Update app.py

app.py

@@ -1,133 +1,167 @@
 import streamlit as st
 import pandas as pd
+import pickle
 from prophet import Prophet
 from datetime import datetime, timedelta
+import numpy as np
 import plotly.graph_objects as go
 
+# Load the trained models (optional, for initialization or fallback)
+@st.cache_resource
+def load_model():
+    try:
+        with open('model.pkl', 'rb') as f:
+            models = pickle.load(f)
+        return models
+    except FileNotFoundError:
+        return None
+
 # Prepare data for Prophet
 def prepare_prophet_data(usage_series):
+    # Create a date range for the last 60 days
     end_date = datetime.now()
     start_date = end_date - timedelta(days=len(usage_series) - 1)
     dates = [start_date + timedelta(days=i) for i in range(len(usage_series))]
     
-    return pd.DataFrame({'ds': dates, 'y': usage_series})
-
-# Train model with fixed low sensitivity
-def train_model(usage_series):
-    df = prepare_prophet_data(usage_series)
-    model = Prophet(
-        yearly_seasonality=False,
-        weekly_seasonality=True,
-        daily_seasonality=True,
-        changepoint_prior_scale=0.05,  # Low sensitivity to recent changes
-        changepoint_range=0.6
-    )
-    model.fit(df)
+    # Create Prophet-compatible DataFrame
+    prophet_df = pd.DataFrame({
+        'ds': dates,
+        'y': usage_series
+    })
+    return prophet_df
+
+# Train or update Prophet model with user-provided usage series
+def train_model_with_usage(usage_series):
+    prophet_df = prepare_prophet_data(usage_series)
+    model = Prophet(yearly_seasonality=False, weekly_seasonality=True, daily_seasonality=True)
+    model.fit(prophet_df)
     return model
 
-# Forecast future usage
+# Function to make forecasts
 def make_forecast(model, periods):
     future = model.make_future_dataframe(periods=periods)
     forecast = model.predict(future)
-    return forecast, round(forecast['yhat'].tail(periods).sum())
+    return round(forecast['yhat'].tail(periods).sum())  # Round to nearest integer
 
-# Validate input
+# Function to validate input
 def validate_usage_series(usage_str):
     try:
-        usage = [float(x) for x in usage_str.split(',')]
-        if len(usage) != 60:
-            return None, "Please enter exactly 60 values."
-        if any(x < 0 for x in usage):
+        usage_list = [float(x) for x in usage_str.split(',')]
+        if len(usage_list) != 60:
+            return None, "Usage series must contain exactly 60 values."
+        if any(x < 0 for x in usage_list):
             return None, "Usage values must be non-negative."
-        return usage, None
+        return usage_list, None
     except:
-        return None, "Invalid format. Use 60 comma-separated numbers."
+        return None, "Invalid usage series format. Please enter 60 comma-separated numbers."
 
-# Main app
+# Main Streamlit app
 def main():
     st.title("SmartLab Consumables Forecast")
-
+    
+    # Load pre-trained models (optional, for reference)
+    models = load_model()
+    
+    # Input form
     st.header("Input Parameters")
     consumable_type = st.selectbox("Consumable Type", ['Filters', 'Reagents', 'Vials'])
-    usage_input = st.text_input("Last 60 Days Usage (comma-separated)", "")
+    usage_series = st.text_input("Last 60 Days Usage (comma-separated)", "")
     current_stock = st.number_input("Current Stock", min_value=0, value=0)
-
+    
     if st.button("Generate Forecast"):
-        usage_series, error = validate_usage_series(usage_input)
+        # Validate inputs
+        usage_list, error = validate_usage_series(usage_series)
         if error:
             st.error(error)
             return
-
-        model = train_model(usage_series)
-        forecast_df, forecast_7 = make_forecast(model, 7)
-        _, forecast_14 = make_forecast(model, 14)
-        _, forecast_30 = make_forecast(model, 30)
-
+        
+        # Train a new model with the user-provided usage series
+        try:
+            model = train_model_with_usage(usage_list)
+        except Exception as e:
+            st.error(f"Error training model: {str(e)}")
+            return
+        
+        # Forecast for 7, 14, and 30 days
+        forecast_7 = make_forecast(model, 7)
+        forecast_14 = make_forecast(model, 14)
+        forecast_30 = make_forecast(model, 30)
+        
+        # Display forecasts
         st.header("Forecast Results")
-        st.write(f"📊 **7-Day Forecast**: {forecast_7} units")
-        st.write(f"📊 **14-Day Forecast**: {forecast_14} units")
-        st.write(f"📊 **30-Day Forecast**: {forecast_30} units")
-
-        st.header("Threshold Alerts 🚨")
-        alert_7 = current_stock < forecast_7
-        alert_14 = current_stock < forecast_14
-        alert_30 = current_stock < forecast_30
-
-        if alert_7:
-            st.warning(f"🚩 Current stock ({current_stock}) is below 7-day forecast ({forecast_7})")
-        if alert_14:
-            st.warning(f"🚩 Current stock ({current_stock}) is below 14-day forecast ({forecast_14})")
-        if alert_30:
-            st.warning(f"🚩 Current stock ({current_stock}) is below 30-day forecast ({forecast_30})")
-
-        st.header("Order Suggestions 🛒")
-        st.write(f"➡️ **7 Days**: Order {max(0, forecast_7 - current_stock)} units")
-        st.write(f"➡️ **14 Days**: Order {max(0, forecast_14 - current_stock)} units")
-        st.write(f"➡️ **30 Days**: Order {max(0, forecast_30 - current_stock)} units")
-
-        # Forecast trend line
-        st.header("Forecast Trend")
-        fig_curve = go.Figure()
-        fig_curve.add_trace(go.Scatter(
-            x=forecast_df['ds'],
-            y=forecast_df['yhat'],
-            mode='lines',
-            name='Predicted Usage',
-            line=dict(color='blue')
+        st.write(f"**7-Day Forecast**: {forecast_7} units")
+        st.write(f"**14-Day Forecast**: {forecast_14} units")
+        st.write(f"**30-Day Forecast**: {forecast_30} units")
+        
+        # Threshold alerting
+        st.header("Threshold Alerts")
+        if current_stock < forecast_7:
+            st.warning(f"Alert: Current stock ({current_stock}) is below 7-day forecast ({forecast_7}). 🚩")
+        if current_stock < forecast_14:
+            st.warning(f"Alert: Current stock ({current_stock}) is below 14-day forecast ({forecast_14}). 🚩")
+        if current_stock < forecast_30:
+            st.warning(f"Alert: Current stock ({current_stock}) is below 30-day forecast ({forecast_30}). 🚩")
+        
+        # Order suggestions
+        st.header("Order Suggestions")
+        order_7 = max(0, round(forecast_7 - current_stock))  # Round to nearest integer
+        order_14 = max(0, round(forecast_14 - current_stock))  # Round to nearest integer
+        order_30 = max(0, round(forecast_30 - current_stock))  # Round to nearest integer
+        
+        st.write(f"**For 7 Days**: Order {order_7} additional units.")
+        st.write(f"**For 14 Days**: Order {order_14} additional units.")
+        st.write(f"**For 30 Days**: Order {order_30} additional units.")
+        
+        # Graphical representation for forecast
+        st.header("Forecast Visualization")
+        forecast_data = pd.DataFrame({
+            'Period': ['7 Days', '14 Days', '30 Days'],
+            'Units': [forecast_7, forecast_14, forecast_30]
+        })
+        fig_forecast = go.Figure()
+        fig_forecast.add_trace(go.Scatter(
+            x=forecast_data['Period'],
+            y=forecast_data['Units'],
+            mode='lines+markers',
+            name='Forecasted Units',
+            line=dict(color='blue'),
+            marker=dict(size=10)
         ))
-        fig_curve.update_layout(
-            title="Forecast Curve",
-            xaxis_title="Date",
-            yaxis_title="Units",
-            template="plotly_white"
+        fig_forecast.update_layout(
+            title='Consumable Usage Forecast',
+            xaxis_title='Time Period',
+            yaxis_title='Units',
+            template='plotly_white'
         )
-        st.plotly_chart(fig_curve)
-
-        # Threshold bar chart
-        st.header("Stock vs Forecast 🚦")
+        st.plotly_chart(fig_forecast)
+        
+        # Graphical representation for threshold alerts
+        st.header("Threshold Alerts Visualization")
         alert_data = pd.DataFrame({
             'Category': ['Current Stock', '7-Day Forecast', '14-Day Forecast', '30-Day Forecast'],
             'Units': [current_stock, forecast_7, forecast_14, forecast_30],
-            'Flag': [False, alert_7, alert_14, alert_30]
+            'Alert': [
+                False,
+                current_stock < forecast_7,
+                current_stock < forecast_14,
+                current_stock < forecast_30
+            ]
         })
-        colors = ['green'] + ['red' if a else 'blue' for a in alert_data['Flag'][1:]]
-        flags = ["" if not f else "🚩" for f in alert_data['Flag']]
-
-        fig_alert = go.Figure()
-        fig_alert.add_trace(go.Bar(
+        fig_alerts = go.Figure()
+        fig_alerts.add_trace(go.Bar(
             x=alert_data['Category'],
             y=alert_data['Units'],
-            marker_color=colors,
-            text=flags,
+            marker_color=['green'] + ['red' if alert else 'blue' for alert in alert_data['Alert'][1:]],
+            text=[f"🚩" if alert else "" for alert in alert_data['Alert']],
             textposition='auto'
         ))
-        fig_alert.update_layout(
-            title="Threshold Alerts Overview",
-            xaxis_title="Category",
-            yaxis_title="Units",
-            template="plotly_white"
+        fig_alerts.update_layout(
+            title='Stock vs Forecast with Alerts (🚩 indicates low stock)',
+            xaxis_title='Category',
+            yaxis_title='Units',
+            template='plotly_white'
         )
-        st.plotly_chart(fig_alert)
+        st.plotly_chart(fig_alerts)
 
 if __name__ == "__main__":
-    main()
+    main()