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import streamlit as st
from streamlit_chat import message as st_message
# from transformers import BlenderbotTokenizer
# from transformers import BlenderbotForConditionalGeneration
from transformers import pipeline
context = '''
نحن شركة متخصصة فى مجال الزكاء الاصطناعى.
نقدم العديد من الخدمات كالحلول للشركات و تدريبات فى مجال الزكاء الاصطناعى.
التدريبات المتاحة الان هى ETE و computer vision.
سعر ال ETE 4500 جنيه مصرى بدلا من 5000 جنيه.
وسعر ال computer vision 6000 جنيه مصرى بدلا من 6500 جنيه مصرى.
'''
@st.cache(allow_output_mutation=True)
def load_model():
model = pipeline('question-answering',model='ZeyadAhmed/AraElectra-Arabic-SQuADv2-QA')
return model
qa = load_model()
if "history" not in st.session_state:
st.session_state.history = []
st.title('Ask a question about Electro-pi')
# qa = load_model()
# user_message = st.session_state.input_text
def generate_answer():
qa = load_model()
user_message = st.session_state.input_text
# inputs = tokenizer(st.session_state.input_text, return_tensors="pt")
# result = model.generate(**inputs)
message_bot = qa(question= user_message, context= context)
print(message_bot)
if message_bot['score'] <= 0.2:
message_bot = "electrobot: sorry i didn't get that"
st.session_state.history.append({"message": user_message, "is_user": True})
st.session_state.history.append({"message": message_bot, "is_user": False})
else:
st.session_state.history.append({"message": user_message, "is_user": True})
st.session_state.history.append({"message": message_bot['answer'], "is_user": False})
print('2')
st.text_input("Talk to the bot", key="input_text", on_change=generate_answer)
print('3')
for chat in st.session_state.history:
# print('4')
st_message(**chat) # unpacking
print('4')
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