app.py

import gradio as gr
import torch
import pandas as pd
from ModelSimilarity import MovieSimilarityModel

# Assume the MovieSimilarityModel class is already defined above
csv_file_path = './Themes_And_Concepts.csv'
movie_similarity_model = MovieSimilarityModel(csv_file_path)

# Load precomputed embeddings if available
movie_similarity_model.load_tensor_list()

# Define a function to wrap the comparison
def get_top_5_similar_movies(input_themes, input_genres):
    # Directly use the input arrays
    input_sentence_embeddings, input_genres_embeddings = movie_similarity_model.encode_input_movie(input_themes, input_genres)
    
    # Get top 5 similar movies
    top_5_similar_movies = movie_similarity_model.compare_input_vs_dataset(input_sentence_embeddings, input_genres_embeddings)
    
    return top_5_similar_movies

# Define Gradio interface
input_themes = gr.List(headers=["Themes"], label="Input Movie Themes (array of strings)")
input_genres = gr.List(headers=["Genres"], label="Input Movie Genres (array of strings)")
output = gr.Dataframe(type="pandas", label="Top 5 Similar Movies")

interface = gr.Interface(fn=get_top_5_similar_movies, inputs=[input_themes, input_genres], outputs=output)

# Launch the Gradio interface
interface.launch()