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app.py
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app.py
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from flask import Flask, request, render_template
import pickle
import os
import requests
app = Flask(__name__)
# Loading the pickled files
path = 'E:/MCAproject/Movie_Recommendation_System_Project/notebooks/'
with open(os.path.join(path, 'tfidf_vectorizer.pkl'), 'rb') as file:
tfidf_vectorizer = pickle.load(file)
with open(os.path.join(path, 'cosine_sim.pkl'), 'rb') as file:
cosine_sim = pickle.load(file)
with open(os.path.join(path, 'subset_df.pkl'), 'rb') as file:
subset_df = pickle.load(file)
# TMDb API key
YOUR_API_KEY = 'd23da6dcf1ea9800335aba342a81139e'
def content_based_recommendations(title, cosine_sim=cosine_sim, df=subset_df, num_recommendations=10):
title = title.strip()
idx = df[df['title'].str.strip().str.lower() == title.lower()].index
if len(idx) == 0:
return []
idx = idx[0]
sim_scores = list(enumerate(cosine_sim[idx]))
sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
movie_indices = [i[0] for i in sim_scores[1:]]
recommended_movies = df['title'].iloc[movie_indices].drop_duplicates()
unique_recommendations = recommended_movies[recommended_movies != title]
additional_recommendations_needed = num_recommendations - len(unique_recommendations)
if additional_recommendations_needed > 0:
additional_recommendations = df[~df['title'].isin(unique_recommendations) & (df['title'] != title)]['title'].head(additional_recommendations_needed)
unique_recommendations = unique_recommendations.append(additional_recommendations)
# Fetching poster URLs only for recommended movies
recommendations = []
for movie in unique_recommendations[:num_recommendations]: # Limiting to num_recommendations
poster_url = get_tmdb_movie_poster(movie, tmdb_api_key)
if poster_url:
recommendations.append((movie, poster_url))
return recommendations
def get_tmdb_movie_poster(movie_title, api_key):
search_url = f"https://api.themoviedb.org/3/search/movie?api_key={api_key}&query={movie_title}"
response = requests.get(search_url)
data = response.json()
if data['results']:
poster_path = data['results'][0]['poster_path']
if poster_path:
poster_url = f"https://image.tmdb.org/t/p/w500{poster_path}"
return poster_url
return None
@app.route('/', methods=['GET', 'POST'])
def index():
input_title = ""
input_movie_poster = ""
recommendations = []
if request.method == 'POST':
input_title = request.form['title']
input_movie_poster = get_tmdb_movie_poster(input_title, tmdb_api_key)
recommendations = content_based_recommendations(input_title)
return render_template('index.html', input_title=input_title, input_movie_poster=input_movie_poster, recommendations=recommendations)
if __name__ == '__main__':
app.run(debug=True)