list[0]]) | |
| # # # Calculate cosine similarity between the query and all embeddings | |
| # # similarities = cosine_similarity([query_embedding], embeddings) | |
| # # Load dataset (using the correct dataset identifier for your case) | |
| # dataset = load_dataset("hugginglearners/netflix-shows") | |
| # # Combine description and genre for embedding | |
| # def combine_description_title_and_genre(description, listed_in, title): | |
| # return f"{description} Genre: {listed_in} Title: {title}" | |
| # # Generate embedding for the query | |
| # def get_embedding(text): | |
| # return embedding_model.encode(text) | |
| # # Vector search function | |
| # def vector_search(query): | |
| # query_embedding = get_embedding(query) | |
| # # Function to generate embeddings for each item in the dataset | |
| # def generate_embeddings(example): | |
| # return { | |
| # 'embedding': get_embedding(combine_description_title_and_genre(example["description"], example["listed_in"], example["title"])) | |
| # } | |
| # # Generate embeddings for the dataset using map | |
| # embeddings_dataset = dataset["train"].map(generate_embeddings) | |
| # # Extract embeddings | |
| # embeddings = np.array([embedding['embedding'] for embedding in embeddings_dataset]) | |
| # # Calculate cosine similarity between the query and all embeddings | |
| # similarities = cosine_similarity([query_embedding], embeddings) | |
| # # # Adjust similarity scores based on ratings | |
| # # ratings = np.array([item["rating"] for item in data_list]) | |
| # # adjusted_similarities = similarities * ratings.reshape(-1, 1) | |
| # # Get top N most similar items (e.g., top 3) | |
| # top_n = 3 | |
| # top_indices = similarities[0].argsort()[-top_n:][::-1] # Get indices of the top N results | |
| # top_items = [dataset["train"][i] for i in top_indices] | |
| # # Format the output for display | |
| # search_result = "" | |
| # for item in top_items: | |
| # search_result += f"Title: {item['title']}, Description: {item['description']}, Genre: {item['listed_in']}\n" | |
| # return search_result | |
| # # Gradio Interface | |
| # def movie_search(query): | |
| # return vector_search(query) | |
| # with gr.Blocks() as demo: | |
| # gr.Markdown("# Netflix Recommendation System") | |
| # gr.Markdown("Enter a query to receive Netflix show recommendations based on title, description, and genre.") | |
| # query = gr.Textbox(label="Enter your query") | |
| # output = gr.Textbox(label="Recommendations") | |
| # submit_button = gr.Button("Submit") | |
| # submit_button.click(fn=movie_search, inputs=query, outputs=output) | |
| # demo.launch() | |
| # # iface = gr.Interface(fn=movie_search, | |
| # # inputs=gr.inputs.Textbox(label="Enter your query"), | |
| # # outputs="text", | |
| # # live=True, | |
| # # title="Netflix Recommendation System", | |
| # # description="Enter a query to get Netflix recommendations based on description and genre.") | |
| # # iface.launch() | |
| # # demo = gr.Interface(fn=greet, inputs="text", outputs="text") | |
| # # demo.launch() | |