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import streamlit as st
from source_code import text_completion, chat, GEC, paraphrase, contextual_answer, summarize, improvements
st.title("AI21 Studio Jurassic")
st.image(
image="jurassic.jpg",
caption="Jurassic",
)
st.sidebar.title("Select your preferred tasks")
jurassic_models = [
"j2-light",
"j2-ultra",
"j2-mid",
]
tasks = [
"Generic",
"Specific"
]
# task = st.sidebar.selectbox(
# label="Select your Model",
# options = tasks
# )
disabled = False
# if task == "Generic":
# disabled = False
# task_disable = False
# if task == "Specific":
# task_disable = True
# generic_tasks = [
# "Text Completion",
# # "Chat"
# ]
specific_tasks = [
# "Contextual Answers",
"Paraphrase",
"Summarize",
"Grammetical Error Corrections",
"Text Improvements"
]
# choose_task = generic_tasks
# if task == "Specific":
# choose_task = specific_tasks
choose_task = specific_tasks
choose = st.sidebar.selectbox(
label="Select your tasks",
options = choose_task,
)
# model = st.sidebar.selectbox(
# label="Select your Model",
# options = jurassic_models,
# disabled=disabled
# )
# numResults = st.sidebar.number_input(
# label="Select Number of results",
# min_value=1,
# max_value=5,
# value=1,
# disabled=disabled
# )
# maxTokens = st.sidebar.number_input(
# label="Max Number of Tokens to generate",
# min_value=32,
# max_value=2048,
# value=200,
# step=2,
# disabled=disabled
# )
# temperature = st.sidebar.slider(
# label="Temperature",
# min_value=0.1,
# max_value=1.0,
# value=0.5,
# step=0.1,
# disabled=disabled
# )
# topP = st.sidebar.slider(
# label="Top P",
# min_value=0.1,
# max_value=1.0,
# value=0.6,
# step=0.1,
# disabled=disabled
# )
# topKReturn = st.sidebar.slider(
# label="Top K",
# min_value=1,
# max_value=10,
# value=5,
# step=1,
# disabled=disabled
# )
# context = st.sidebar.text_input(
# label="Context",
# )
# if choose == "Chat":
# question = st.chat_input(key="Question")
# if context is None:
# context = "Everything"
# # template = f"<|system|>\nYou are a intelligent chatbot and expertise in {context}.</s>\n<|user|>\n{question}.\n<|assistant|>"
# template = f"{context}\n{question}"
# if "messages" not in st.session_state:
# st.session_state.messages = []
# for message in st.session_state.messages:
# with st.chat_message(message.get('role')):
# st.write(message.get("content"))
# st.session_state.messages.append(
# {
# "role":"user",
# "content": f"Question: {question}"
# }
# )
# if question:
# result = chat(model, template, numResults, maxTokens, temperature, topKReturn, topP)
# with st.chat_message("user"):
# st.write(f"Context: {context}\n\nQuestion: {question}")
# if question.lower() == "clear":
# del st.session_state.messages
# st.session_state.messages.append(
# {
# "role": "assistant",
# "content": result
# }
# )
# with st.chat_message('User'):
# st.write(f"Context: {context}\n\nQuestion: {question}")
# with st.chat_message('assistant'):
# st.markdown(result)
if 0 > 1:
pass
else:
question = st.text_area(label="Question")
# if context is None:
# context = "Everything"
# template = f"<|system|>\nYou are a intelligent chatbot and expertise in {context}.</s>\n<|user|>\n{question}.\n<|assistant|>"
# if choose == "Text Completion":
# if question:
# result = text_completion(model, template, numResults, maxTokens, temperature, topKReturn, topP)
# st.markdown(result)
# if choose == "Contextual Answers":
# if question:
# result = contextual_answer(context, question)
# st.markdown(result)
if choose == "Paraphrase":
if question:
result = paraphrase(question)
st.markdown(result)
elif choose == "Summarize":
if question:
result = summarize(question)
st.markdown(result)
elif choose == "Grammetical Error Corrections":
if question:
result = GEC(question)
st.markdown(result)
elif choose == "Text Improvements":
if question:
result = improvements(question)
st.markdown(result)
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