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import streamlit as st
from langchain_core.messages import HumanMessage
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory
from langchain.memory.chat_message_histories import StreamlitChatMessageHistory
from streamlit_chat import message
import time
import random
import os
api = os.environ.get("api_key")
def text():
st.title("Vanilla Chat")
st.markdown("""
<style>
.anim-typewriter {
animation: typewriter 3s steps(40) 1s 1 normal both, blinkTextCursor 800ms steps(40) infinite normal;
overflow: hidden;
white-space: nowrap;
border-right: 3px solid;
font-family: serif;
font-size: 0.9em;
}
@keyframes typewriter {
from {
width: 0;
}
to {
width: 100%;
height: 100%
}
}
@keyframes blinkTextCursor {
from {
border-right-color: rgba(255, 255, 255, 0.75);
}
to {
border-right-color: transparent;
}
}
</style>
""", unsafe_allow_html=True)
text ="Hello 👋, how may I assist you today?"
animated_output = f'<div class="line-1 anim-typewriter">{text}</div>'
with st.chat_message("assistant").markdown(animated_output,unsafe_allow_html=True ):
st.markdown(animated_output,unsafe_allow_html=True)
apiKey = api
msgs = StreamlitChatMessageHistory(key="special_app_key")
memory = ConversationBufferMemory(memory_key="history", chat_memory=msgs)
if len(msgs.messages) == 0:
msgs.add_ai_message("How can I help you?")
template = """You are an AI chatbot having a conversation with a human.
{history}
Human: {human_input}
AI: """
prompt = PromptTemplate(input_variables=["history", "human_input"], template=template)
llm_chain = LLMChain( llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=apiKey), prompt=prompt, memory = memory)
if 'messages' not in st.session_state:
st.session_state['messages'] = []
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
prompt = st.chat_input("Say something")
if prompt:
with st.chat_message("user").markdown(prompt):
st.session_state.messages.append(
{
"role": "user",
"content": prompt
}
)
# Custom HTML and CSS for three-dot animation
spinner_html = """
<div class="col-3">
<div class="snippet" data-title="dot-pulse">
<div class="stage">
<div class="dot-pulse"></div>
</div>
</div>
</div>
"""
spinner_css = """
.dot-pulse {
position: relative;
left: -9999px;
width: 10px;
height: 10px;
border-radius: 5px;
background-color: #9880ff;
color: #9880ff;
box-shadow: 9999px 0 0 -5px;
animation: dot-pulse 1.5s infinite linear;
animation-delay: 0.25s;
}
.dot-pulse::before, .dot-pulse::after {
content: "";
display: inline-block;
position: absolute;
top: 0;
width: 10px;
height: 10px;
border-radius: 5px;
background-color: #9880ff;
color: #9880ff;
}
.dot-pulse::before {
box-shadow: 9984px 0 0 -5px;
animation: dot-pulse-before 1.5s infinite linear;
animation-delay: 0s;
}
.dot-pulse::after {
box-shadow: 10014px 0 0 -5px;
animation: dot-pulse-after 1.5s infinite linear;
animation-delay: 0.5s;
}
@keyframes dot-pulse-before {
0% {
box-shadow: 9984px 0 0 -5px;
}
30% {
box-shadow: 9984px 0 0 2px;
}
60%, 100% {
box-shadow: 9984px 0 0 -5px;
}
}
@keyframes dot-pulse {
0% {
box-shadow: 9999px 0 0 -5px;
}
30% {
box-shadow: 9999px 0 0 2px;
}
60%, 100% {
box-shadow: 9999px 0 0 -5px;
}
}
@keyframes dot-pulse-after {
0% {
box-shadow: 10014px 0 0 -5px;
}
30% {
box-shadow: 10014px 0 0 2px;
}
60%, 100% {
box-shadow: 10014px 0 0 -5px;
}
}
"""
st.markdown(f'<style>{spinner_css}</style>', unsafe_allow_html=True)
st.markdown(spinner_html, unsafe_allow_html=True)
for chunk in llm_chain.stream(prompt):
text_output = chunk.get("text", "")
st.markdown('<style>.dot-pulse { visibility: hidden; }</style>', unsafe_allow_html=True)
with st.chat_message("assistant").markdown(text_output):
st.session_state.messages.append(
{
"role": "assistant",
"content": text_output
}
)
#with st.chat_message("assistant"):
#message_placeholder = st.empty()
#full_response = ""
#assistant_response = random.choice(
#[
#"Hello there! How can I assist you today?",
#"Hi, human! Is there anything I can help you with?",
# "Do you need help?",
# ]
# )
# Simulate stream of response with milliseconds delay
# for chunk in text_output.split():
# full_response += chunk + " "
# time.sleep(0.05)
# Add a blinking cursor to simulate typing
# message_placeholder.markdown(full_response + "▌")
# message_placeholder.markdown(full_response)
# Add assistant response to chat history
# st.session_state.messages.append({"role": "assistant", "content": full_response})
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