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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
def text():
with st.chat_message("assistant"):
st.write("Hello 👋, how may I assist you today?")
apiKey = "AIzaSyAXkkcrrUBjPEgj93tZ9azy7zcS1wI1jUA"
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
}
)
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