Update app.py

app.py

@@ -7,7 +7,7 @@ from datetime import datetime, timedelta
 import google.generativeai as genai
 
 # Page config
-st.set_page_config(page_title="Production Monitor", page_icon="🏭", layout="wide")
+st.set_page_config(page_title="Production Data Monitor Platform – Nilsen Service & Consulting AS", page_icon="🏭", layout="wide")
 
 @st.cache_resource
 def init_ai():
@@ -27,12 +27,10 @@ def load_data(file):
 def get_material_stats(df):
     stats = {}
     total = df['weight_kg'].sum()
-
     total_work_days = df['date'].nunique()
     
     for material in df['material_type'].unique():
         data = df[df['material_type'] == material]
-
         work_days = data['date'].nunique()
         daily_avg = data.groupby('date')['weight_kg'].sum().mean()
         
@@ -54,56 +52,58 @@ def get_material_stats(df):
     
     return stats
 
-def display_material_overview(stats):
-    st.subheader("📋 Material Overview")
-    
-
-    materials = [k for k in stats.keys() if k != '_total_']
-    
-
-    cols = st.columns(4)
-    
-
-    for i, material in enumerate(materials):
-        if i < 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")
-                st.caption(f"Work days: {info['work_days']} days")
+def create_total_production_chart(df, time_period='daily'):
+    """Create total production trend chart"""
+    if time_period == 'daily':
+        grouped = df.groupby('date')['weight_kg'].sum().reset_index()
+        fig = px.line(grouped, x='date', y='weight_kg', 
+                     title='📊 Total Production Trend',
+                     labels={'weight_kg': 'Weight (kg)', 'date': 'Date'})
+    elif time_period == 'weekly':
+        df['week'] = df['date'].dt.isocalendar().week
+        df['year'] = df['date'].dt.year
+        grouped = df.groupby(['year', 'week'])['weight_kg'].sum().reset_index()
+        grouped['week_label'] = grouped['year'].astype(str) + '-W' + grouped['week'].astype(str)
+        fig = px.bar(grouped, x='week_label', y='weight_kg',
+                    title='📊 Total Production Trend (Weekly)',
+                    labels={'weight_kg': 'Weight (kg)', 'week_label': 'Week'})
+    else:  # monthly
+        df['month'] = df['date'].dt.to_period('M')
+        grouped = df.groupby('month')['weight_kg'].sum().reset_index()
+        grouped['month'] = grouped['month'].astype(str)
+        fig = px.bar(grouped, x='month', y='weight_kg',
+                    title='📊 Total Production Trend (Monthly)',
+                    labels={'weight_kg': 'Weight (kg)', 'month': 'Month'})
     
+    fig.update_layout(height=400, showlegend=False)
+    fig.update_traces(line_color='#1f77b4' if time_period == 'daily' else None)
+    return fig
 
-    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.caption(f"Work days: {total_info['work_days']} days")
-
-def create_trend_chart(df, time_period='daily', material_filter=None):
-    if material_filter:
-        df = df[df['material_type'].isin(material_filter)]
+def create_materials_trend_chart(df, time_period='daily', selected_materials=None):
+    """Create individual materials trend chart"""
+    if selected_materials:
+        df = df[df['material_type'].isin(selected_materials)]
     
     if time_period == 'daily':
         grouped = df.groupby(['date', 'material_type'])['weight_kg'].sum().reset_index()
         fig = px.line(grouped, x='date', y='weight_kg', color='material_type',
-                     title='Daily Production Trend')
-    elif time_period == 'shift':
-        grouped = df.groupby(['date', 'shift', 'material_type'])['weight_kg'].sum().reset_index()
-        fig = px.bar(grouped, x='date', y='weight_kg', color='shift', 
-                    facet_col='material_type', title='Production by Shift')
-    else:  # weekly
+                     title='🏷️ Materials Production Trends',
+                     labels={'weight_kg': 'Weight (kg)', 'date': 'Date', 'material_type': 'Material'})
+    elif time_period == 'weekly':
         df['week'] = df['date'].dt.isocalendar().week
-        grouped = df.groupby(['week', 'material_type'])['weight_kg'].sum().reset_index()
-        fig = px.bar(grouped, x='week', y='weight_kg', color='material_type',
-                    title='Weekly Production')
+        df['year'] = df['date'].dt.year
+        grouped = df.groupby(['year', 'week', 'material_type'])['weight_kg'].sum().reset_index()
+        grouped['week_label'] = grouped['year'].astype(str) + '-W' + grouped['week'].astype(str)
+        fig = px.bar(grouped, x='week_label', y='weight_kg', color='material_type',
+                    title='🏷️ Materials Production Trends (Weekly)',
+                    labels={'weight_kg': 'Weight (kg)', 'week_label': 'Week', 'material_type': 'Material'})
+    else:  # monthly
+        df['month'] = df['date'].dt.to_period('M')
+        grouped = df.groupby(['month', 'material_type'])['weight_kg'].sum().reset_index()
+        grouped['month'] = grouped['month'].astype(str)
+        fig = px.bar(grouped, x='month', y='weight_kg', color='material_type',
+                    title='🏷️ Materials Production Trends (Monthly)',
+                    labels={'weight_kg': 'Weight (kg)', 'month': 'Month', 'material_type': 'Material'})
     
     fig.update_layout(height=400)
     return fig
@@ -129,7 +129,7 @@ def query_ai(model, stats, question):
     
     context = f"""Production Data Summary:
     {chr(10).join([f"- {mat.title()}: {info['total']:,.0f}kg ({info['percentage']:.1f}%)" 
-                  for mat, info in stats.items()])}
+                  for mat, info in stats.items() if mat != '_total_'])}
     
     Question: {question}
     Answer concisely based on the data:"""
@@ -161,11 +161,13 @@ def main():
         df = load_data(uploaded_file)
         stats = get_material_stats(df)
         
-        # Material cards
+        # Material Overview
         st.subheader("📋 Material Overview")
-        cols = st.columns(len(stats))
+        materials = [k for k in stats.keys() if k != '_total_']
         
-        for i, (material, info) in enumerate(stats.items()):
+        cols = st.columns(4)
+        for i, material in enumerate(materials[:3]):
+            info = stats[material]
             with cols[i]:
                 st.metric(
                     label=material.replace('_', ' ').title(),
@@ -173,33 +175,87 @@ def main():
                     delta=f"{info['percentage']:.1f}% of total"
                 )
                 st.caption(f"Daily avg: {info['daily_avg']:,.0f} kg")
+                st.caption(f"Work days: {info['work_days']} days")
+        
+        # Total production metric
+        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.caption(f"Work days: {total_info['work_days']} days")
+        
+        # Production Trends Section
+        st.subheader("📈 Production Trends")
         
         # Chart controls
-        st.subheader("📈 Trends")
-        col1, col2 = st.columns([3, 1])
-        
-        with col2:
-            time_view = st.selectbox("Time View", ["daily", "weekly", "shift"])
-            materials = st.multiselect(
-                "Materials", 
-                options=list(stats.keys()),
-                default=list(stats.keys())
+        col_ctrl1, col_ctrl2, col_ctrl3 = st.columns([2, 2, 3])
+        
+        with col_ctrl1:
+            time_view = st.selectbox("Time Period", ["daily", "weekly", "monthly"])
+        
+        with col_ctrl2:
+            chart_type = st.selectbox("View Type", ["both", "total_only", "materials_only"])
+        
+        with col_ctrl3:
+            selected_materials = st.multiselect(
+                "Select Materials", 
+                options=materials,
+                default=materials,
+                help="Choose which materials to display in trends"
             )
         
-        with col1:
-            if materials:
-                chart = create_trend_chart(df, time_view, materials)
-                st.plotly_chart(chart, use_container_width=True)
+        # Display charts based on selection
+        if chart_type == "both":
+            chart_col1, chart_col2 = st.columns(2)
+            
+            with chart_col1:
+                total_chart = create_total_production_chart(df, time_view)
+                st.plotly_chart(total_chart, use_container_width=True)
+            
+            with chart_col2:
+                if selected_materials:
+                    materials_chart = create_materials_trend_chart(df, time_view, selected_materials)
+                    st.plotly_chart(materials_chart, use_container_width=True)
+                else:
+                    st.info("Please select materials to display trends")
+        
+        elif chart_type == "total_only":
+            total_chart = create_total_production_chart(df, time_view)
+            st.plotly_chart(total_chart, use_container_width=True)
         
-        # Shift analysis
+        else:  # materials_only
+            if selected_materials:
+                materials_chart = create_materials_trend_chart(df, time_view, selected_materials)
+                st.plotly_chart(materials_chart, use_container_width=True)
+            else:
+                st.info("Please select materials to display trends")
+        
+        # Shift Analysis
         if 'shift' in df.columns:
             st.subheader("🌓 Shift Analysis")
-            shift_data = df.groupby(['shift', 'material_type'])['weight_kg'].sum().reset_index()
-            shift_chart = px.bar(shift_data, x='shift', y='weight_kg', color='material_type',
-                               title='Production by Shift')
-            st.plotly_chart(shift_chart, use_container_width=True)
+            
+            shift_col1, shift_col2 = st.columns(2)
+            
+            with shift_col1:
+                # Shift comparison by material
+                shift_data = df.groupby(['shift', 'material_type'])['weight_kg'].sum().reset_index()
+                shift_chart = px.bar(shift_data, x='shift', y='weight_kg', color='material_type',
+                                   title='Production by Shift and Material',
+                                   labels={'weight_kg': 'Weight (kg)', 'shift': 'Shift', 'material_type': 'Material'})
+                st.plotly_chart(shift_chart, use_container_width=True)
+            
+            with shift_col2:
+                # Total production by shift
+                shift_total = df.groupby('shift')['weight_kg'].sum().reset_index()
+                shift_total_chart = px.pie(shift_total, values='weight_kg', names='shift',
+                                         title='Total Production Distribution by Shift')
+                st.plotly_chart(shift_total_chart, use_container_width=True)
         
-        # Anomaly detection
+        # Quality Check
         st.subheader("⚠️ Quality Check")
         outliers = detect_outliers(df)
         
@@ -208,7 +264,7 @@ def main():
             with alert_cols[i]:
                 if info['count'] > 0:
                     st.warning(f"**{material.title()}**: {info['count']} outliers")
-                    st.caption(f"Normal: {info['range']}")
+                    st.caption(f"Normal range: {info['range']}")
                 else:
                     st.success(f"**{material.title()}**: All normal")
         
@@ -217,35 +273,35 @@ def main():
             st.subheader("🤖 AI Insights")
             
             # Quick questions
-            quick_q = [
+            quick_questions = [
                 "What's the production trend?",
                 "Which material is most consistent?",
                 "Any efficiency recommendations?"
             ]
             
-            cols = st.columns(len(quick_q))
-            for i, q in enumerate(quick_q):
+            cols = st.columns(len(quick_questions))
+            for i, q in enumerate(quick_questions):
                 with cols[i]:
-                    if st.button(q, key=f"q{i}"):
+                    if st.button(q, key=f"quick_q_{i}"):
                         answer = query_ai(model, stats, q)
                         st.info(answer)
             
             # Custom question
-            custom_q = st.text_input("Ask anything about your data:")
-            if custom_q:
-                if st.button("Ask"):
-                    answer = query_ai(model, stats, custom_q)
-                    st.success(f"**Q:** {custom_q}")
-                    st.write(f"**A:** {answer}")
+            custom_question = st.text_input("Ask anything about your production data:")
+            if custom_question:
+                if st.button("Ask AI"):
+                    answer = query_ai(model, stats, custom_question)
+                    st.success(f"**Question:** {custom_question}")
+                    st.write(f"**Answer:** {answer}")
     
     else:
-        st.info("📁 Upload your production data to start")
+        st.info("📁 Upload your production data to start analysis")
         st.markdown("""
-        **Expected format (TSV):**
-        - `date`: MM/DD/YYYY
-        - `weight_kg`: Production weight
-        - `material_type`: Material category
-        - `shift`: day/night (optional)
+        **Expected CSV format:**
+        - `date`: MM/DD/YYYY format
+        - `weight_kg`: Production weight in kilograms
+        - `material_type`: Material category/type
+        - `shift`: day/night shift (optional)
         """)
 
 if __name__ == "__main__":