Estoy creando una aplicación de matraz que se supone que genera un resumen. Sin embargo, el matraz me dice que tengo una función que no devuelve nada. Revisé dos veces y no puedo encontrar ninguna función que no devuelva algo.

 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)

Aquí hay una imagen de la pila de errores.

¿Alguna idea de por qué sigo recibiendo este error?