Flask TypeError：视图函数未返回有效响应。函数返回None或没有return语句结束[重复]

Question

这个问题在这里已有答案：

• Flask view return error “View function did not return a response” 2回答

我正在制作一个应该生成摘要的烧瓶应用程序。然而，烧瓶告诉我，我有一个不返回任何东西的功能。我仔细检查，我找不到任何没有返回的功能。

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)

这是错误堆栈的图像

关于为什么我仍然会收到此错误的任何想法？

Answer 1

这里的问题是你的一个函数返回None而不是缺少一个return语句，正如你所做的错误中所观察到的那样。

为了提供更详细的帮助，您需要提供Minimal, Complete and Verifiable example。

您的一些计算返回None值，并且您尝试将该值作为返回值传递。

这是一个返回None的函数示例：

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

输出：

None

具体来说，我也在你的代码中看到：

model = None
nlp = None

为了进一步调试，我建议使用Flask的日志工具，以便在控制台中打印您用于操作的变量值，以便跟踪错误。

这是关于如何在Flask中使用日志记录的relevant documentation。