Update utils.py

utils.py

@@ -17,37 +17,25 @@ def init_session_state():
     if 'csv_data' not in st.session_state:
         st.session_state.csv_data = None
 
-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):
-    st.markdown('<div class="section-header">Export Reports</div>', unsafe_allow_html=True)
+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("Generate PDF Report", key="generate_pdf_btn", type="primary"):
+        if st.button(t['btn_generate_pdf'], key="generate_pdf_btn", type="primary"):
             try:
-                with st.spinner("Generating comprehensive PDF report..."):
+                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("PDF report generated successfully")
+                st.success(t['pdf_success'])
             except Exception as e:
-                st.error(f"PDF generation failed: {str(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="Download PDF Report",
+                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",
@@ -55,17 +43,17 @@ def render_export_section(df: pd.DataFrame, stats: Dict, outliers: Dict, model):
             )
     
     with col2:
-        if st.button("Generate CSV Summary", key="generate_csv_btn", type="primary"):
+        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("CSV summary generated successfully")
+                st.success(t['csv_success'])
             except Exception as e:
-                st.error(f"CSV generation failed: {str(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="Download CSV Summary",
+                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",
@@ -75,15 +63,15 @@ def render_export_section(df: pd.DataFrame, stats: Dict, outliers: Dict, model):
     with col3:
         csv_string = df.to_csv(index=False)
         st.download_button(
-            label="Download Raw Data",
+            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):
-    st.markdown('<div class="section-header">Quality Check</div>', unsafe_allow_html=True)
+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()):
@@ -92,72 +80,52 @@ def render_quality_check(outliers: Dict):
                 dates_str = ", ".join(info['dates'])
                 st.markdown(f'''<div class="alert-warning">
                     <strong>{material.title()}</strong><br>
-                    {info["count"]} outliers detected<br>
-                    Normal range: {info["range"]}<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>All values normal</div>', 
+                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):
-    st.markdown('<div class="section-header">AI Insights</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 = [
-        "How does production distribution on weekdays compare to weekends?",
-        "Which material exhibits the most volatility in our dataset?",
-        "To improve stability, which material or shift needs immediate attention?"
-    ]
+    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("Analyzing..."):
-                    answer = query_ai(model, stats, q, df)
+                with st.spinner(t['ai_analyzing']):
+                    answer = query_ai(model, stats, q, df, lang)
                     st.info(answer)
     
     custom_question = st.text_input(
-        "Ask about your production data:", 
-        placeholder="e.g., 'Compare steel vs aluminum last month'",
+        t['ai_ask_label'], 
+        placeholder=t['ai_custom_placeholder'],
         key="custom_ai_question"
     )
     
-    if custom_question and st.button("Ask AI", key="ask_ai_btn"):
+    if custom_question and st.button(t['ai_ask_btn'], key="ask_ai_btn"):
         from ai_engine import query_ai
-        with st.spinner("Analyzing..."):
-            answer = query_ai(model, stats, custom_question, df)
+        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():
-    st.markdown('<div class="section-header">How to Use This Platform</div>', unsafe_allow_html=True)
+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("""
-        ### Quick Start
-        1. Upload your TSV data in the sidebar
-        2. Or click Quick Load buttons for preset data
-        3. View production by material type
-        4. Analyze trends (daily/weekly/monthly)
-        5. Check anomalies in Quality Check
-        6. Export reports (PDF with AI, CSV)
-        7. Ask the AI assistant for insights
-        """)
+        st.markdown(f"### {t['welcome_quick_start']}")
+        st.markdown(t['welcome_steps'])
     
     with col2:
-        st.markdown("""
-        ### Key Features
-        - Real-time interactive charts
-        - One-click preset data loading
-        - Time-period comparisons
-        - Shift performance analysis
-        - Outlier detection with dates
-        - AI-powered PDF reports
-        - Intelligent recommendations
-        """)
+        st.markdown(f"### {t['welcome_features']}")
+        st.markdown(t['welcome_features_list'])
     
-    st.info("Ready to start? Upload your production data or use Quick Load buttons to begin analysis")
+    st.info(t['welcome_ready'])