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import os
import streamlit as st
from embedchain import App
os.environ["HF_HOME"] = "models"
@st.cache_resource
def embedchain_bot():
return App.from_config(config_path="./config_main.yaml")
st.title("💬 Chatbot")
st.caption("🚀 An Embedchain app created by Anurag Shukla (IRLP Lab) for DA-IICT!")
if "messages" not in st.session_state:
st.session_state.messages = [
{
"role": "assistant",
"content": """
Hi! I'm a chatbot. I can answer questions and learn new things!\n
""",
}
]
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if prompt := st.chat_input("Ask me anything!"):
app = embedchain_bot()
# if prompt.startswith("/add"):
# with st.chat_message("user"):
# st.markdown(prompt)
# st.session_state.messages.append({"role": "user", "content": prompt})
# prompt = prompt.replace("/add", "").strip()
# with st.chat_message("assistant"):
# message_placeholder = st.empty()
# message_placeholder.markdown("Adding to knowledge base...")
# app.add(prompt)
# message_placeholder.markdown(f"Added {prompt} to knowledge base!")
# st.session_state.messages.append({"role": "assistant", "content": f"Added {prompt} to knowledge base!"})
# st.stop()
with st.chat_message("user"):
st.markdown(prompt)
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("assistant"):
msg_placeholder = st.empty()
msg_placeholder.markdown("Thinking...")
print("Querying the Agent.")
full_response = app.query(prompt.lower())
full_response = full_response.rpartition("Answer:")[-1].strip()
print(f"Answer:\n\n{full_response}")
msg_placeholder.markdown(full_response)
st.session_state.messages.append(
{"role": "assistant", "content": full_response}
)
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