Delete app.py

app.py

@@ -1,65 +0,0 @@
-# Import statements
-from langchain.chat_models import ChatOpenAI
-from langchain.chains import ConversationChain
-from langchain.chains.conversation.memory import ConversationBufferWindowMemory
-from langchain.prompts import (
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-    ChatPromptTemplate,
-    MessagesPlaceholder
-)
-import streamlit as st
-from streamlit_chat import message
-from utils import *
-
-# Streamlit setup
-st.subheader("Legal Guardian")
-
-# Session state initialization
-if 'responses' not in st.session_state:
-    st.session_state['responses'] = ["How can I assist you?"]
-
-if 'requests' not in st.session_state:
-    st.session_state['requests'] = []
-
-if 'buffer_memory' not in st.session_state:
-    st.session_state.buffer_memory = ConversationBufferWindowMemory(k=3, return_messages=True)
-
-# Initialize ChatOpenAI and conversation
-llm = ChatOpenAI(model_name="gpt-3.5-turbo", openai_api_key="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")
-
-system_msg_template = SystemMessagePromptTemplate.from_template("""
-    Legal Guardian' is a GPT designed to assist with a broad range of legal questions related to children's issues, focusing on laws in India...
-    ...It asks for clarification on vague questions to ensure accurate and relevant responses, and treats each query independently for focused assistance.'
-""")
-
-human_msg_template = HumanMessagePromptTemplate.from_template("{input}")
-prompt_template = ChatPromptTemplate.from_messages([system_msg_template, MessagesPlaceholder(variable_name="history"), human_msg_template])
-conversation = ConversationChain(memory=st.session_state.buffer_memory, prompt=prompt_template, llm=llm, verbose=True)
-
-# Streamlit UI components
-response_container = st.container()
-textcontainer = st.container()
-
-# Handle user input and display conversation
-with textcontainer:
-    query = st.text_input("Query: ", key="input")
-    if st.button("Submit"):
-        with st.spinner("typing..."):
-            conversation_string = get_conversation_string()
-            refined_query = query_refiner(conversation_string, query)
-            st.subheader("Refined Query:")
-            st.write(refined_query)
-            context = find_match(refined_query)
-            response = conversation.predict(input=f"Context:\n {context} \n\n Query:\n{query}")
-        st.session_state.requests.append(query)
-        st.session_state.responses.append(response)
-
-# Display conversation history
-with response_container:
-    if st.session_state['responses']:
-        st.subheader("Chat History:")
-        for i in range(len(st.session_state['responses'])):
-            message(st.session_state['responses'][i], key=str(i))
-            if i < len(st.session_state['requests']):
-                message(st.session_state["requests"][i], is_user=True, key=str(i) + '_user')