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| import pandas as pd | |
| import numpy as np | |
| import joblib | |
| import streamlit as st | |
| cosine_sim=joblib.load('model_movie_recomadation_sigmoid.joblib') | |
| indices_df=pd.read_csv('indices.csv') | |
| html_temp = """ | |
| <div style="background-color:tomato;padding:10px"> | |
| <h2 style="color:white;text-align:center;">Movie Recomandation APP</h2> | |
| </div> | |
| """ | |
| st.markdown(html_temp, unsafe_allow_html=True) | |
| image_url="https://tse1.mm.bing.net/th?id=OIP.T1nYWZh17oT5wuISslNzdwHaEK&pid=Api&P=0&h=180" | |
| st.image(image_url, use_container_width=True) | |
| st.markdown(f""" | |
| <style> | |
| /* Set the background image for the entire app */ | |
| .stApp {{ | |
| background-color:#6793AC; | |
| background-size: 100px; | |
| background-repeat:no; | |
| background-attachment: auto; | |
| background-position:full; | |
| }} | |
| </style> | |
| """, unsafe_allow_html=True) | |
| def get_recommendations(title, cosine_sim=cosine_sim): | |
| # Get the index of the movie that matches the title | |
| filtered_df = indices_df[indices_df.apply(lambda row: row.astype(str).str.contains(movie, case=False).any(), axis=1)].index | |
| sim_scores = list(enumerate(cosine_sim[filtered_df])) | |
| index, values = sim_scores[0] | |
| result = list(zip(range(len(values)), values)) | |
| # Sort the movies based on the similarity scores | |
| result_sorted = sorted(result, key=lambda x: x[1], reverse=True) | |
| # Get the scores of the 10 most similar movies | |
| sim_scores = result_sorted[1:11] | |
| # Get the movie indices | |
| ##global movie_indices | |
| movie_indices = [i[0] for i in sim_scores] | |
| #Return the top 10 most similar movies | |
| return movie_indices | |
| movie=st.text_input("Enter the Movie Tittle") | |
| if st.button("Search"): | |
| val=get_recommendations(movie) | |
| st.dataframe(indices_df.iloc[val]) |