import os
import nest_asyncio
nest_asyncio.apply()
import streamlit as st
from transformers import pipeline
from huggingface_hub import login
from streamlit.components.v1 import html
import pandas as pd

# Retrieve the token from environment variables
hf_token = os.environ.get("HF_TOKEN")
if not hf_token:
    st.error("Hugging Face token not found. Please set the HF_TOKEN environment variable.")
    st.stop()

# Login with the token
login(token=hf_token)

# Initialize session state for timer
if 'timer_started' not in st.session_state:
    st.session_state.timer_started = False
if 'timer_frozen' not in st.session_state:
    st.session_state.timer_frozen = False

# Timer component using HTML and JavaScript
def timer():
    return """
    <div id="timer" style="font-size:16px;color:#666;margin-bottom:10px;">⏱️ Elapsed: 00:00</div>
    <script>
    (function() {
        var start = Date.now();
        var timerElement = document.getElementById('timer');
        localStorage.removeItem("freezeTimer");
        var interval = setInterval(function() {
            if(localStorage.getItem("freezeTimer") === "true"){
                clearInterval(interval);
                timerElement.style.color = '#00cc00';
                return;
            }
            var elapsed = Date.now() - start;
            var minutes = Math.floor(elapsed / 60000);
            var seconds = Math.floor((elapsed % 60000) / 1000);
            timerElement.innerHTML = '⏱️ Elapsed: ' +
            (minutes < 10 ? '0' : '') + minutes + ':' +
            (seconds < 10 ? '0' : '') + seconds;
        }, 1000);
    })();
    </script>
    """

st.set_page_config(page_title="Review Scorer & Report Generator", page_icon="📝")
st.header("Review Scorer & Report Generator")

# Concise introduction
st.write("This model will score your reviews in your CSV file and generate a report based on those results.")

# Load models with caching to avoid reloading on every run
@st.cache_resource
def load_models():
    # Load the scoring model via pipeline.
    score_pipe = pipeline("text-classification", model="mixedbread-ai/mxbai-rerank-base-v1")
    # Load the Gemma text generation pipeline.
    gemma_pipe = pipeline("text-generation", model="google/gemma-3-1b-it", use_auth_token=hf_token)
    return score_pipe, gemma_pipe

score_pipe, gemma_pipe = load_models()

# Input: Query text for scoring and CSV file upload for candidate reviews
query_input = st.text_area("Enter your query text for analysis (this does not need to be part of the CSV):")
uploaded_file = st.file_uploader("Upload Reviews CSV File (must contain a 'document' column)", type=["csv"])

if uploaded_file is not None:
    try:
        df = pd.read_csv(uploaded_file)
        if 'document' not in df.columns:
            st.error("CSV must contain a 'document' column.")
            st.stop()
        candidate_docs = df['document'].dropna().astype(str).tolist()
    except Exception as e:
        st.error(f"Error reading CSV file: {e}")
        st.stop()
else:
    st.error("Please upload a CSV file.")
    st.stop()

if st.button("Generate Report"):
    if not query_input.strip():
        st.error("Please enter a query text!")
    else:
        if not st.session_state.timer_started and not st.session_state.timer_frozen:
            st.session_state.timer_started = True
            html(timer(), height=50)
        status_text = st.empty()
        progress_bar = st.progress(0)
        try:
            # Stage 1: Score candidate documents using the provided query.
            status_text.markdown("**🔍 Scoring candidate documents...**")
            progress_bar.progress(0)
            
            scored_docs = []
            for doc in candidate_docs:
                combined_text = f"Query: {query_input} Document: {doc}"
                result = score_pipe(combined_text)[0]
                scored_docs.append((doc, result["score"]))
            
            progress_bar.progress(50)
            
            # Stage 2: Generate Report using Gemma, including query and scored results.
            status_text.markdown("**📝 Generating report with Gemma...**")
            prompt = f"""
Generate a detailed report based on the following analysis.
Query:
"{query_input}"
Candidate Reviews with their scores:
{scored_docs}
Please provide a concise summary report explaining the insights derived from these scores.
"""
            report = gemma_pipe(prompt, max_length=200)
            progress_bar.progress(100)
            status_text.success("**✅ Generation complete!**")
            html("<script>localStorage.setItem('freezeTimer', 'true');</script>", height=0)
            st.session_state.timer_frozen = True
            st.write("**Scored Candidate Reviews:**", scored_docs)
            st.write("**Generated Report:**", report[0]['generated_text'])
        except Exception as e:
            html("<script>document.getElementById('timer').remove();</script>")
            status_text.error(f"**❌ Error:** {str(e)}")
            progress_bar.empty()