import streamlit as st
import pandas as pd
import json
import os
from qa_loader import load_qa_and_create_vectorstore
from langchain_chroma import Chroma

# 🔹 Page Configuration
st.set_page_config(page_title="Admin Dashboard", layout="wide")

# 📂 Load available log files
log_dir = "logs"
log_files = [f for f in os.listdir(log_dir) if f.startswith("auto_test_results")]

# 🔹 Read the latest log file
if log_files:
    latest_log = sorted(log_files)[-1]
    log_path = os.path.join(log_dir, latest_log)
    with open(log_path, "r", encoding="utf-8") as f:
        log_data = f.readlines()
else:
    log_data = []

# 📊 **Log Analysis**
st.title("📊 Admin Dashboard")
st.subheader("🔍 AI Model Log Analysis")

if log_data:
    correct_count = sum(1 for line in log_data if "✅ Correct" in line)
    incorrect_count = sum(1 for line in log_data if "❌ Incorrect" in line)
    total_count = correct_count + incorrect_count
    accuracy = (correct_count / total_count) * 100 if total_count > 0 else 0

    st.metric("✅ Correct Answers", correct_count)
    st.metric("❌ Incorrect Answers", incorrect_count)
    st.metric("🎯 Accuracy", f"{accuracy:.2f}%")

    # **Most Frequently Asked Questions**
    question_lines = [line for line in log_data if line.startswith("Q:")]
    question_counts = pd.Series(question_lines).value_counts().head(10)

    st.subheader("📌 Most Frequently Asked Questions")
    st.write(question_counts)

else:
    st.warning("⚠️ No log records found. Please run the automated tests.")

# 📚 **Q&A Data Update**
st.subheader("📌 Update Q&A Database")

# Load existing Q&A data
qa_file = "Q&A_cleaned.json"
if os.path.exists(qa_file):
    with open(qa_file, "r", encoding="utf-8") as f:
        qa_data = json.load(f)
else:
    qa_data = []

# Add a new question
new_question = st.text_input("📝 New Question:")
new_answer = st.text_area("📌 Answer:")

if st.button("💾 Save"):
    if new_question and new_answer:
        qa_data.append({"QUESTION": new_question, "ANSWER": new_answer})
        with open(qa_file, "w", encoding="utf-8") as f:
            json.dump(qa_data, f, indent=4)
        st.success("✅ New question added!")
    else:
        st.warning("⚠️ Please enter both a question and an answer.")

# **Edit Incorrect Answers**
st.subheader("🔄 Edit Incorrect Answers")

if qa_data:
    question_list = [q["QUESTION"] for q in qa_data]
    selected_question = st.selectbox("🔎 Select Question to Edit:", question_list)
    
    selected_item = next((q for q in qa_data if q["QUESTION"] == selected_question), None)
    updated_answer = st.text_area("✏️ Updated Answer:", selected_item["ANSWER"] if selected_item else "")

    if st.button("📝 Update"):
        if selected_item:
            selected_item["ANSWER"] = updated_answer
            with open(qa_file, "w", encoding="utf-8") as f:
                json.dump(qa_data, f, indent=4)
            st.success("✅ Answer updated!")

# **Update Vector Database**
st.subheader("🔄 Update Vector Database")
if st.button("📥 Re-train"):
    retriever = load_qa_and_create_vectorstore()
    st.success("✅ Vector database successfully updated!")