onMovie.py

from imdb import Cinemagoer
from ofMovie import *
from langprocess import *
from score import *
from director import *
import pandas

ia = Cinemagoer()


df = pandas.read_csv("movie_data.csv")

def take_input(moviedict):
    moremovies = False

    nlpout = nlp(moviedict["textbox"])

    
    res = []
    
    
    if moviedict["similar_movies"] != "no":

        moremovies = True
        movies = moviedict["similar_movies"]
        movlist = movies.split(",")
    
        vectors = []
        tags = []
    
    
    # Loop through the list of movie titles
        for mov in movlist:
        # Search for each movie title and append the result to the 'res' list
            try:
                movie_info = ia.search_movie(mov)
                if movie_info:
                    res.append(movie_info[0]["title"])
            except Exception as e:
            # Handle the exception (e.g., log the error, continue, or take appropriate action)
                print(f"Error fetching data for '{mov}': {e}")
          
    
    
    if moviedict["genres"] != "no":
        yo = moviedict["genres"]
        if not nlpout[0] in moviedict["genres"]:
            if not nlpout[0] == "no":
                yo = nlpout[0]+","+moviedict["genres"]

        genreforvector = genre_encoding(yo) 
        
    if moviedict["decade"] != "no":
        decadecode = decade_coder(moviedict["decade"].strip())
    else:
        decadecode = 5
       
    moviecode = 1
    if moviedict["length"] != "no":
        length = moviedict["length"].strip().lower()
        if moviedict["length"] == "long" or moviedict["length"] == "longer":
            moviecode = 2
        if moviedict["length"] == "medium":
            moviecode = 1
        if moviedict["length"] == "short" or moviedict["length"] =="shorter":
            moviecode = 0
        else:
            moviecode = 1
    
    vector = [decadecode,moviecode,genreforvector,nlpout[1]]
    
    listofsimilarities = []
    director_star_lm = []
    for ds in moviedict["actor-director"].split(', '):  # Split the names by comma and space
        result = ia.search_person(ds)
        if result:  # Check if the search returned any results
            first_name = result[0]['name']
            director_star_lm.append(first_name)


    for index in range(1000):
        if narrow(index,moviedict["maturity"],director_star_lm):
            
            listofsimilarities.append((calculate_score(df.loc[index,"Vector"], vector),df.loc[index,"Titles"]))
        
        
        
    listofsimilarities.sort(key = lambda x: x[0],reverse=True)
    
    print(listofsimilarities)
    return(listofsimilarities[0][1])
        
def nlp(text):
    taglist = ['absurd', 'action', 'adult comedy', 'allegory', 'alternate history', 'alternate reality', 'anti war', 'atmospheric', 'autobiographical', 'avant garde', 'blaxploitation', 'bleak', 'boring', 'brainwashing', 'claustrophobic', 'clever', 'comedy', 'comic', 'cruelty', 'cult', 'cute', 'dark', 'depressing', 'dramatic', 'entertaining', 'fantasy', 'feel-good', 'flashback', 'good versus evil', 'gothic', 'haunting', 'historical', 'historical fiction', 'home movie', 'horror', 'humor', 'insanity', 'inspiring', 'intrigue', 'magical realism', 'melodrama', 'murder', 'mystery', 'neo noir', 'non fiction', 'paranormal', 'philosophical', 'plot twist', 'pornographic', 'prank', 'psychedelic', 'psychological', 'queer', 'realism', 'revenge', 'romantic', 'sadist', 'satire', 'sci-fi', 'sentimental', 'storytelling', 'stupid', 'suicidal', 'suspenseful', 'thought-provoking', 'tragedy', 'violence', 'western', 'whimsical']
    g = get_genre_prediction(text)
    t = get_tags(text)
    tag_vector = [0]*len(taglist)
    for index,value in enumerate(taglist):
        if value in t:
            tag_vector[index] = 1
    return(g,tag_vector)