Flask中的外部模块-TypeError：“系列”对象不可调用

Question

I am looking to bring in an external module into flask 'reommendationSearch', which essentially calculates the similarity of movie titles based frequency of similar key words

My issue arises when attempting to integrate this with flask, where 'title' is 'search_string'. Error: 'AttributeError: 'NoneType' object has no attribute 'indices'. Here is the traceback:

Traceback Snapshot

Flask Code:

from flask import Flask, render_template, flash, redirect, url_for, session, logging, request
from wtforms import Form,StringField, TextAreaField, PasswordField, validators
from ReccomendationTest import reommendationSearch
# Parse HTML Page via Flask
@app.route('/')
def index():
    return render_template('home.html')

class searchForm(Form):
    search = StringField('Movie Title:')

@app.route('/about',methods = ['GET','POST'])    
def search_results():
    form = searchForm(request.form)
    search_string = form.search.data 
    search_results = reommendationSearch()
    output_results = search_results.get_recommendations(search_string)
    results=[]
    if request.method == 'POST':
        return(output_results)

if __name__ =="__main__":
    app.secret_key='secret123'
    app.run()

ReccomendationTest :

import pandas as pd
import numpy as np
import os

from ast import literal_eval
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel


class reommendationSearch():
        #Import the Open Source Dataset
        os.chdir('C:\\Users\parmi\Documents\Python Scripts')
        org_data = pd.read_csv('tmdb_5000_movies.csv')

        #Define a TF-IDF Vectorizer Object. Remove all english stop words such as 'the', 'a'
        tfidf = TfidfVectorizer(stop_words='english')

    #Replace NaN with an empty string
    org_data['overview'] = org_data['overview'].fillna('')

    #Construct the required TF-IDF matrix by fitting and transforming the data
    tfidf_matrix = tfidf.fit_transform(org_data['overview'])

    # Compute Consine Similarirty Matrix
    cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)

    indices = pd.Series(org_data.index, index=org_data['title']).drop_duplicates()

    # Function that takes in movie title as input and outputs most similar movies
    def get_recommendations(title,self):
            #self.user_input = user_input
            # Get the index of the movie that matches the title
        idx = self.indices[title]

            # Get the pairwsie similarity scores of all movies with that movie
        sim_scores = list(enumerate(self.cosine_sim[idx]))

            # Sort the movies based on the similarity scores
        sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)

            # Get the scores of the 10 most similar movies
        sim_scores = sim_scores[1:11]


            # Sort the movies based on the similarity scores
        sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)

            # Get the movie indices
        movie_indices = [i[0] for i in sim_scores]

            # Return the top 10 most similar movies
        return self.org_data['title'].iloc[movie_indices]

Answer 1

You have an undefined title variable you're not declaring in your code.

If the title is something you would get from the user via 'POST' request, consider adding a param to the POST query and passing it into the "reommendationSearch" class.

@app.route('/about',methods = ['GET','POST'])    
def search_results():
    req_title = request.args.get('title')
    search_results = reommendationSearch(req_title)
...

class reommendationSearch(title):
    # And here goes the rest of your code

EDIT: I've just noticed you're using wtforms, in any case the title is not being passed into the "get_recommendations" function because it's external to the class and hence not defined within said class' scope.

In any case, the solution is to ensure that the title variable is defined inside of the class.