import os
import csv
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
from huggingface_hub import login

# Login to Hugging Face
login(token=st.secrets["HF_TOKEN"])

# Load FAISS index and ensure it only happens once
if 'db' not in st.session_state:
    st.session_state.db = FAISS.load_local(
        "faiss_index", 
        HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L12-v2'),
        allow_dangerous_deserialization=True
    )

# Use session state for retriever
retriever = st.session_state.db.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 1}
)

# Define prompt template
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"

# Load the model only once
if 'mistral_llm' not in st.session_state:
    st.session_state.mistral_llm = HuggingFaceEndpoint(
        repo_id=repo_id, 
        max_length=2048, 
        temperature=0.05, 
        huggingfacehub_api_token=st.secrets["HF_TOKEN"]
    )

# Create prompt and LLM chain
prompt = PromptTemplate(
    input_variables=["question"],
    template=prompt_template,
)
llm_chain = LLMChain(llm=st.session_state.mistral_llm, prompt=prompt)

# Create QA chain
qa = RetrievalQA.from_chain_type(
    llm=st.session_state.mistral_llm,
    chain_type="stuff",
    retriever=retriever,
    chain_type_kwargs={"prompt": prompt},
)

# Streamlit UI setup
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

# Define function to save feedback to CSV
def save_feedback(question, response, rating, comment):
    try:
        filename = '/tmp/feedback.csv'  # Use /tmp directory for temporary storage in Spaces
        file_exists = os.path.isfile(filename)
        with open(filename, 'a', newline='', encoding='utf-8') as csvfile:
            fieldnames = ['question', 'response', 'rating', 'comment']
            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
            if not file_exists:
                writer.writeheader()
            writer.writerow({'question': question, 'response': response, 'rating': rating, 'comment': comment})
        st.success("Thank you for your feedback! It has been saved.")
    except Exception as e:
        st.error(f"Error saving feedback: {e}")

# Use session state to store user input, bot response, rating, and comment
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 = ""

if 'rating' not in st.session_state:
    st.session_state.rating = 3  # Default rating

if 'comment' not in st.session_state:
    st.session_state.comment = ""

# 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)

# Add CSS for styling
st.markdown("""
    <style>
    .centered-text {
        text-align: center;
    }
    .centered-orange-text {
        text-align: center;
        color: darkorange;
    }
    </style>
    """, unsafe_allow_html=True)

# Center and color 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 and button for user interaction
st.session_state.user_input = st.text_input("You:", st.session_state.user_input)

if st.button("Ask 📨"):
    if st.session_state.user_input.strip() != "":
        st.session_state.bot_response = chatbot_response(st.session_state.user_input)

if st.session_state.bot_response:
    st.markdown("### Bot:")
    st.text_area("", value=st.session_state.bot_response, height=600)

    # Feedback Section
    st.markdown("### Évaluation de la réponse")
    st.session_state.rating = st.slider("Rating (1 to 5)", 1, 5, st.session_state.rating)
    st.session_state.comment = st.text_area("Your comment:", st.session_state.comment)
    
    if st.button("Submit Feedback"):
        if st.session_state.comment.strip() != "":
            save_feedback(st.session_state.user_input, st.session_state.bot_response, st.session_state.rating, st.session_state.comment)
        else:
            st.warning("⚠️ Please enter a comment.")

# 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.")