Update app.py

app.py

@@ -13,58 +13,7 @@ from statsmodels.tsa.arima.model import ARIMA
 import tensorflow as tf
 import utils
 
-#from utils import predict_medicine_demand
-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']
-            
-
-            # 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)
-
-
-
+from utils import predict_medicine_demand
     
 def load_locations():
     df = pd.read_csv("medicinal_sales_data_large.csv")
@@ -90,10 +39,6 @@ if st.button("📈 Predict Demand"):
     st.info("Running prediction, please wait...")
     try:
         result_df = predict_medicine_demand(selected_location, selected_year, selected_month)
-        st.write(selected_year)
-        st.write(selected_month)
-        
-        st.write(result_df.head())
         if result_df.empty:
             st.warning("No data available for this location.")
         else: