utils.py

import streamlit as st
from datetime import datetime
from typing import Dict
import pandas as pd

from pdf_generator import create_enhanced_pdf_report, create_csv_export

def init_session_state():
    if 'current_df' not in st.session_state:
        st.session_state.current_df = None
    if 'current_stats' not in st.session_state:
        st.session_state.current_stats = None
    if 'export_ready' not in st.session_state:
        st.session_state.export_ready = False
    if 'pdf_buffer' not in st.session_state:
        st.session_state.pdf_buffer = None
    if 'csv_data' not in st.session_state:
        st.session_state.csv_data = None

def render_export_section(df: pd.DataFrame, stats: Dict, outliers: Dict, model, t: Dict):
    st.markdown(f'<div class="section-header">{t["section_export"]}</div>', unsafe_allow_html=True)
    
    col1, col2, col3 = st.columns(3)
    
    with col1:
        if st.button(t['btn_generate_pdf'], key="generate_pdf_btn", type="primary"):
            try:
                with st.spinner(t['pdf_generating']):
                    st.session_state.pdf_buffer = create_enhanced_pdf_report(df, stats, outliers, model)
                    st.session_state.export_ready = True
                st.success(t['pdf_success'])
            except Exception as e:
                st.error(f"{t['pdf_failed']}: {str(e)}")
                st.session_state.export_ready = False
        
        if st.session_state.export_ready and st.session_state.pdf_buffer:
            st.download_button(
                label=t['btn_download_pdf'],
                data=st.session_state.pdf_buffer,
                file_name=f"production_report_{datetime.now().strftime('%Y%m%d_%H%M')}.pdf",
                mime="application/pdf",
                key="download_pdf_btn"
            )
    
    with col2:
        if st.button(t['btn_generate_csv'], key="generate_csv_btn", type="primary"):
            try:
                st.session_state.csv_data = create_csv_export(df, stats)
                st.success(t['csv_success'])
            except Exception as e:
                st.error(f"{t['csv_failed']}: {str(e)}")
        
        if st.session_state.csv_data is not None:
            csv_string = st.session_state.csv_data.to_csv(index=False)
            st.download_button(
                label=t['btn_download_csv'],
                data=csv_string,
                file_name=f"production_summary_{datetime.now().strftime('%Y%m%d_%H%M')}.csv",
                mime="text/csv",
                key="download_csv_btn"
            )
    
    with col3:
        csv_string = df.to_csv(index=False)
        st.download_button(
            label=t['btn_download_raw'],
            data=csv_string,
            file_name=f"raw_production_data_{datetime.now().strftime('%Y%m%d_%H%M')}.csv",
            mime="text/csv",
            key="download_raw_btn"
        )

def render_quality_check(outliers: Dict, t: Dict):
    st.markdown(f'<div class="section-header">{t["section_quality_check"]}</div>', unsafe_allow_html=True)
    
    cols = st.columns(len(outliers))
    for i, (material, info) in enumerate(outliers.items()):
        with cols[i]:
            if info['count'] > 0:
                dates_str = ", ".join(info['dates'])
                st.markdown(f'''<div class="alert-warning">
                    <strong>{material.title()}</strong><br>
                    {info["count"]} {t['quality_outliers']}<br>
                    {t['quality_normal_range']}: {info["range"]}<br>
                    <div class="quality-dates">Dates: {dates_str}</div>
                </div>''', unsafe_allow_html=True)
            else:
                st.markdown(f'<div class="alert-success"><strong>{material.title()}</strong><br>{t["quality_normal"]}</div>', 
                          unsafe_allow_html=True)

def render_ai_insights(model, stats: Dict, df: pd.DataFrame, t: Dict, lang: str):
    st.markdown(f'<div class="section-header">{t["section_ai_insights"]}</div>', unsafe_allow_html=True)
    
    quick_questions = [t['ai_quick_q1'], t['ai_quick_q2'], t['ai_quick_q3']]
    
    cols = st.columns(len(quick_questions))
    for i, q in enumerate(quick_questions):
        with cols[i]:
            if st.button(q, key=f"ai_q_{i}"):
                from ai_engine import query_ai
                with st.spinner(t['ai_analyzing']):
                    answer = query_ai(model, stats, q, df, lang)
                    st.info(answer)
    
    custom_question = st.text_input(
        t['ai_ask_label'], 
        placeholder=t['ai_custom_placeholder'],
        key="custom_ai_question"
    )
    
    if custom_question and st.button(t['ai_ask_btn'], key="ask_ai_btn"):
        from ai_engine import query_ai
        with st.spinner(t['ai_analyzing']):
            answer = query_ai(model, stats, custom_question, df, lang)
            st.success(f"**Q:** {custom_question}")
            st.write(f"**A:** {answer}")

def render_welcome_screen(t: Dict):
    st.markdown(f'<div class="section-header">{t["welcome_title"]}</div>', unsafe_allow_html=True)
    
    col1, col2 = st.columns(2)
    
    with col1:
        st.markdown(f"### {t['welcome_quick_start']}")
        st.markdown(t['welcome_steps'])
    
    with col2:
        st.markdown(f"### {t['welcome_features']}")
        st.markdown(t['welcome_features_list'])
    
    st.info(t['welcome_ready'])