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import streamlit as st
from streamlit_option_menu import option_menu
from transformers import pipeline, Conversation
convo = pipeline(task="conversational", model="microsoft/DialoGPT-medium")
imgclassifier = pipeline(model="microsoft/beit-base-patch16-224-pt22k-ft22k")
qnabot = pipeline(task="question-answering", model="distilbert-base-cased-distilled-squad")
txtgen = pipeline(task="text-generation", model="EleutherAI/gpt-neo-2.7B")
txtclassifi = pipeline(task="text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment")
summurize = pipeline(task="summarization", model="sshleifer/distilbart-cnn-12-6")
visualqna = pipeline(task="vqa", model="microsoft/DialoGPT-medium")
# def homepage():
# st.write("Timeline")
# # allmessages =[]
# if "messages" not in st.session_state:
# st.session_state.messages = []
# if usrmsg := st.chat_input("Share a thought"):
# st.session_state.messages.append(usrmsg)
# with st.chat_message("user"):
# st.session_state.messages
def chat():
st.title("Chit-Chatbot")
if query := st.chat_input("Enter your message"):
uquery = Conversation(query)
response = convo(uquery)
with st.chat_message("assistant"):
st.write(response.generated_responses[-1])
def image_classifi():
st.title("Image Classification")
file = st.text_input("Enter Image URL")
output = imgclassifier(file)
if st.button("View Results"):
st.write(output)
def qna_bot():
st.title("Q&A-Chatbot")
if query := st.chat_input("Enter your message"):
response = qnabot(query)
with st.chat_message("assistant"):
st.write(response)
def txt_gen():
st.title("Text Generation")
if query := st.chat_input("Enter your message"):
response = txtgen(query)
with st.chat_message("assistant"):
st.write(response)
def txt_classifi():
st.title("Text Classification")
if query := st.chat_input("Enter your message"):
response = txtclassifi(query,)
with st.chat_message("assistant"):
st.write(response)
def summury():
st.title("Summury")
if query := st.chat_input("Enter your message"):
response = summurize(query, min_length=5, max_length=20)
with st.chat_message("assistant"):
st.write(response)
def visual_qna():
st.title("Visual Q&A")
with st.sidebar:
if img := st.text_input("Enter Image URL"):
st.image(img)
if query := st.chat_input("Enter your message"):
response = visualqna(img, query)
with st.chat_message("assistant"):
st.write(response)
def dashboard():
with st.sidebar:
selected = option_menu(None, ['Conversational', "Q&A", "Text Generation", "Text Classification", "Image Classification", "Summurization", "Visual Q&A" , "Logout"],
icons=['💬','❓', '📝', '🔤', '🖼️', '📑', '🔎', '🔓'])
# if selected == 'Home':
# homepage()
if selected == 'Conversational':
chat()
elif selected == "Image Classification":
image_classifi()
elif selected == 'Logout':
st.session_state.user = None
st.experimental_rerun()
# elif selected == "Invoke Document":
# invoke_document()
# elif selected == "Invoke Audio":
# invoke_audio()
# elif selected == "Invoke Video":
# invoke_video()
# elif selected == "Invoke Image":
# invoke_image()
# elif selected == "Invoke Text":
# invoke_text()
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