from openai import OpenAI
import streamlit as st
from langchain_openai import ChatOpenAI
from langchain_openai.embeddings import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
import markdown
from operator import itemgetter
from langchain.schema.runnable import RunnablePassthrough
from langchain_core.prompts import ChatPromptTemplate
from langchain.schema import Document
from dotenv import load_dotenv
from langchain_community.vectorstores import Qdrant
#from langchain_qdrant import Qdrant
import os
import pandas as pd
import numpy as np
import datetime


# Page config
from PIL import Image, ImageEnhance

st.set_page_config(
    page_title="TrafficLens 📰",
    layout="wide",
    initial_sidebar_state="expanded",
    page_icon="🔍",
)

# Load environment variables
load_dotenv()
OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
base_llm = ChatOpenAI(model="gpt-4o")
embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")

prompt='I-495'
date='2025-01-15'

# Custom CSS for centered content
st.markdown("""
    <style>
    .main-container {
        max-width: 800px;
        margin: 0 auto;
        padding: 20px;
    }
    
    .stSelectbox {
        max-width: 400px;
        margin: 0 auto;
    }
    
    /* Center all text elements */
    .centered-text {
        text-align: center;
    }
    </style>
""", unsafe_allow_html=True)

# Header
col1, col2, col3, col4,col5 = st.columns([1, 1, 2, 1, 1])


with col3:
    st.markdown("<h1 class='centered-text'>Search TrafficLens</h1>", unsafe_allow_html=True)
    
with col4:
    image = Image.open('./data/news_icon.png')
    st.image(image, width=100, output_format="PNG", clamp=True)

st.markdown("<p class='centered-text'>Enter a topic and optional date to analyze traffic.</p>", unsafe_allow_html=True)

# Suggestions
topic_suggestions = [
    "accident",
    "traffic",
    "I-95"
]

data=pd.read_csv('./data/sentiment_index_traffic_index_final1.csv',
                index_col='index',
                parse_dates=True
            )

# Convert the index to datetime, if not already done
data.index = pd.to_datetime(data.index)

# Generate a sorted list of unique dates
sorted_dates = sorted(pd.unique(data.index))

# Format the sorted dates as string 'YYYY-MM-DD'
date_suggestions = [pd.Timestamp(date).strftime('%Y-%m-%d') for date in sorted_dates]
date_suggestions=np.append('',date_suggestions)
# Create centered container for search
# Define the allowed date range
start_date = datetime.date(2025, 1, 15)
end_date = datetime.date(2025, 1, 21)
col1, col2= st.columns([1,1])
with col1:
    prompt = st.selectbox(
        "Topic:",
        options=[""] + topic_suggestions,
        index=0,
        key="topic_select",
        placeholder="Select or type a topic..."
    )
with col2:    

    # date = 
    #st.date_input(
    # "Choose a date:", 
    # # min_value=start_date, 
    # # max_value=end_date, 
    # # value=start_date  # Default to start date
    # )
    date = st.selectbox(
        "Date (optional):",
        options=date_suggestions,
        index=0,
        key="date_select",
        placeholder="Select or type a date..."
    )
    date=str(date)


# st.write(f"You selected: {date} for the topic: {prompt}.")

col1, col2, col3, col4 = st.columns([1,1,1,1])
with col2:
    chat = st.button("chat", key="chat_button", use_container_width=True)
with col3:
    tableau=st.button("tableau", key="tableau_button", use_container_width=True)

# Handle search submission
st.session_state.prompt = prompt
st.session_state.date = date

if chat:
    
    # You can add navigation to results page or display results here
    st.success(f"Searching for: {prompt} {'on ' + date if date else ''}")
    # Add your search processing logic here
    st.switch_page("./pages/chat.py")

if tableau:
    st.switch_page("./pages/tableau.py")