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import os | |
import streamlit as st | |
from langchain_community.vectorstores import FAISS | |
from langchain_community.embeddings import HuggingFaceEmbeddings | |
from langchain_huggingface import HuggingFaceEndpoint | |
from langchain.prompts import PromptTemplate | |
from langchain.chains import LLMChain, RetrievalQA | |
import gspread | |
from oauth2client.service_account import ServiceAccountCredentials | |
import json | |
# Load Google service account credentials from Hugging Face secrets | |
GOOGLE_SERVICE_ACCOUNT_JSON = st.secrets["GOOGLE_SERVICE_ACCOUNT_JSON"] | |
# Google Sheets API v4 setup | |
scope = ["https://www.googleapis.com/auth/spreadsheets", "https://www.googleapis.com/auth/drive"] | |
service_account_info = json.loads(GOOGLE_SERVICE_ACCOUNT_JSON) | |
creds = ServiceAccountCredentials.from_json_keyfile_dict(service_account_info, scope) | |
client = gspread.authorize(creds) | |
spreadsheet_id = '1Jf1k7Q71ihsxBf-XQYyucamMy14q7IjhUDlU8ZzR_Nc' # Replace with your actual spreadsheet ID | |
sheet = client.open_by_key(spreadsheet_id).sheet1 | |
# Function to save user feedback to Google Sheets | |
def save_feedback(user_input, bot_response, rating, comment): | |
feedback = [user_input, bot_response, rating, comment] | |
sheet.append_row(feedback) | |
# Hugging Face API login | |
from huggingface_hub import login | |
login(token=st.secrets["HF_TOKEN"]) | |
# Initialize LangChain components | |
db = FAISS.load_local("faiss_index", HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L12-v2'), allow_dangerous_deserialization=True) | |
retriever = db.as_retriever(search_type="mmr", search_kwargs={'k': 1}) | |
prompt_template = """ | |
### [INST] | |
Instruction: You are a Q&A assistant. Your goal is to answer questions as accurately as possible based on the instructions and context provided without using prior knowledge. You answer in FRENCH. | |
Analyse carefully the context and provide a direct answer based on the context. If the user says Bonjour or Hello, your only answer will be: Hi! comment puis-je vous aider? | |
Answer in french only | |
{context} | |
Vous devez répondre aux questions en français. | |
### QUESTION: | |
{question} | |
[/INST] | |
Answer in french only | |
Vous devez répondre aux questions en français. | |
""" | |
repo_id = "mistralai/Mistral-7B-Instruct-v0.2" | |
mistral_llm = HuggingFaceEndpoint( | |
repo_id=repo_id, max_length=2048, temperature=0.05, huggingfacehub_api_token=st.secrets["HF_TOKEN"] | |
) | |
# Create prompt from prompt template | |
prompt = PromptTemplate( | |
input_variables=["question"], | |
template=prompt_template, | |
) | |
# Create LLM chain | |
llm_chain = LLMChain(llm=mistral_llm, prompt=prompt) | |
# Create RetrievalQA chain | |
qa = RetrievalQA.from_chain_type( | |
llm=mistral_llm, | |
chain_type="stuff", | |
retriever=retriever, | |
chain_type_kwargs={"prompt": prompt}, | |
) | |
# Streamlit interface with improved aesthetics | |
st.set_page_config(page_title="Alter-IA Chat", page_icon="🤖") | |
# Define function to handle user input and display chatbot response | |
def chatbot_response(user_input): | |
response = qa.run(user_input) | |
return response | |
# Session state to hold user input and chatbot response | |
if 'user_input' not in st.session_state: | |
st.session_state.user_input = "" | |
if 'bot_response' not in st.session_state: | |
st.session_state.bot_response = "" | |
# Create columns for logos | |
col1, col2, col3 = st.columns([2, 3, 2]) | |
with col1: | |
st.image("Design 3_22.png", width=150, use_column_width=True) # Adjust image path and size as needed | |
with col3: | |
st.image("Altereo logo 2023 original - eau et territoires durables.png", width=150, use_column_width=True) # Adjust image path and size as needed | |
# Streamlit components | |
st.markdown(""" | |
<style> | |
.centered-text { | |
text-align: center; | |
} | |
.centered-orange-text { | |
text-align: center; | |
color: darkorange; | |
} | |
</style> | |
""", unsafe_allow_html=True) | |
# Use CSS classes to style the text | |
st.markdown('<h3 class="centered-text">🤖 AlteriaChat 🤖 </h3>', unsafe_allow_html=True) | |
st.markdown('<p class="centered-orange-text">"Votre Réponse à Chaque Défi Méthodologique "</p>', unsafe_allow_html=True) | |
# Input form for user interaction | |
with st.form(key='interaction_form'): | |
st.session_state.user_input = st.text_input("You:", key="user_input_input") | |
ask_button = st.form_submit_button("Ask 📨") # Button to submit the question | |
if ask_button and st.session_state.user_input.strip(): | |
st.session_state.bot_response = chatbot_response(st.session_state.user_input) | |
# Display the bot response if available | |
if st.session_state.bot_response: | |
st.markdown("### Bot:") | |
st.text_area("", value=st.session_state.bot_response, height=600) | |
# Separate form for feedback submission | |
with st.form(key='feedback_form'): | |
st.markdown("### Rate the response:") | |
rating = st.slider("Select a rating:", min_value=1, max_value=5, value=1, key="rating") | |
st.markdown("### Leave a comment:") | |
comment = st.text_area("", key="comment") | |
# Separate submit button for feedback | |
feedback_submit_button = st.form_submit_button("Submit Feedback") | |
if feedback_submit_button: | |
if comment.strip(): | |
save_feedback(st.session_state.user_input, st.session_state.bot_response, rating, comment) | |
st.success("Thank you for your feedback!") | |
# Clear the session state after submission | |
st.session_state.user_input = "" | |
st.session_state.bot_response = "" | |
else: | |
st.warning("Please provide a comment before submitting feedback.") | |
st.markdown("---") | |
st.markdown("Collaboration is the key to success. Each question finds its answer, each challenge becomes an opportunity.") | |