movieAI.py

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer # type: ignore
from sklearn.metrics.pairwise import cosine_similarity
from textblob import TextBlob
from colorama import init, Fore
import time
import sys

# Initialize colorama
init(autoreset=True)

# Load and preprocess the dataset
def load_data(file_path='imdb_top_1000.csv'):
    try:
        df = pd.read_csv(file_path)
        df['combined_features'] = df['Genre'].fillna('') + ' ' + df['Overview'].fillna('')
        return df
    except FileNotFoundError:
        print(Fore.RED + f"Error: The file '{file_path}' was not found.")
        exit()
    
movies_df = load_data()

# Vectorize the combined features and compute cosine similarity
tfidf = TfidfVectorizer(stop_words='english')
tfidf_matrix = tfidf.fit_transform(movies_df['combined_features'])
cosine_sim = cosine_similarity(tfidf_matrix, tfidf_matrix)

# List all unique genres
def list_genres(df):
    return sorted(set(genre.strip() for sublist in df['Genre'].dropna().str.split(',') for genre in sublist))

genres = list_genres(movies_df)

# Recommend movies based on filters (genre, mood, rating)
def recomend_movies(genre=None, mood=None, rating=None, top_n=5):
    filtered_df = movies_df
    if genre:
        filtered_df = filtered_df[filtered_df['Genre'].str.contains(genre, case=False, na=False)]
        if rating:
            filtered_df = filtered_df[filtered_df]

# Display recommendations🍿 😊  😞  🎥

# Small processing animation


# Handle AI recommendation flow 🔍


    # Processing animation while analyzing mood 😊  😞  😐
    
    # Processing animation while finding movies
    
      # Small processing animation while finding movies 🎬🍿

   
# Main program 🎥