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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")
# 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():
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():
with st.sidebar:
file = st.file_uploader("Upload Image", ["png", "jpg"])
output = imgclassifier(file)
if st.button("View Results"):
st.write(output)
# def invoke_audio():
# st.write("Invoke Audio")
# st.write("Welcome to the invoke audio page")
# def invoke_video():
# st.write("Invoke Video")
# st.write("Welcome to the invoke video page")
# def invoke_image():
# st.write("Invoke Image")
# st.write("Welcome to the invoke image page")
# def invoke_text():
# st.write("Invoke Text")
# st.write("Welcome to the invoke text page")
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 == '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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