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import base64
import os
import openai
import streamlit as st
# from fpdf import FPDF
st.set_page_config(page_title="ChatGPT", page_icon="π")
MAIN = st.empty()
def create_download_link(val, filename):
b64 = base64.b64encode(val) # val looks like b'...'
return f'<a href="data:application/octet-stream;base64,{b64.decode()}" download="{filename}.pdf">Download file</a>'
@st.cache_resource
def init_openai_settings():
openai.api_key = os.getenv("OPENAI_API_KEY")
def init_session():
if not st.session_state.get("chats"):
st.session_state["chats"] = {}
def new_chat(chat_name):
if not st.session_state["chats"].get(chat_name):
st.session_state["chats"][chat_name] = {
"answer": [],
"question": [],
"messages": [
{"role": "system", "content": st.session_state["params"]["prompt"]}
],
}
return chat_name
def switch_chat(chat_name):
if st.session_state.get("current_chat") != chat_name:
st.session_state["current_chat"] = chat_name
init_chat(chat_name)
st.stop()
def switch_chat2(chat_name):
if st.session_state.get("current_chat") != chat_name:
st.session_state["current_chat"] = chat_name
init_sidebar()
init_chat(chat_name)
st.stop()
def init_sidebar():
st.sidebar.title("ChatGPT")
chat_name_container = st.sidebar.container()
chat_config_expander = st.sidebar.expander('Chat configuration')
# export_pdf = st.sidebar.empty()
# chat config
st.session_state["params"] = dict()
# st.session_state['params']["api_key"] = chat_config_expander.text_input("API_KEY", placeholder="Please input openai key")
st.session_state["params"]["model"] = chat_config_expander.selectbox(
"Please select a model",
["gpt-3.5-turbo"], # , "text-davinci-003"
help="ID of the model to use",
)
st.session_state["params"]["temperature"] = chat_config_expander.slider(
"Temperature",
min_value=0.0,
max_value=2.0,
value=1.2,
step=0.1,
format="%0.2f",
help="""What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.""",
)
st.session_state["params"]["max_tokens"] = chat_config_expander.number_input(
"MAX_TOKENS",
value=2000,
step=1,
max_value=4000,
help="The maximum number of tokens to generate in the completion",
)
st.session_state["params"]["prompt"] = chat_config_expander.text_area(
"Prompts",
"You are a helpful assistant that answer questions as possible as you can.",
help="The prompt(s) to generate completions for, encoded as a string, array of strings, array of tokens, or array of token arrays.",
)
chat_config_expander.caption('Looking for help at https://platform.openai.com/docs/api-reference/chat')
new_chat_button = chat_name_container.button(
label="β New Chat", use_container_width=True
)
if new_chat_button:
new_chat_name = f"Chat{len(st.session_state['chats'])}"
st.session_state["current_chat"] = new_chat_name
new_chat(new_chat_name)
with st.sidebar.container():
for chat_name in st.session_state.get("chats", {}).keys():
if chat_name == st.session_state.get('current_chat'):
chat_name_container.button(
label='π¬ ' + chat_name,
on_click=switch_chat2,
key=chat_name,
args=(chat_name,),
type='primary',
use_container_width=True,
)
else:
chat_name_container.button(
label='π¬ ' + chat_name,
on_click=switch_chat2,
key=chat_name,
args=(chat_name,),
use_container_width=True,
)
if new_chat_button:
switch_chat(new_chat_name)
# Download pdf
# if st.session_state.get('current_chat'):
# chat = st.session_state["chats"][st.session_state['current_chat']]
# pdf = FPDF('p', 'mm', 'A4')
# pdf.add_page()
# pdf.set_font(family='Times', size=16)
# # pdf.cell(40, 50, txt='abcd.pdf')
#
# if chat["answer"]:
# for i in range(len(chat["answer"]) - 1, -1, -1):
# # message(chat["answer"][i], key=str(i))
# # message(chat['question'][i], is_user=True, key=str(i) + '_user')
# pdf.cell(40, txt=f"""YOU: {chat["question"][i]}""")
# pdf.cell(40, txt=f"""AI: {chat["answer"][i]}""")
#
# export_pdf.download_button('π€ PDF', data=pdf.output(dest='S').encode('latin-1'), file_name='abcd.pdf')
def init_chat(chat_name):
chat = st.session_state["chats"][chat_name]
# with MAIN.container():
answer_zoom = st.container()
ask_form = st.empty()
if len(chat['messages']) == 1 and st.session_state["params"]["prompt"]:
chat["messages"][0]['content'] = st.session_state["params"]["prompt"]
if chat['messages']:
# answer_zoom.markdown(f"""π€ **Prompt:** {chat["messages"][0]['content']}""")
answer_zoom.info(f"""Prompt: {chat["messages"][0]['content']}""", icon="βΉοΈ")
answer_zoom.caption(f"""βΉοΈ Prompt: {chat["messages"][0]['content']}""")
if chat["question"]:
for i in range(len(chat["question"])):
answer_zoom.markdown(f"""π **YOU:** {chat["question"][i]}""")
if i < len(chat["answer"]):
answer_zoom.markdown(f"""π€ **AI:** {chat["answer"][i]}""")
with ask_form.form(chat_name):
col1, col2 = st.columns([10, 1])
question_widget = col1.empty()
if not chat["question"]:
input_text = question_widget.text_area("π You: ", "Hello, how are you?", key="input", max_chars=2000,
label_visibility='collapsed')
else:
input_text = question_widget.text_area("π You: ", "", key="input", max_chars=2000,
label_visibility='collapsed')
submitted = col2.form_submit_button("π«")
if submitted and input_text:
chat["messages"].append({"role": "user", "content": input_text})
answer_zoom.markdown(f"""π **YOU:** {input_text}""")
with st.spinner("Wait for responding..."):
answer = ask(chat["messages"])
answer_zoom.markdown(f"""π€ **AI:** {answer}""")
chat["messages"].append({"role": "assistant", "content": answer})
if answer:
chat["question"].append(input_text)
chat["answer"].append(answer)
question_widget.text_area("π You: ", "", key="input-1", max_chars=2000,
label_visibility='collapsed')
def init_css():
"""try to fixed input field"""
st.markdown(
"""
<style>
div[data-testid="stVerticalBlock"] > div[style*="flex-direction: column;"] > [data-testid="stVerticalBlock"] > [data-testid="stForm"] {
border: 20px groove red;
position: fixed;
width: 100%;
flex-direction: column;
flex-grow: 5;
overflow: auto;
}
</style>
""",
unsafe_allow_html=True,
)
def ask(messages):
if st.session_state["params"]["model"] == 'gpt-3.5-turbo':
response = openai.ChatCompletion.create(
model=st.session_state["params"]["model"],
temperature=st.session_state["params"]["temperature"],
messages=messages,
max_tokens=st.session_state["params"]["max_tokens"],
)
answer = response["choices"][0]["message"]["content"]
else:
raise NotImplementedError('Not implemented yet!')
return answer
if __name__ == "__main__":
print("loading")
init_openai_settings()
# init_css()
init_session()
init_sidebar()
if st.session_state.get("current_chat"):
print("current_chat: ", st.session_state.get("current_chat"))
init_chat((st.session_state["current_chat"]))
if len(st.session_state["chats"]) == 0:
switch_chat(new_chat(f"Chat{len(st.session_state['chats'])}"))
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