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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.schema.runnable import RunnablePassthrough | |
from langchain.chains import LLMChain | |
from huggingface_hub import login | |
login(token=st.secrets["HF_TOKEN"]) | |
from langchain_community.document_loaders import TextLoader | |
from langchain_text_splitters import CharacterTextSplitter | |
from langchain_community.document_loaders import PyPDFLoader | |
from langchain.chains import RetrievalQA | |
from langchain.prompts import PromptTemplate | |
from langchain.embeddings.huggingface import HuggingFaceEmbeddings | |
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 said 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-v0.3" | |
mistral_llm = HuggingFaceEndpoint( | |
repo_id=repo_id, max_length=1024, 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) | |
retriever.search_kwargs = {'k':1} | |
qa = RetrievalQA.from_chain_type( | |
llm=mistral_llm, | |
chain_type="stuff", | |
retriever=retriever, | |
chain_type_kwargs={"prompt": prompt}, | |
) | |
import streamlit as st | |
import pandas as pd | |
import os | |
# Ensure the star rating component is available | |
try: | |
from streamlit_star_rating import st_star_rating | |
except ImportError: | |
st.write("Installing required package: streamlit-star-rating") | |
!pip install streamlit-star-rating | |
from streamlit_star_rating import st_star_rating | |
# 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 = "This is a dummy response." # Replace with actual chatbot logic | |
return 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 | |
# Add custom CSS for centered and colored text | |
st.markdown(""" | |
<style> | |
.centered-text { | |
text-align: center; | |
} | |
.centered-orange-text { | |
text-align: center; | |
color: darkorange; | |
} | |
</style> | |
""", unsafe_allow_html=True) | |
# Display title and subtitle | |
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 and button for user interaction | |
user_input = st.text_input("You:", "") | |
submit_button = st.button("Ask 📨") | |
# Handle user input | |
if submit_button: | |
if user_input.strip() != "": | |
bot_response = chatbot_response(user_input) | |
st.markdown("### Bot:") | |
st.text_area("", value=bot_response, height=200) | |
else: | |
st.warning("⚠️ Please enter a message.") | |
# Motivational quote at the bottom | |
st.markdown("---") | |
st.markdown("*La collaboration est la clé du succès. Chaque question trouve sa réponse, chaque défi devient une opportunité.*") | |
# Star rating system | |
st.markdown("### Évaluez notre service :") | |
rating = st_star_rating('Rate the response', 5, 0) | |
# Save rating to a file | |
ratings_file = "user_ratings.csv" | |
if rating > 0: | |
if not os.path.exists(ratings_file): | |
# Create a new file with headers if it doesn't exist | |
df = pd.DataFrame(columns=["User Input", "Bot Response", "Rating"]) | |
df.to_csv(ratings_file, index=False) | |
# Append new rating to the file | |
new_rating = {"User Input": user_input, "Bot Response": bot_response, "Rating": rating} | |
df = pd.read_csv(ratings_file) | |
df = df.append(new_rating, ignore_index=True) | |
df.to_csv(ratings_file, index=False) | |
st.success("Thank you for your feedback!") | |
# Display the contents of the ratings file | |
st.markdown("### User Ratings:") | |
if os.path.exists(ratings_file): | |
df = pd.read_csv(ratings_file) | |
st.write(df) | |
else: | |
st.write("No ratings yet.") | |