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import streamlit as st
from app_config import SYSTEM_PROMPT
from langchain_groq import ChatGroq
from dotenv import load_dotenv
from pathlib import Path
import os
import session_manager
from langchain_community.utilities import GoogleSerperAPIWrapper
env_path = Path('.') / '.env'
load_dotenv(dotenv_path=env_path)
st.markdown(
"""
<style>
.st-emotion-cache-janbn0 {
flex-direction: row-reverse;
text-align: right;
}
.st-emotion-cache-1ec2a3d{
display: none;
}
</style>
""",
unsafe_allow_html=True,
)
# Intialize chat history
print("SYSTEM MESSAGE")
if "messages" not in st.session_state:
st.session_state.messages = [{"role": "system", "content": SYSTEM_PROMPT}]
print("SYSTEM MODEL")
if "llm" not in st.session_state:
st.session_state.llm = ChatGroq(
model="llama-3.3-70b-versatile",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
api_key=str(os.getenv('GROQ_API'))
)
if "search_tool" not in st.session_state:
st.session_state.search_tool = GoogleSerperAPIWrapper(
serper_api_key=str(os.getenv('SERPER_API')))
def get_answer(query):
new_search_query = st.session_state.llm.invoke(
f"Convert below query to english for Ahmedabad Municipal Corporation (AMC) You just need to give translated query. Don't add any additional details.\n Query: {query}").content
search_result = st.session_state.search_tool.run(
f"{new_search_query} site:https://ahmedabadcity.gov.in/")
system_prompt = """You are a helpful assistance for The Ahmedabad Municipal Corporation (AMC). which asnwer user query from given context only. Output language should be as same as `original_query_from_user`.
context: {context}
original_query_from_user: {original_query}
query: {query}"""
return st.session_state.llm.invoke(system_prompt.format(context=search_result, query=new_search_query, original_query=query)).content
session_manager.set_session_state(st.session_state)
print("container")
# Display chat messages from history
st.markdown("<h1 style='text-align: center;'>AMC Bot</h1>", unsafe_allow_html=True)
container = st.container(height=700)
for message in st.session_state.messages:
if message["role"] != "system":
with container.chat_message(message["role"]):
if message['type'] == "table":
st.dataframe(message['content'].set_index(
message['content'].columns[0]))
elif message['type'] == "html":
st.markdown(message['content'], unsafe_allow_html=True)
else:
st.write(message["content"])
# When user gives input
if prompt := st.chat_input("Enter your query here... "):
with container.chat_message("user"):
st.write(prompt)
st.session_state.messages.append(
{"role": "user", "content": prompt, "type": "string"})
st.session_state.last_query = prompt
with container.chat_message("assistant"):
current_conversation = """"""
# if st.session_state.next_agent != "general_agent" and st.session_state.next_agent in st.session_state.agent_history:
for message in st.session_state.messages:
if message['role'] == 'user':
current_conversation += f"""user: {message['content']}\n"""
if message['role'] == 'assistant':
current_conversation += f"""ai: {message['content']}\n"""
current_conversation += f"""user: {prompt}\n"""
print("****************************************** Messages ******************************************")
print("messages", current_conversation)
print()
print()
response = get_answer(current_conversation)
print("******************************************************** Response ********************************************************")
print("MY RESPONSE IS:", response)
st.write(response)
st.session_state.messages.append(
{"role": "assistant", "content": response, "type": "string"})
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