app.py

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")  #created using sentence_transformers on kaggle
metadata = pd.read_csv("netflix_metadata.csv") #created using sentence_transformers on kaggle

# 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()