def index():
form = Question()
suggestion = Suggestion()
similarity_result = np.array([])
if form.validate_on_submit():
the_question = form.question.data
if len(the_question.split(" ")) < 3:
flash("Veuillez Formulez une question correcte", "dark")
return redirect(url_for('index'))
for index, t in enumerate(nlp_question):
sm = t.similarity(nlp(the_question.lower()))
similarity_result = np.append(similarity_result, sm)
best_score, best_index = similarity_result.max(
), similarity_result.argmax()
if best_score < 0.87:
return redirect(url_for("suggest", question=the_question))
suggestion.question.data = the_question
return render_template('test.html',
reponse=rep[best_index],
ind=best_index,
question=the_question,
q_sim=ques[best_index],
similarity=100 *
float("{0:.3f}".format(best_score)),
suggestion=suggestion)
return render_template("index.html",
form=form,
legend='A Propos de Orange et moi')
def index():
form = Question()
suggestion = Suggestion()
better_index = []
similarity_result = []
indexes = []
if form.validate_on_submit():
the_question = form.question.data
if len(the_question.split(" ")) < 3:
flash("Veuillez Formulez une question correcte","dark")
return redirect(url_for('index'))
for index,t in enumerate(nlp_question):
sm = t.similarity(nlp(the_question.lower()))
if sm >= 0.87:
similarity_result.append(sm)
indexes.append(index)
scores = np.array(similarity_result)
if scores.size == 0:
return redirect(url_for("suggest",question=the_question))
elif scores.size < 3:
better_index,with_scores = find_maxs(scores,indexes,scores.size)
else:
better_index,with_scores = find_maxs(scores,indexes,3)
all_data = [(rep[i],ques[i],100*float("{0:.3f}".format(j))) for i,j in zip(better_index,with_scores)]
suggestion.question.data = the_question
return render_template('test.html',all_data=all_data,the_question=the_question,suggestion=suggestion)
return render_template("index.html",form=form,legend='A Propos de Orange et moi')
def validate_question():
user_q = request.args.get("user_q")
q_index = int(request.args.get("q_sim_index"))
q = user_q
r = rep[q_index]
c = cat[q_index]
ques.append(q)
rep.append(r)
cat.append(c)
nlp_question.append(nlp(q.lower()))
chat = Chat(question=q, reponse=r, categorie=c)
db.session.add(chat)
db.session.commit()
return redirect(url_for('index'))
def new_suggest():
suggestion = Suggestion()
if suggestion.validate_on_submit():
rep_sug = (suggestion.reponse.data).split(" ")
if len(rep_sug) <= 2:
flash("Merci de suggérer une réponse correct", "dark")
return redirect(url_for('index'))
chat = Chat(question=suggestion.question.data,
reponse=suggestion.reponse.data,
categorie=suggestion.categorie.data)
db.session.add(chat)
db.session.commit()
ques.append(suggestion.question.data)
rep.append(suggestion.reponse.data)
cat.append(suggestion.categorie.data)
nlp_question.append(nlp(ques.lower()))
flash("Merci pour votre contribution !", "primary")
return redirect(url_for("index"))
else:
flash("Merci de bien voloir suggérer une réponse correcte", "dark")
return redirect(url_for('index'))