🏭 Production Monitor with AI Insights
Nilsen Service & Consulting AS | Real-time Production Analytics & Recommendations
""", unsafe_allow_html=True)
model = init_ai()
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
with st.sidebar:
st.markdown("### 📊 Data Source")
uploaded_file = st.file_uploader("Upload Production Data", type=['csv'])
st.markdown("---")
st.markdown("### 📊 Quick Load")
col1, col2 = st.columns(2)
with col1:
if st.button("📊 2024 Data", type="primary", key="load_2024"):
st.session_state.load_preset = "2024"
with col2:
if st.button("📊 2025 Data", type="primary", key="load_2025"):
st.session_state.load_preset = "2025"
st.markdown("---")
st.markdown("""
**Expected TSV format:**
- `date`: MM/DD/YYYY
- `weight_kg`: Production weight
- `material_type`: Material category
- `shift`: day/night (optional)
""")
if model:
st.success("🤖 AI Assistant Ready")
else:
st.warning("⚠️ AI Assistant Unavailable")
st.info("To enable AI features, set GOOGLE_API_KEY as environment variable or in Streamlit secrets")
df = st.session_state.current_df
stats = st.session_state.current_stats
if uploaded_file:
try:
df = load_data(uploaded_file)
stats = get_material_stats(df)
st.session_state.current_df = df
st.session_state.current_stats = stats
st.success("✅ Data uploaded successfully!")
except Exception as e:
st.error(f"❌ Error loading uploaded file: {str(e)}")
elif 'load_preset' in st.session_state:
year = st.session_state.load_preset
try:
with st.spinner(f"Loading {year} data..."):
df = load_preset_data(year)
if df is not None:
stats = get_material_stats(df)
st.session_state.current_df = df
st.session_state.current_stats = stats
st.success(f"✅ {year} data loaded successfully!")
except Exception as e:
st.error(f"❌ Error loading {year} data: {str(e)}")
finally:
del st.session_state.load_preset
if df is not None and stats is not None:
st.markdown('', unsafe_allow_html=True)
materials = [k for k in stats.keys() if k != '_total_']
cols = st.columns(4)
for i, material in enumerate(materials[:3]):
info = stats[material]
with cols[i]:
st.metric(
label=material.replace('_', ' ').title(),
value=f"{info['total']:,.0f} kg",
delta=f"{info['percentage']:.1f}% of total"
)
st.caption(f"Daily avg: {info['daily_avg']:,.0f} kg")
if len(materials) >= 3:
total_info = stats['_total_']
with cols[3]:
st.metric(
label="Total Production",
value=f"{total_info['total']:,.0f} kg",
delta="100% of total"
)
st.caption(f"Daily avg: {total_info['daily_avg']:,.0f} kg")
st.markdown('', unsafe_allow_html=True)
col1, col2 = st.columns([3, 1])
with col2:
time_view = st.selectbox("Time Period", ["daily", "weekly", "monthly"], key="time_view_select")
with col1:
with st.container():
st.markdown('', unsafe_allow_html=True)
total_chart = create_total_production_chart(df, time_view)
st.plotly_chart(total_chart, use_container_width=True)
st.markdown('
', unsafe_allow_html=True)
st.markdown('', unsafe_allow_html=True)
col1, col2 = st.columns([3, 1])
with col2:
selected_materials = st.multiselect(
"Select Materials",
options=materials,
default=materials,
key="materials_select"
)
with col1:
if selected_materials:
with st.container():
st.markdown('', unsafe_allow_html=True)
materials_chart = create_materials_trend_chart(df, time_view, selected_materials)
st.plotly_chart(materials_chart, use_container_width=True)
st.markdown('
', unsafe_allow_html=True)
if 'shift' in df.columns:
st.markdown('', unsafe_allow_html=True)
with st.container():
st.markdown('', unsafe_allow_html=True)
shift_chart = create_shift_trend_chart(df, time_view)
st.plotly_chart(shift_chart, use_container_width=True)
st.markdown('
', unsafe_allow_html=True)
st.markdown('', unsafe_allow_html=True)
outliers = detect_outliers(df)
cols = st.columns(len(outliers))
for i, (material, info) in enumerate(outliers.items()):
with cols[i]:
if info['count'] > 0:
# Show all dates for outliers
dates_str = ", ".join(info['dates'])
st.markdown(f'''