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-v0.3"

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)

# 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

# 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;
    }
    </style>
    """, unsafe_allow_html=True)

# Utiliser la classe CSS pour centrer et colorer le texte
st.markdown('<h3 class="centered-text">🤖 AlteriaChat 🤖 </h3>', unsafe_allow_html=True)
st.markdown("""
    <style>
    .centered-orange-text {
        text-align: center;
        color: darkorange;
    }
    </style>
    """, unsafe_allow_html=True)

# Centrer et colorer en orange foncé le texte spécifique
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 📨")

import streamlit as st
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)
sheet = client.open("users feedback").sheet1  # Replace with your Google Sheet name

# 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)

# Streamlit app layout


user_input = st.text_input("You:")
bot_response = "This is a bot response."  # Replace this with your chatbot's response logic

st.markdown("### Rate the response:")
rating = st.selectbox("", [1, 2, 3, 4, 5])

st.markdown("### Leave a comment:")
comment = st.text_area("")

if st.button("Submit"):
    if user_input.strip() and comment.strip():
        save_feedback(user_input, bot_response, rating, comment)
        st.success("Thank you for your feedback!")
    else:
        st.warning("Please provide both input and comment.")

st.markdown("---")
st.markdown("Collaboration is the key to success. Each question finds its answer, each challenge becomes an opportunity.")