app.py

import os
import streamlit as st
import pandas as pd
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
from huggingface_hub import login
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

# Authenticate with Hugging Face
login(token=st.secrets["HF_TOKEN"])

# Load FAISS index
db = FAISS.load_local("faiss_index", HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L12-v2'), allow_dangerous_deserialization=True)

# Set up retriever
retriever = db.as_retriever(search_type="mmr", search_kwargs={'k': 1})

# Prompt template for the LLM
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.
"""

# Set up the LLM from Hugging Face
repo_id = "mistralai/Mistral-7B-Instruct-v0.3"

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)

# Set up RetrievalQA chain
retriever.search_kwargs = {'k': 1}
qa = RetrievalQA.from_chain_type(
    llm=mistral_llm,
    chain_type="stuff",
    retriever=retriever,
    chain_type_kwargs={"prompt": prompt},
)

# Streamlit interface setup
st.set_page_config(page_title="Alter-IA Chat", page_icon="🤖")

# Function to handle user input and display chatbot response
def chatbot_response(user_input):
    response = qa.run(user_input)
    return response

# Function to save user feedback
def save_feedback(user_input, bot_response, rating, comment):
    feedback = {
        "user_input": user_input,
        "bot_response": bot_response,
        "rating": rating,
        "comment": comment
    }
    
    # Check if the feedback file exists
    feedback_file = "feedback.csv"
    if os.path.exists(feedback_file):
        # Load existing feedback and append new feedback
        feedback_df = pd.read_csv(feedback_file)
        feedback_df = feedback_df.append(feedback, ignore_index=True)
    else:
        # Create a new dataframe for the feedback
        feedback_df = pd.DataFrame([feedback])
    
    # Save feedback to CSV
    feedback_df.to_csv(feedback_file, index=False)

# 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)

with col3:
    st.image("Altereo logo 2023 original - eau et territoires durables.png", width=150, use_column_width=True)

# Adding centered header and subtitle
st.markdown("""
    <style>
    .centered-text { text-align: center; }
    .centered-orange-text { text-align: center; color: darkorange; }
    </style>
""", unsafe_allow_html=True)
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 and display response
if submit_button and user_input.strip():
    bot_response = chatbot_response(user_input)
    st.markdown("### Bot:")
    st.text_area("", value=bot_response, height=300)
    
    # Star rating system
    st.markdown("### How would you rate the response?")
    rating = st.slider("Rate from 1 star to 5 stars", min_value=1, max_value=5, value=3)
    
    # Comment section
    comment = st.text_area("Any comments or suggestions for improvement?", "")
    
    # Save feedback when the user submits a rating and comment
    if st.button("Submit Feedback"):
        save_feedback(user_input, bot_response, rating, comment)
        st.success("Thank you for your feedback!")

# 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.")

# Section for the developer to review feedback
if st.checkbox("Show Feedback (Developer Only)"):
    if os.path.exists("feedback.csv"):
        feedback_df = pd.read_csv("feedback.csv")
        st.dataframe(feedback_df)
    else:
        st.warning("No feedback available yet.")