Spaces:
Sleeping
Sleeping
File size: 2,268 Bytes
c23507d f488983 c23507d f488983 c23507d f488983 c23507d f488983 c23507d f488983 628fc9c c23507d 63e8cad f488983 c6cb50d 63e8cad 628fc9c 63e8cad c23507d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
import os
import streamlit as st
from embedchain import App
os.environ["HF_HOME"] = "./models"
#! PROVIDE HUGGINGFACE TOKEN IF RUNNING OFFLINE
@st.cache_resource
def conversational_ai():
return App.from_config(config_path="./config_main.yaml")
st.title('Demo of "AI Chatbot in Law"')
st.caption(
"🚀 A demo of conversation AI for Dhirubhai Ambani Centre for Technology and Law (DA-CTL) made by **Anurag Shukla**, **Tanaz Pathan** under guidance of **Prof. Prasenjit Majumder**"
)
if "messages" not in st.session_state:
st.session_state.messages = [
{
"role": "assistant",
"content": """
Hi! I'm a conversational AI specializing in Indian Legal System. How may I assist you today?
""",
}
]
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if prompt := st.chat_input("Disclaimer: I am still a product in developement"):
app = conversational_ai()
# app.reset()
# print(len(app.db.get()["metadatas"]))
# print(len(app.get_data_sources()))
# quit()
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.\n")
cntxt = app.search(prompt)
relevant_c = [i["context"] for i in cntxt if i["metadata"]["score"] <= 1.2]
print(
"\n===================\n",
*relevant_c,
sep="\n===================\n",
)
if len(relevant_c) != 0:
full_response = app.llm.query(
input_query=prompt,
contexts=relevant_c,
)
full_response = full_response.rpartition("Answer:")[-1].strip()
else:
full_response = (
"Sorry but I don't have relevant knowledge to asnwer that query."
)
print(f"\n#ANSWER\n\n{full_response}")
msg_placeholder.markdown(full_response)
st.session_state.messages.append(
{"role": "assistant", "content": full_response}
)
|