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()