app.py

import gradio as gr
import torch, numpy as np, pandas as pd
import skimage 
import pickle

defaultColumns = ['movieId', 'rating']

movies_df = pd.read_csv("./csv/movies.csv")
ratings_df = pd.read_csv("./csv/ratings.csv")

options = [
"Toy Story (1995)",
"Jumanji (1995)",
"Dracula: Dead and Loving It (1995)",
"Babe (1995)",
"Captain Marvel (2018)",
"Avengers: Infinity War - Part I (2018)",
"Avengers: Infinity War - Part II (2019)",
"Thor: Ragnarok (2017)",
"Guardians of the Galaxy 2 (2017)",
"Captain America: Civil War (2016)",
"Doctor Strange (2016)",
"X-Men: Apocalypse (2016)",
"Untitled Spider-Man Reboot (2017)",
"Manolito Four Eyes (1999)",
"El Robobo De La Jojoya (1991)",
"Elsa & Fred (2014)",
"Soccer Days (2003)",
"Too Late Blues (1961)",
"Melbourne (2014)",
"Clear History (2013)",
"The Identical (2014)",
"The Trans-Atlantic Mystery (1932)",
"The Gamers: Hands of Fate (2013)",
"Love at First Fight (2014)",
"Mare Nostrum (1926)",
"Ring Of The Musketeers (1992)",
"Skylark (1941)",
"The Pagan (1929)",
"Scorpio One (1998)",
"The Chinese Ring (1947)",
"The Power and the Prize (1956)",
"The Gambler Returns: The Luck Of The Draw (1991)",
"Smart Blonde (1937)",
"The Affairs of Annabel (1938)",
"Montana Belle (1952)",
"The Clown (1953)",
"In the Blood (2006)",
"Pound of Flesh (2010)",
"The Chicken Chronicles (1977)",
"Lizzie (1957)",
"Magic Boy (1961)",
"Shadow Zone: The Undead Express (1996)",
"Someone To Love (1988)",
"I Found Stella Parish (1935)",
"The Savage Seven (1968)",
"The Decks Ran Red (1958)",
"Man on Fire (1957)",
"Life Begins (1932)",
"The Last of Mrs. Cheyney (1929)",
"One More Time (1970)",
"I Don't Buy Kisses Anymore (1992)",
"Night Unto Night (1949)",
"Heidi (1952)",
"Hammersmith Is Out (1972)",
"The Daughter of Rosie O'Grady (1950)",
"Sidewalks of New York (1931)",
"Mary Stevens M.D. (1933)",
"The Prize (2011)",
"Secrets of an Actress (1938)",
"Man Made Monster (1941)",
"Horror Island (1941)",
"Sealed Lips (1942)",
"Tangier (1946)",
"Aces Go Places V: The Terracotta Hit (1989)",
"Pagan Love Song (1950)",
"The Racers (1955)",
"The Oklahoman (1957)",
"The Golden Age of Comedy (1957)",
"Stalingrad (1989)",
"The Tsunami and the Cherry Blossom (2011)",
"The Power Within (1995)",
"No Greater Glory (1934)",
"Goodbye, Mr. President (1987)",
"Sankofa (1993)",
"Teza (2008)",
"The Mystery of Mr. X (1934)",
"Midnight Heat (1996)",
"Red Dust (1990)",
"In the Presence of Mine Enemies (1997)",
"Talk About a Stranger (1952)",
"The Newcomers (2000)",
"Right Cross (1950)",
"Heidi (1993)",
"The Miracle Worker (2000)",
"The Return of Sabata (1971)",
"The McKenzie Break (1970)",
"Mercy (2010)",
"Happily N'Ever After 2 (2009)",
"Left for Dead (2007)",
"Hotel (2004)",
"The Hearse (1980)",
"Infection (2005)",
"Voyage of the Unicorn (2001)",
"The Geisha Boy (1959)",
"The Phantom of the Opera (1990)",
"P.U.N.K.S (1999)",
"When a Man Falls (2007)",
"Springfield Rifle (1952)",
"Why Be Good? (1929)",
"The American Success Company (1980)",
"The Expedition to the End of the World (2014)",
"Jealousy (2013)",
"Magic Kid II (1994)",
"The Radio Burglary (1951)",
"A Dangerous Place (1995)",
"Magic Kid (1993)",
"Sin Takes a Holiday (1930)",
"Hoodlum Priest (1961)",
"Street Girl (1929)",
"The Fleet's In (1942)",
"The Divine Woman (1928)",
"The Sundowners (1950)",
"The Real Howard Spitz (1998)",
"Mrs. O'Malley and Mr. Malone (1950)",
"Sierra (1950)",
"The Gangster (1947)",
"Metamorphosis (2007)",
"Russkies (1987)",
"Julius Caesar (1971)",
"Hercules and the Lost Kingdom (1994)",
"Rites of Passage (2012)",
"The Suspect (2013)",
"Mongolian Death Worm (2010)",
"Winter Break (2003)",
"The Facility (2012)",
"Shredderman Rules (2007)",
"Man on Fire (1987)",
"Piranha Hunt (2006)",
"Trouble Along the Way (1953)",
"The Secret Agent Club (1996)",
"I'm in Love with a Church Girl (2013)",
"Ironclad 2: Battle for Blood (2014)",
"Voices (2007)",
"The Land Before Time XI: Invasion of the Tinysauruses (2005)",
"See Girl Run (2013)",
"Ritual (2002)",
"Head Office (1985)",
"Jim Jefferies: I Swear to God (2009)",
"Social Nightmare (2013)",
"Once Fallen (2010)",
"Lease of Life (1954)",
"The Magic Box (1951)",
"Captain Boycott (1947)",
"The Adventures of Tartu (1943)",
"Knight Without Armor (1937)",
"Suddenly (2006)",
"Winning Streak (2012)",
"The Slaughter (2006)",
"The Flesh and the Fiends (1961)",
"The Lion Has Wings (1939)",
"Sanders of the River (1935)",
"Counter-Attack (1945)",
"The Macomber Affair (1947)",
"A Woman's Vengeance (1948)",
"Cry, the Beloved Country (1952)",
"Address Unknown (1944)",
"Drums in the Deep South (1951)",
"The Great Man (1956)",
"I Accuse! (1958)",
"State Fair (1962)",
"Kill Katie Malone (2010)",
"The First of the Few (1942)",
"The Sacketts (1979)",
"Rio Conchos (1964)",
"Hercules and the Circle of Fire (1994)",
"Clawed: The Legend of Sasquatch (2005)",
"June Bride (1948)",
"Without Reservations (1946)",
"Katherine (1975)",
"Conspiracy: The Trial of the Chicago 8 (1987)",
"Descending Angel (1990)",
"Roswell (1994)",
"The Ballad of Lucy Whipple (2001)",
"Taken (2002)",
"Crown Heights (2004)",
"Marianne (1929)",
"They Fought for Their Motherland (1975)",
"Welcome to Hard Times (1967)",
"The Lost Continent (1968)",
"The Incredible 2-Headed Transplant (1971)",
"Scared to Death (1947)",
"Scrooge (1935)",
"Knife Fight (2013)",
"Suddenly (2013)",
"Tornado! (1996)",
"The Opposite Sex (1956)",
"Mischief Night (2013)",
"Saratoga (1937)",
"Legendary: Tomb of the Dragon (2013)",
"Red Riding Hood (2006)",
"Sea Beast (2008)",
"Hitler: The Last Ten Days (1973)",
"The Man From The Alamo (1953)",
"Hard Ride to Hell (2010)",
"End of the World (1931)",
"Lucrèce Borgia (1935)",
"Un grand amour de Beethoven (1936)",
"I Accuse (1938)",
"Tower of Nesle (1955)",
"The Battle of Austerlitz (1960)",
"Cyrano et D'Artagnan (1964)",
]

with open("model.pkl", "rb") as f:
    model = pickle.load(f)

def recomendacao(filme, nota):

    f_filme = movies_df.loc[movies_df['title'] == filme]['movieId'].values[0]
    f_nota = float(nota) 

    default = [
        f_filme,
        f_nota
    ]

    df=pd.DataFrame([default], columns = defaultColumns)

    predictions = model.predict(df)

    user_rating = ratings_df.loc[ratings_df['userId'] == predictions[0]]
    top_ratings = user_rating.sort_values(by='rating', ascending=False)
    
    top_ratings = top_ratings.drop(top_ratings[top_ratings['movieId'] == f_filme].index)
    top_movies = top_ratings.head(5)['movieId'].tolist()

    recomendacoes = []
    for movie_id in top_movies:
        movie = movies_df.loc[movies_df['movieId'] == movie_id]
        title = movie['title'].values[0]
        recomendacoes.append(title)
    recomendacoes

    result = '1 - ' + recomendacoes[0] + ';\n' + '2 - ' + recomendacoes[1] + ';\n' +'3 - ' + recomendacoes[2] + ';\n' +'4 - ' + recomendacoes[3] + ';\n' +'5 - ' + recomendacoes[4] + '.\n'

    return result

iface = gr.Interface(
    fn=recomendacao,
    title="Data Mining",
    allow_flagging="never",
    inputs=[
        gr.Dropdown(options, label="Filme", info="Escolha o nome de um filme"),
        gr.Slider(0, 5, value=0, label="Rating", info="Dê uma nota entre 0 e 5"),
    ],
    outputs="text")
iface.launch()