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Upload app.py
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app.py
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# -*- coding: utf-8 -*-
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"""
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Created on Fri Aug 18 08:01:41 2023
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@author: Shamim Ahamed, RE AIMS Lab
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"""
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import streamlit as st
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import pandas as pd
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from tqdm.cli import tqdm
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import numpy as np
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import requests
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import pandas as pd
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from tqdm import tqdm
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def get_user_data(api, parameters):
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response = requests.post(f"{api}", json=parameters)
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if response.status_code == 200:
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return response.json()
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else:
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print(f"ERROR: {response.status_code}")
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return None
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st.set_page_config(page_title="SuSastho.AI Chatbot", page_icon="🚀", layout='wide')
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st.markdown("""
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<style>
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p {
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font-size:0.8rem !important;
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}
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textarea {
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font-size: 0.8rem !important;
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padding: 0.8rem 1rem 0.75rem 0.8rem !important;
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}
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button {
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padding: 0.65rem !important;
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}
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.css-1lr5yb2 {
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background-color: rgb(105 197 180) !important;
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}
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.css-1c7y2kd {
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background-color: Transparent !important;
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}
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.css-4oy321 {
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background-color: rgba(240, 242, 246, 0.5) !important;
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}
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</style>
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""", unsafe_allow_html=True)
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st.markdown("""
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<style>
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#MainMenu {visibility: hidden;}
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footer {visibility: hidden;}
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</style>
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""",unsafe_allow_html=True)
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model_names = {
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'BLOOM 7B': 'bloom-7b',
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}
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with st.sidebar:
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st.title("SuSastho.AI - ChatBot 🚀")
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model_name = model_names[st.selectbox('Model', list(model_names.keys()), 0)]
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ctx_checker_tmp = st.slider('Context Checker Sensitivity', min_value=0.001, max_value=1.0, value=0.008, step=0.001)
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lm_tmp = st.slider('Language Model Sensitivity', min_value=0.001, max_value=1.0, value=0.1, step=0.001)
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endpoint = st.secrets["LLMEndpoint"]
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def main():
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if model_name == 'None':
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st.markdown('##### Please select a model.')
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return
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# Initialize chat history
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if "messages" not in st.session_state:
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st.session_state.messages = [{"role": 'assistant', "content": 'হ্যালো! আমি একটি এআই অ্যাসিস্ট্যান্ট। কীভাবে সাহায্য করতে পারি? 😊'}]
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# Display chat messages from history on app rerun
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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# Accept user input
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if prompt := st.chat_input("এখানে মেসেজ লিখুন"):
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# Display user message in chat message container
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with st.chat_message("user"):
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st.markdown(prompt)
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# Add user message to chat history
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st.session_state.messages.append({"role": "user", "content": prompt})
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## Get context
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params = {
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"chat_history": [
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{"content": prompt}
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],
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"model": "bloom-7b",
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"mode": "specific",
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"config": {
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"ctx_checker_tmp": ctx_checker_tmp,
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"lm_tmp": lm_tmp,
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}
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}
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resp = get_user_data(endpoint, params)
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if resp == None:
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st.markdown('#### INTERNAL ERROR')
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return
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response = resp['data']['responses'][0]['content']
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context = resp['data']['logs']['content']['retrival_model']['matched_doc']
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clen = len(context)
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context = '\n\n===============================\n\n'.join(context)
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response = f'###### Config: Context Checker Value: {ctx_checker_tmp}, LM Value: {lm_tmp}\n\n##### Matched Context: {clen}\n{context}\n\n##### Response:\n{response}'
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# Display assistant response in chat message container
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with st.chat_message("assistant", avatar=None):
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st.markdown(response)
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# Add assistant response to chat history
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st.session_state.messages.append({"role": "assistant", "content": response})
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main()
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