Flask TypeError : The view function did not return a valid response. The function either returned None or ended without a return statement [duplicate]

Question

I'm making a flask app that is supposed to generate a summary. However, flask is telling me that i have a function that doesn't return anything. I double checked and i can't find any function that isn't returning something.

app = Flask(__name__)

@app.route('/',methods=['GET'])
def index():
    return render_template('index.html')

UPLOAD_FOLDER = '/path_to_directory/SUMM-IT-UP/Uploads'
ALLOWED_EXTENSIONS = set(['txt', 'pdf'])

app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER

model = None
nlp = None

# @app.route('/load', methods=['GET'])
# def _load_model():
#     model = load_model()
#     return True

def load_model():
    nlp = en_coref_md.load()
    print "coref model loaded"

    VOCAB_FILE = "skip_thoughts_uni/vocab.txt"
    EMBEDDING_MATRIX_FILE = "skip_thoughts_uni/embeddings.npy"
    CHECKPOINT_PATH = "skip_thoughts_uni/model.ckpt-501424"
    encoder = encoder_manager.EncoderManager()
    print "loading skip model"
    encoder.load_model(configuration.model_config(),
                        vocabulary_file=VOCAB_FILE,
                        embedding_matrix_file=EMBEDDING_MATRIX_FILE,
                        checkpoint_path=CHECKPOINT_PATH)
    print "loaded"
    return encoder,nlp

def convertpdf (fname, pages=None):
    if not pages:
        pagenums = set()
    else:
        pagenums = set(pages)

    output = StringIO()
    manager = PDFResourceManager()
    converter = TextConverter(manager, output, laparams=LAParams())
    interpreter = PDFPageInterpreter(manager, converter)

    infile = file(fname, 'rb')
    for page in PDFPage.get_pages(infile, pagenums):
        interpreter.process_page(page)
    infile.close()
    converter.close()
    text = output.getvalue()
    output.close
    return text 

def readfiles (file):
    with open(file, 'r') as f:
        contents = f.read()
    return contents

def preprocess (data):
    data = data.decode('utf-8')
    data = data.replace("\n", "")
    data = data.replace(".", ". ")
    sentences = ""
    for s in sent_tokenize(data.decode('utf-8')):
        sentences= sentences + str(s.strip()) + " "
    return sentences

def coref_resolution (data,nlp):
    sent = unicode(data, "utf-8")
    doc = nlp(sent)
    if(doc._.has_coref):
        data = str(doc._.coref_resolved)
    return data

def generate_embed (encoder,data):
    sent = sent_tokenize(data)
    embed = encoder.encode(sent)
    x = np.isnan(embed)
    if (x.any() == True):
        embed = Imputer().fit_transform(embed)
    return sent, embed

def cluster (embed,n):
    n_clusters = int(np.ceil(n*0.33))
    kmeans = KMeans(n_clusters=n_clusters, random_state=0)
    kmeans = kmeans.fit(embed)

    array = []
    for j in range(n_clusters):
        array.append(list(np.where(kmeans.labels_ == j)))

    arr= []
    for i in range (n_clusters):
        ratio = float(len(array[i][0]))/float(n)
        sent_num = int(np.ceil(float(len(array[i][0]))*ratio))
        if (sent_num > 0):
            arr.append([i,sent_num])

    return array,arr

def sent_select (arr, array, sentences,embed):
    selected = []
    for i in range(len(arr)):
        sentences_x = []
        for j in range(len(array[arr[i][0]][0])):
                    sentences_x.append(sentences[array[arr[i][0]][0][j]])

        sim_mat = np.zeros([len(array[arr[i][0]][0]), len(array[arr[i][0]][0])])
        for k in range(len(array[arr[i][0]][0])):
            for l in range(len(array[arr[i][0]][0])):
                if k != l:
                    sim_mat[k][l] = cosine_similarity(embed[k].reshape(1,2400), embed[l].reshape(1,2400))

        nx_graph = nx.from_numpy_array(sim_mat)
        scores = nx.pagerank(nx_graph)
        ranked = sorted(scores)
        x = arr[i][1]  
        for p in range(x):
                selected.append(sentences_x[ranked[p]])

    return selected


def generate_summary(encoder,text):
    sent, embed = generate_embed(encoder,text)
    array , arr = cluster(embed, len(sent))
    selected = sent_select (arr,array,sent,embed)
    summary = ""
    for x in range(len(selected)):
        try:
            summary = summary + selected[x].encode('utf-8') + " "
        except:
            summary = summary + str(selected[x]) + " "
    try:
        sum_sent = sent_tokenize(summary.decode('utf-8'))
    except:
        sum_sent = sent_tokenize(summary)


        summary = ""
        for s in sent:
            for se in sum_sent:
                if (se == s):
                    try:
                        summary = summary + se.encode('utf-8') + " "
                    except:
                        summary = summary + str(se) + " "
    return summary


def allowed_file(filename):
    return '.' in filename and \
           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS

@app.route('/single-summary', methods=['POST'])
def singleFileInput():
    print(request.files['singleFile'])
    file = request.files['singleFile']

    filename = secure_filename(file.filename)
    file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
    uploaded_file_path = os.path.join(UPLOAD_FOLDER, filename)
    text = ""
    # for i in range(1, len(sys.argv)):
    if(".pdf" in uploaded_file_path):
        t = convertpdf(uploaded_file_path)
        t = preprocess(t)
        t = coref_resolution(t, nlp).decode('utf-8')
        text = text + t.decode('utf-8')
    elif(".txt" in uploaded_file_path):
        t = readfiles(uploaded_file_path)
        t = preprocess(t)
        t = coref_resolution(t, nlp).decode('utf-8')
        text = text + t
    summary = generate_summary(model,text)
    return summary


@app.route('/multiple-summary', methods=['POST'])
def multipleFileInput():
    # for f in range(1, len(request.files['multipleFile'])):
    print(request.files['multipleFile'])
    file = request.files['multipleFile']

    filename = secure_filename(file.filename)
    file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
    uploaded_file_path = os.path.join(UPLOAD_FOLDER, filename)
    text = ""
    # for i in range(1, len(sys.argv)):
    if(".pdf" in uploaded_file_path):
        t = convertpdf(uploaded_file_path)
        t = preprocess(t)
        t = coref_resolution(t, nlp)
        text = text + t
    elif(".txt" in uploaded_file_path):
        t = readfiles(uploaded_file_path)
        t = preprocess(t)
        t = coref_resolution(t, nlp)
        text = text + t
    summary = generate_summary(model,text)
    return summary

@app.route('/', methods=['GET', 'POST'])
def upload_file():
    if request.method == 'POST':
        # check if the post request has the file part
        if 'file' not in request.files:
            flash('No file part')
            return redirect(request.url)
        file = request.files['file']
        # if user does not select file, browser also
        # submit an empty part without filename
        if file.filename == '':
            flash('No selected file')
            return redirect(request.url)
        if file and allowed_file(file.filename):
            filename = secure_filename(file.filename)
            file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
            #return uploaded_file(filename)
            # return redirect(url_for('uploaded_file',
            #                         filename=filename))
    return render_template('index.html')

if __name__ == '__main__':
    global model
    global nlp
    model, nlp = load_model()

    app.run(debug=True)

Here is an image of the error stack

Any ideas as to why i'm still getting this error?

Answer 1

The problem here is that one of your functions returns None and not that a return statement is missing, as observed in the error shown in your question.

In order to provide more detailed help you'd need to provide a Minimal, Complete and Verifiable example.

Some of your calculations are returning a None value and you are trying to pass that value as a return value.

Here's an example of a function returning None:

def lyrics():
    pass
a = lyrics()
print (a)

Output:

None

Specifically I also see in your code:

model = None
nlp = None

What I would suggest, for further debugging, is using Flask's logging facility in order to print in the console the values of the variables that you are using for manipulation in order to track down the error.

Here's the relevant documentation about how to use logging in Flask.