Update utils.py

utils.py

@@ -1,64 +1,64 @@
-import streamlit as st
-import pandas as pd
-from datetime import datetime
-import joblib
-import numpy as np
-import random
-import os
-from keras.models import Sequential
-from keras.layers import Dense, LSTM
-from sklearn.preprocessing import MinMaxScaler
-from sklearn.tree import DecisionTreeRegressor
-from statsmodels.tsa.arima.model import ARIMA
-import tensorflow as tf
-import utils
-
-
-def predict_medicine_demand(location: str, year: int, month: int):
-    results = []
-    products=('Medicine_4', 'Medicine_10', 'Medicine_5', 'Medicine_7','Medicine_3', 'Medicine_2', 'Medicine_8', 'Medicine_1','Medicine_6', 'Medicine_9')
-    for product in products:
-        try:
-            monthly_data=pd.read_csv('monthly_data.csv')
-            # Get last 3 values from the data
-            ts_df = monthly_data[(monthly_data['product'] == product) & (monthly_data['location'] == location)].copy()
-            ts_df.set_index('date', inplace=True)
-            ts_df = ts_df.sort_index()
-            ts = ts_df['quantity']
-            if len(ts) < 6:
-                continue
-
-            # Load ARIMA model
-            arima_model = joblib.load(f"models/arima_{product}_{location}.pkl")
-            arima_forecast = arima_model.forecast(steps=1)[0]
-
-            # Load ANN
-            ann_model = tf.keras.models.load_model(f"models/ann_{product}_{location}.h5")
-            scaler_X = joblib.load(f"models/ann_scalerX_{product}_{location}.pkl")
-            scaler_y = joblib.load(f"models/ann_scalerY_{product}_{location}.pkl")
-
-            last_3 = ts[-3:].values.reshape(1, -1)
-            last_scaled = scaler_X.transform(last_3)
-            ann_pred_scaled = ann_model.predict(last_scaled, verbose=0)
-            ann_forecast = scaler_y.inverse_transform(ann_pred_scaled)[0][0]
-
-            # Load LSTM
-            lstm_model = tf.keras.models.load_model(f"models/lstm_{product}_{location}.h5")
-            lstm_scaler = joblib.load(f"models/lstm_scaler_{product}_{location}.pkl")
-
-            series_scaled = lstm_scaler.transform(ts.values.reshape(-1, 1)).flatten()
-            last_seq = series_scaled[-3:].reshape(1, 3, 1)
-            lstm_pred_scaled = lstm_model.predict(last_seq, verbose=0)[0][0]
-            lstm_forecast = lstm_scaler.inverse_transform([[lstm_pred_scaled]])[0][0]
-
-            results.append({
-                "Product": product,
-                "ARIMA Pred": round(arima_forecast, 2),
-                "ANN Pred": round(ann_forecast, 2),
-                "LSTM Pred": round(lstm_forecast, 2),
-                "Average Requirement":round((arima_forecast+ann_forecast+lstm_forecast)/3,2)
-            })
-
-        except Exception as e:
-            print(f"Failed to predict for {product} - {location}: {e}")
-    return pd.DataFrame(results)
+import streamlit as st
+import pandas as pd
+from datetime import datetime
+import joblib
+import numpy as np
+import random
+import os
+from keras.models import Sequential
+from keras.layers import Dense, LSTM
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.tree import DecisionTreeRegressor
+from statsmodels.tsa.arima.model import ARIMA
+import tensorflow as tf
+import utils
+
+monthly_data=pd.read_csv('monthly_data.csv')
+def predict_medicine_demand(location: str, year: int, month: int):
+    results = []
+    products=('Medicine_4', 'Medicine_10', 'Medicine_5', 'Medicine_7','Medicine_3', 'Medicine_2', 'Medicine_8', 'Medicine_1','Medicine_6', 'Medicine_9')
+    for product in products:
+        try:
+            
+            # Get last 3 values from the data
+            ts_df = monthly_data[(monthly_data['product'] == product) & (monthly_data['location'] == location)].copy()
+            ts_df.set_index('date', inplace=True)
+            ts_df = ts_df.sort_index()
+            ts = ts_df['quantity']
+            if len(ts) < 6:
+                continue
+
+            # Load ARIMA model
+            arima_model = joblib.load(f"models/arima_{product}_{location}.pkl")
+            arima_forecast = arima_model.forecast(steps=1)[0]
+
+            # Load ANN
+            ann_model = tf.keras.models.load_model(f"models/ann_{product}_{location}.h5")
+            scaler_X = joblib.load(f"models/ann_scalerX_{product}_{location}.pkl")
+            scaler_y = joblib.load(f"models/ann_scalerY_{product}_{location}.pkl")
+
+            last_3 = ts[-3:].values.reshape(1, -1)
+            last_scaled = scaler_X.transform(last_3)
+            ann_pred_scaled = ann_model.predict(last_scaled, verbose=0)
+            ann_forecast = scaler_y.inverse_transform(ann_pred_scaled)[0][0]
+
+            # Load LSTM
+            lstm_model = tf.keras.models.load_model(f"models/lstm_{product}_{location}.h5")
+            lstm_scaler = joblib.load(f"models/lstm_scaler_{product}_{location}.pkl")
+
+            series_scaled = lstm_scaler.transform(ts.values.reshape(-1, 1)).flatten()
+            last_seq = series_scaled[-3:].reshape(1, 3, 1)
+            lstm_pred_scaled = lstm_model.predict(last_seq, verbose=0)[0][0]
+            lstm_forecast = lstm_scaler.inverse_transform([[lstm_pred_scaled]])[0][0]
+
+            results.append({
+                "Product": product,
+                "ARIMA Pred": round(arima_forecast, 2),
+                "ANN Pred": round(ann_forecast, 2),
+                "LSTM Pred": round(lstm_forecast, 2),
+                "Average Requirement":round((arima_forecast+ann_forecast+lstm_forecast)/3,2)
+            })
+
+        except Exception as e:
+            print(f"Failed to predict for {product} - {location}: {e}")
+    return pd.DataFrame(results)