Update app.py

app.py

@@ -7,6 +7,355 @@ import matplotlib.pyplot as plt
 import io
 import base64
 
+# ✅ Mapping of best ARIMA orders per part number
+# (replace with your actual best parameters from the notebook)
+BEST_ARIMA_PARAMS = {
+    "TE50011": (2, 0, 1),
+    "TE50012": (2, 1, 1),
+    "TE50013": (1, 1, 1),
+    "TE50014": (0, 0, 0),
+    "TE50015": (0, 1, 0),
+    "TE50016": (0, 1, 1),
+    "TE50017": (2, 1, 0),
+    "TE50018": (1, 1, 2),
+    "TE50019": (2, 1, 0),
+    "TE50020": (2, 1, 1),
+    "TE50021": (0, 0, 0),
+    "TE50022": (2, 1, 2),
+    "TE50023": (1, 0, 0),
+    "TE50024": (1, 1, 2),
+    "TE50025": (1, 1, 0),
+    "TE50026": (2, 0, 0),
+    "TE50027": (0, 0, 2),
+    "TE50028": (1, 0, 1),
+    "TE50029": (0, 1, 0),
+    "TE50030": (0, 1, 0),
+    "TE50031": (1, 1, 0),
+    "TE50032": (1, 1, 0),
+    "TE50033": (2, 1, 1),
+    "TE50034": (0, 0, 0),
+    "TE50035": (2, 1, 0),
+    "TE50036": (0, 1, 0),
+    "TE50037": (0, 0, 2),
+    "TE50038": (2, 1, 1),
+    "TE50039": (2, 0, 1),
+    "TE50040": (1, 1, 1),
+    "TE50041": (1, 1, 0),
+    "TE50042": (0, 1, 0),
+    "TE50043": (2, 1, 0),
+    "TE50044": (0, 1, 0),
+    "TE50045": (1, 0, 0),
+    "TE50046": (0, 1, 0),
+    "TE50047": (1, 1, 2),
+    "TE50048": (0, 1, 0),
+    "TE50049": (2, 1, 2),
+    "TE50050": (2, 0, 2),
+    "TE50051": (0, 1, 0),
+    "TE50052": (2, 0, 1),
+    "TE50053": (1, 0, 2),
+    "TE50054": (1, 1, 0),
+    "TE50055": (0, 0, 0),
+    "TE50056": (2, 0, 2),
+    "TE50057": (0, 1, 0),
+    "TE50058": (1, 1, 0),
+    "TE50059": (0, 1, 0),
+    "TE50060": (1, 1, 2),
+    "TE50061": (2, 1, 0),
+    "TE50062": (1, 1, 2),
+    "TE50063": (0, 0, 2),
+    "TE50064": (0, 1, 0),
+    "TE50065": (0, 1, 0),
+    "TE50066": (2, 1, 0),
+    "TE50067": (2, 1, 0),
+    "TE50068": (1, 1, 0),
+    "TE50069": (0, 1, 2),
+    "TE50070": (1, 1, 1),
+    "TE50071": (2, 1, 2),
+    "TE50072": (2, 0, 2),
+    "TE50073": (2, 0, 1),
+    "TE50074": (0, 1, 1),
+    "TE50075": (2, 1, 0),
+    "TE50076": (0, 1, 0),
+    "TE50077": (0, 1, 2),
+    "TE50078": (2, 1, 0),
+    "TE50079": (0, 1, 0),
+    "TE50080": (2, 1, 2),
+    "TE50081": (2, 1, 0),
+    "TE50082": (0, 1, 0),
+    "TE50083": (2, 1, 0),
+    "TE50084": (0, 1, 0),
+    "TE50085": (2, 1, 0),
+    "TE50086": (0, 1, 1),
+    "TE50087": (2, 0, 1),
+    "TE50088": (2, 1, 0),
+    "TE50089": (2, 0, 2),
+    "TE50090": (0, 1, 1),
+    "TE50091": (0, 1, 0),
+    "TE50092": (0, 1, 0),
+    "TE50093": (2, 0, 2),
+    "TE50094": (2, 0, 1),
+    "TE50095": (1, 0, 0),
+    "TE50096": (0, 1, 0),
+    "TE50097": (0, 1, 0),
+    "TE50098": (1, 1, 0),
+    "TE50099": (0, 1, 0),
+    "TE50100": (2, 0, 2),
+    "TE50101": (2, 0, 2),
+    "TE50102": (1, 1, 1),
+    "TE50103": (0, 0, 1),
+    "TE50104": (0, 1, 0),
+    "TE50105": (2, 0, 0),
+    "TE50106": (2, 0, 0),
+    "TE50107": (2, 0, 0),
+    "TE50108": (2, 1, 0),
+    "TE50109": (2, 1, 0),
+    "TE50110": (0, 1, 0),
+    "TE50111": (0, 1, 2),
+    "TE50112": (2, 1, 2),
+    "TE50113": (0, 1, 0),
+    "TE50114": (0, 1, 0),
+    "TE50115": (1, 1, 0),
+    "TE50116": (1, 0, 0),
+    "TE50117": (2, 1, 0),
+    "TE50118": (2, 1, 1),
+    "TE50119": (2, 1, 0),
+    "TE50120": (2, 1, 1),
+    "TE50121": (0, 1, 0),
+    "TE50122": (1, 1, 2),
+    "TE50123": (2, 1, 1),
+    "TE50124": (1, 0, 2),
+    "TE50125": (1, 0, 2),
+    "TE50126": (0, 0, 0),
+    "TE50127": (0, 1, 0),
+    "TE50128": (2, 1, 0),
+    "TE50129": (0, 1, 0),
+    "TE50130": (1, 1, 0),
+    "TE50131": (0, 1, 1),
+    "TE50132": (0, 1, 0),
+    "TE50133": (2, 0, 2),
+    "TE50134": (1, 1, 0),
+    "TE50135": (0, 1, 0),
+    "TE50136": (0, 1, 2),
+    "TE50137": (2, 0, 0),
+    "TE50138": (2, 1, 0),
+    "TE50139": (0, 1, 0),
+    "TE50140": (0, 1, 0),
+    "TE50141": (0, 1, 0),
+    "TE50142": (2, 1, 2),
+    "TE50143": (0, 1, 0),
+    "TE50144": (0, 1, 0),
+    "TE50145": (1, 0, 2),
+    "TE50146": (0, 0, 0),
+    "TE50147": (0, 0, 2),
+    "TE50148": (2, 0, 2),
+    "TE50149": (1, 0, 2),
+    "TE50150": (2, 1, 1),
+    "TE50151": (2, 0, 2),
+    "TE50152": (2, 0, 1),
+    "TE50153": (2, 0, 2),
+    "TE50154": (2, 0, 2),
+    "TE50155": (0, 0, 0),
+    "TE50156": (0, 0, 0),
+    "TE50157": (0, 1, 2),
+    "TE50158": (1, 0, 2),
+    "TE50159": (2, 1, 0),
+    "TE50160": (2, 1, 0),
+    "TE50161": (2, 0, 2),
+    "TE50162": (0, 1, 0),
+    "TE50163": (0, 1, 0),
+    "TE50164": (1, 0, 0),
+    "TE50165": (0, 1, 0),
+    "TE50166": (2, 0, 2),
+    "TE50167": (0, 1, 2),
+    "TE50168": (0, 1, 0),
+    "TE50169": (0, 1, 0),
+    "TE50170": (1, 1, 0),
+    "TE50171": (2, 1, 2),
+    "TE50172": (2, 1, 2),
+    "TE50173": (0, 1, 0),
+    "TE50174": (2, 1, 0),
+    "TE50175": (0, 1, 0),
+    "TE50176": (2, 0, 2),
+    "TE50177": (2, 0, 2),
+    "TE50178": (1, 1, 0),
+    "TE50179": (1, 0, 1),
+    "TE50180": (0, 1, 0),
+    "TE50181": (0, 1, 0),
+    "TE50182": (1, 1, 0),
+    "TE50183": (2, 1, 1),
+    "TE50184": (0, 1, 0),
+    "TE50185": (1, 1, 0),
+    "TE50186": (2, 0, 2),
+    "TE50187": (0, 1, 0),
+    "TE50188": (0, 1, 1),
+    "TE50189": (2, 0, 2),
+    "TE50190": (1, 1, 0),
+    "TE50191": (0, 1, 0),
+    "TE50192": (0, 0, 1),
+    "TE50193": (1, 1, 0),
+    "TE50194": (2, 1, 0),
+    "TE50195": (2, 0, 2),
+    "TE50196": (0, 1, 0),
+    "TE50197": (0, 0, 1),
+    "TE50198": (0, 1, 0),
+    "TE50199": (0, 1, 0),
+    "TE50200": (0, 1, 0),
+    "TE50201": (0, 1, 0),
+    "TE50202": (0, 1, 0),
+    "TE50203": (0, 1, 0),
+    "TE50204": (2, 1, 2),
+    "TE50205": (2, 0, 1),
+    "TE50206": (1, 1, 1),
+    "TE50207": (2, 0, 0),
+    "TE50208": (0, 1, 0),
+    "TE50209": (2, 0, 2),
+    "TE50210": (0, 1, 0),
+    "TE50211": (0, 1, 0),
+    "TE50212": (0, 1, 0),
+    "TE50213": (0, 1, 0),
+    "TE50214": (0, 1, 0),
+    "TE50215": (0, 1, 0),
+    "TE50216": (0, 1, 0),
+    "TE50217": (0, 1, 0),
+    "TE50218": (2, 0, 1),
+    "TE50219": (2, 1, 0),
+    "TE50220": (2, 1, 0),
+    "TE50221": (1, 1, 2),
+    "TE50222": (0, 1, 0),
+    "TE50223": (0, 1, 1),
+    "TE50224": (0, 1, 0),
+    "TE50225": (0, 1, 0),
+    "TE50226": (0, 1, 0),
+    "TE50227": (0, 1, 0),
+    "TE50228": (0, 1, 0),
+    "TE50229": (0, 1, 0),
+    "TE50230": (0, 1, 0),
+    "TE50231": (0, 1, 0),
+    "TE50232": (0, 1, 1),
+    "TE50233": (1, 1, 2),
+    "TE50234": (0, 1, 0),
+    "TE50235": (0, 1, 0),
+    "TE50236": (0, 1, 0),
+    "TE50237": (0, 1, 0),
+    "TE50238": (0, 0, 2),
+    "TE50239": (0, 1, 0),
+    "TE50240": (0, 1, 0),
+    "TE50241": (0, 1, 0),
+    "TE50242": (0, 1, 0),
+    "TE50243": (0, 1, 0),
+    "TE50244": (0, 1, 0),
+    "TE50245": (0, 1, 0),
+    "TE50246": (0, 1, 0),
+    "TE50247": (0, 1, 0),
+    "TE50248": (0, 1, 0),
+    "TE50249": (0, 1, 0),
+    "TE50250": (2, 0, 1),
+    "TE50251": (2, 0, 1),
+    "TE50252": (0, 1, 0),
+    "TE50253": (0, 1, 0),
+    "TE50254": (0, 1, 0),
+    "TE50255": (0, 1, 0),
+    "TE50256": (0, 1, 0),
+    "TE50257": (0, 1, 0),
+    "TE50258": (0, 1, 0),
+    "TE50259": (0, 1, 0),
+    "TE50260": (0, 1, 0),
+    "TE50261": (0, 1, 0),
+    "TE50262": (0, 1, 0),
+    "TE50263": (0, 1, 0),
+    "TE50264": (0, 1, 0),
+    "TE50265": (0, 1, 0),
+    "TE50266": (0, 1, 0),
+    "TE50267": (0, 1, 0),
+    "TE50268": (0, 1, 0),
+    "TE50269": (0, 1, 0),
+    "TE50270": (0, 1, 0),
+    "TE50271": (0, 1, 0),
+    "TE50272": (0, 1, 0),
+    "TE50273": (0, 1, 0),
+    "TE50274": (0, 1, 0),
+    "TE50275": (0, 1, 0),
+    "TE50276": (0, 1, 0),
+    "TE50277": (0, 1, 0),
+    "TE50278": (0, 1, 0),
+    "TE50279": (0, 1, 0),
+    "TE50280": (0, 1, 0),
+    "TE50281": (0, 1, 0),
+    "TE50282": (0, 1, 0),
+    "TE50283": (0, 1, 0),
+    "TE50284": (0, 1, 0),
+    "TE50285": (0, 1, 0),
+    "TE50286": (0, 1, 0),
+    "TE50287": (0, 1, 0),
+    "TE50288": (0, 1, 0),
+    "TE50289": (0, 1, 0),
+    "TE50290": (0, 1, 0),
+    "TE50291": (0, 1, 0),
+    "TE50292": (0, 1, 0),
+    "TE50293": (0, 1, 0),
+    "TE50294": (0, 1, 0),
+    "TE50295": (0, 1, 0),
+    "TE50296": (0, 1, 0),
+    "TE50297": (0, 1, 0),
+    "TE50298": (0, 1, 0),
+    "TE50299": (0, 1, 0),
+    "TE50300": (0, 1, 0),
+    "TE50301": (0, 1, 0),
+    "TE50302": (0, 1, 0),
+    "TE50303": (0, 1, 0),
+    "TE50304": (0, 1, 0),
+    "TE50305": (0, 1, 0),
+    "TE50306": (0, 1, 0),
+    "TE50307": (0, 1, 0),
+    "TE50308": (0, 1, 0),
+    "TE50309": (0, 1, 0),
+    "TE50310": (0, 1, 0),
+    "TE50311": (0, 1, 0),
+    "TE50312": (0, 1, 0),
+    "TE50313": (0, 1, 0),
+    "TE50314": (0, 1, 0),
+    "TE50315": (0, 1, 0),
+    "TE50316": (0, 1, 0),
+    "TE50317": (0, 1, 0),
+    "TE50318": (0, 1, 0),
+    "TE50319": (0, 1, 0),
+    "TE50320": (0, 1, 0),
+    "TE50321": (0, 1, 0),
+    "TE50322": (0, 1, 0),
+    "TE50323": (0, 1, 0),
+    "TE50324": (0, 1, 0),
+    "TE50325": (0, 1, 0),
+    "TE50326": (0, 1, 0),
+    "TE50327": (0, 1, 0),
+    "TE50328": (2, 0, 2),
+    "TE50329": (0, 1, 0),
+    "TE50330": (0, 1, 0),
+    "TE50331": (0, 1, 0),
+    "TE50332": (0, 1, 0),
+    "TE50333": (0, 1, 0),
+    "TE50334": (0, 1, 0),
+    "TE50335": (0, 1, 1),
+    "TE50336": (0, 1, 1),
+    "TE50337": (0, 1, 0),
+    "TE50338": (0, 1, 0),
+    "TE50339": (0, 1, 0),
+    "TE50340": (0, 1, 0),
+    "TE50341": (0, 1, 0),
+    "TE50342": (0, 1, 0),
+    "TE50343": (0, 1, 0),
+    "TE50344": (0, 1, 0),
+    "TE50345": (0, 1, 0),
+    "TE50346": (0, 1, 0),
+    "TE50346": (0, 1, 0),
+    "TE50347": (0, 1, 0),
+    "TE50348": (0, 1, 0),
+    "TE50349": (0, 1, 0),
+    "TE50350": (0, 1, 0),
+    "TE50351": (0, 1, 0),
+    "TE50352": (0, 1, 0),
+    "TE50353": (0, 1, 0)
+}
+
 # Load and preprocess data
 def load_data():
     df = pd.read_csv("data (3).csv", skiprows=2, header=None)
@@ -54,13 +403,11 @@ def train_arima(series, order=(5,1,0)):
     forecast = model_fit.forecast(steps=10)
     return model_fit, forecast
 
-# Create plot
 # Create plot
 def create_plot(historical, forecast, freq='M'):
-    plt.figure(figsize=(14, 7))  # bigger figure
+    plt.figure(figsize=(14, 7))  
     plt.plot(historical.index, historical, label='Historical', linewidth=2)
     
-    # Generate forecast index
     forecast_index = pd.date_range(
         start=historical.index[-1] + pd.tseries.frequencies.to_offset(freq),
         periods=len(forecast),
@@ -74,7 +421,6 @@ def create_plot(historical, forecast, freq='M'):
     plt.ylabel('Value', fontsize=14)
     plt.grid(True, alpha=0.3)
     
-    # Convert plot to base64 for Gradio
     buf = io.BytesIO()
     plt.savefig(buf, format='png', bbox_inches="tight")
     buf.seek(0)
@@ -82,45 +428,44 @@ def create_plot(historical, forecast, freq='M'):
     buf.close()
     plt.close()
     
-    # Force full width in Gradio
     return f'<img src="data:image/png;base64,{img_str}" style="width:100%; height:auto;" />'
 
-
 # Main prediction function
 def predict(part_number, model_name):
     df = load_data()
     df_part = df[df['PN'] == part_number].copy()
     
-    # Prepare time series data
+    # Prepare time series
     start_date = '2021-10-09'
     date_range = pd.date_range(start=start_date, periods=len(df_part), freq='W')
     df_part['Date'] = date_range
     df_part.set_index('Date', inplace=True)
     series = df_part['y'].astype(float)
 
-    # Detect frequency automatically
     freq = pd.infer_freq(series.index) or 'M'
 
+    # ✅ Choose ARIMA order from mapping (fallback = (5,1,0))
+    order = BEST_ARIMA_PARAMS.get(part_number, (5,1,0))
+    
     if model_name == 'ARIMA':
-        model, forecast = train_arima(series)
+        model, forecast = train_arima(series, order=order)
 
     plot_html = create_plot(series, forecast, freq=freq)
 
-    
-    # Calculate metrics
+    # Evaluate
     train_size = int(len(series) * 0.8)
     train, test = series[:train_size], series[train_size:]
     
-    model_eval = ARIMA(train, order=(5,1,0))
+    model_eval = ARIMA(train, order=order)
     model_fit_eval = model_eval.fit()
     predictions = model_fit_eval.forecast(steps=len(test))
     
     rmse = np.sqrt(mean_squared_error(test, predictions))
     mae = mean_absolute_error(test, predictions)
     
-    plot_html = create_plot(series, forecast)
-    
     metrics = f"""
+    Best ARIMA Order for {part_number}: {order}
+    
     Model Performance Metrics:
     - RMSE: {rmse:.2f}
     - MAE: {mae:.2f}
@@ -159,4 +504,4 @@ def create_interface():
 
 if __name__ == "__main__":
     demo = create_interface()
-    demo.launch()
+    demo.launch()