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.")