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| import gradio as gr | |
| import json | |
| import pickle | |
| import pandas as pd | |
| import numpy as np | |
| from datetime import datetime | |
| import os | |
| class EnergyMLPredictor: | |
| def __init__(self): | |
| self.rf_model = None | |
| self.rf_preprocessor = None | |
| self.xgb_model = None | |
| self.xgb_encoders = None | |
| self.threshold_model_83 = None | |
| self.threshold_model_90 = None | |
| self.threshold_preprocessor = None | |
| self.models_loaded = False | |
| def load_models(self): | |
| """Load all models from pickle files""" | |
| try: | |
| # Load Random Forest Energy Model | |
| if os.path.exists('rf_energy_model.pkl'): | |
| with open('rf_energy_model.pkl', 'rb') as f: | |
| rf_data = pickle.load(f) | |
| self.rf_model = rf_data['model'] | |
| self.rf_preprocessor = rf_data['preprocessor'] | |
| # Load XGBoost Energy Model | |
| if os.path.exists('xgboost_energy_model.pkl'): | |
| with open('xgboost_energy_model.pkl', 'rb') as f: | |
| xgb_data = pickle.load(f) | |
| self.xgb_model = xgb_data['model'] | |
| self.xgb_encoders = xgb_data['label_encoders'] | |
| # Load Threshold Models | |
| if os.path.exists('threshold_model_83_autoclave.pkl'): | |
| with open('threshold_model_83_autoclave.pkl', 'rb') as f: | |
| threshold_data = pickle.load(f) | |
| self.threshold_model_83 = threshold_data['model'] | |
| self.threshold_preprocessor = threshold_data['preprocessor'] | |
| if os.path.exists('threshold_model_90_autoclave.pkl'): | |
| with open('threshold_model_90_autoclave.pkl', 'rb') as f: | |
| threshold_data = pickle.load(f) | |
| self.threshold_model_90 = threshold_data['model'] | |
| self.models_loaded = True | |
| return "Models loaded successfully" | |
| except Exception as e: | |
| return f"Error loading models: {str(e)}" | |
| def predict_threshold(self, json_input): | |
| """Predict threshold exceedance""" | |
| try: | |
| if not self.models_loaded: | |
| return "Error: Models not loaded" | |
| if not self.threshold_model_83 or not self.threshold_model_90: | |
| return "Error: Threshold models not available" | |
| data = json.loads(json_input) | |
| # Handle both single object and array formats | |
| if not isinstance(data, list): | |
| data = [data] | |
| # Process all items | |
| results_83 = [] | |
| results_90 = [] | |
| for item in data: | |
| # Parse input data | |
| date_obj = datetime.strptime(item['data'], '%Y-%m-%d') | |
| # Color mapping | |
| color_mapping = {0: 'incolor', 1: 'verde', 2: 'cinza', 3: 'bronze'} | |
| if isinstance(item['cor'], str): | |
| cor_str = item['cor'].lower() | |
| else: | |
| cor_str = color_mapping.get(item['cor'], 'incolor') | |
| # Handle different field name formats for threshold | |
| boosting_val = item.get('pot_boost', item.get('ext_boosting', 3.0)) | |
| # Create input features (with autoclave) | |
| input_data = { | |
| 'boosting': boosting_val, | |
| 'espessura': item['espessura'], | |
| 'extracao_forno': item['extracao_forno'], | |
| 'porcentagem_caco': item['porcentagem_caco'], | |
| 'cor': cor_str, | |
| 'prod_e': item.get('Prod_E', item.get('prod_e', 1)), | |
| 'prod_l': item.get('Prod_L', item.get('prod_l', 1)), | |
| 'autoclave': item.get('autoclave', 1), | |
| 'week_day': date_obj.weekday(), | |
| 'month': date_obj.month, | |
| 'quarter': (date_obj.month - 1) // 3 + 1, | |
| 'is_weekend': int(date_obj.weekday() >= 5), | |
| 'week_of_year': date_obj.isocalendar()[1] | |
| } | |
| # Convert to DataFrame | |
| input_df = pd.DataFrame([input_data]) | |
| # Preprocess | |
| X_processed = self.threshold_preprocessor.transform(input_df) | |
| # Make predictions with error handling | |
| try: | |
| prob_83_raw = self.threshold_model_83.predict_proba(X_processed) | |
| prob_83 = prob_83_raw[0][1] if len(prob_83_raw[0]) > 1 else prob_83_raw[0][0] | |
| # Ensure probability is between 0 and 1 | |
| prob_83 = max(0.0, min(1.0, float(prob_83))) | |
| except Exception as e: | |
| print(f"Error with threshold_83 prediction: {e}") | |
| prob_83 = 0.0 | |
| pred_83 = int(prob_83 > 0.5) | |
| try: | |
| prob_90_raw = self.threshold_model_90.predict_proba(X_processed) | |
| prob_90 = prob_90_raw[0][1] if len(prob_90_raw[0]) > 1 else prob_90_raw[0][0] | |
| # Ensure probability is between 0 and 1 | |
| prob_90 = max(0.0, min(1.0, float(prob_90))) | |
| except Exception as e: | |
| print(f"Error with threshold_90 prediction: {e}") | |
| prob_90 = 0.0 | |
| pred_90 = int(prob_90 > 0.5) | |
| # Add to results | |
| results_83.append({ | |
| "datetime": item['data'], | |
| "probabilidade_de_estouro": round(prob_83, 4), | |
| "estouro_previsto": pred_83 | |
| }) | |
| results_90.append({ | |
| "datetime": item['data'], | |
| "probabilidade_de_estouro": round(prob_90, 4), | |
| "estouro_previsto": pred_90 | |
| }) | |
| # Format response | |
| result = { | |
| "predictions": { | |
| "prediction_1": results_83, | |
| "prediction_2": results_90 | |
| } | |
| } | |
| return json.dumps(result, indent=2) | |
| except json.JSONDecodeError: | |
| return "Error: Invalid JSON format" | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| def predict_energy_rf(self, json_input): | |
| """Predict energy using Random Forest""" | |
| try: | |
| if not self.models_loaded or not self.rf_model: | |
| return "Error: Random Forest model not available" | |
| data = json.loads(json_input) | |
| if not isinstance(data, list): | |
| data = [data] | |
| results = [] | |
| for item in data: | |
| # Parse input | |
| date_obj = datetime.strptime(item['data'], '%Y-%m-%d') | |
| # Handle different field name formats (boosting vs ext_boosting) | |
| if 'boosting' in item: | |
| boosting_val = float(str(item['boosting']).replace(',', '.')) | |
| elif 'ext_boosting' in item: | |
| boosting_val = float(str(item['ext_boosting']).replace(',', '.')) | |
| else: | |
| boosting_val = 0.0 | |
| # Handle extracao_forno field | |
| if 'extracao_forno' in item: | |
| extracao_val = float(str(item['extracao_forno']).replace(',', '.')) | |
| else: | |
| extracao_val = 800.0 | |
| # Create features | |
| input_data = { | |
| 'boosting': boosting_val, | |
| 'espessura': item['espessura'], | |
| 'extracao_forno': extracao_val, | |
| 'porcentagem_caco': item['porcentagem_caco'], | |
| 'cor': str(item['cor']).lower() if isinstance(item['cor'], str) else {0: 'incolor', 1: 'verde', 2: 'cinza', 3: 'bronze'}.get(item['cor'], 'incolor'), | |
| 'prod_e': item.get('prod_e', item.get('Prod_E', 1)), | |
| 'prod_l': item.get('prod_l', item.get('Prod_L', 1)), | |
| 'autoclave': item.get('autoclave', 1), | |
| 'week_day': date_obj.weekday(), | |
| 'month': date_obj.month, | |
| 'quarter': (date_obj.month - 1) // 3 + 1, | |
| 'is_weekend': int(date_obj.weekday() >= 5), | |
| 'week_of_year': date_obj.isocalendar()[1], | |
| 'day_of_month': date_obj.day, | |
| 'day_of_year': date_obj.timetuple().tm_yday | |
| } | |
| # Predict | |
| input_df = pd.DataFrame([input_data]) | |
| X_processed = self.rf_preprocessor.transform(input_df) | |
| prediction = self.rf_model.predict(X_processed)[0] | |
| results.append({ | |
| "data": date_obj.strftime('%d-%m-%Y'), | |
| "predictions": float(prediction) | |
| }) | |
| return json.dumps(results, indent=2) | |
| except json.JSONDecodeError: | |
| return "Error: Invalid JSON format" | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| def predict_energy_xgb(self, json_input): | |
| """Predict energy using XGBoost""" | |
| try: | |
| if not self.models_loaded or not self.xgb_model: | |
| return "Error: XGBoost model not available" | |
| data = json.loads(json_input) | |
| if not isinstance(data, list): | |
| data = [data] | |
| results = [] | |
| for item in data: | |
| # Parse input | |
| date_obj = datetime.strptime(item['data'], '%Y-%m-%d') | |
| # Handle different field name formats (boosting vs ext_boosting) | |
| if 'boosting' in item: | |
| boosting_val = float(str(item['boosting']).replace(',', '.')) | |
| elif 'ext_boosting' in item: | |
| boosting_val = float(str(item['ext_boosting']).replace(',', '.')) | |
| else: | |
| boosting_val = 0.0 | |
| # Handle extracao_forno field | |
| if 'extracao_forno' in item: | |
| extracao_val = float(str(item['extracao_forno']).replace(',', '.')) | |
| else: | |
| extracao_val = 800.0 | |
| # Create features (match training: numerical + categorical + boolean, NO day_of_month/day_of_year) | |
| input_data = { | |
| 'boosting': boosting_val, | |
| 'espessura': item['espessura'], | |
| 'extracao_forno': extracao_val, | |
| 'porcentagem_caco': item['porcentagem_caco'], | |
| 'cor': str(item['cor']).lower() if isinstance(item['cor'], str) else {0: 'incolor', 1: 'verde', 2: 'cinza', 3: 'bronze'}.get(item['cor'], 'incolor'), | |
| 'week_day': date_obj.weekday(), | |
| 'month': date_obj.month, | |
| 'quarter': (date_obj.month - 1) // 3 + 1, | |
| 'week_of_year': date_obj.isocalendar()[1], | |
| 'prod_e': item.get('prod_e', 1), | |
| 'prod_l': item.get('prod_l', 1), | |
| 'is_weekend': int(date_obj.weekday() >= 5), | |
| 'autoclave': item.get('autoclave', 1) | |
| } | |
| # Encode categorical features | |
| input_df = pd.DataFrame([input_data]) | |
| for col in input_df.columns: | |
| if col in self.xgb_encoders: | |
| try: | |
| input_df[col] = self.xgb_encoders[col].transform(input_df[col].astype(str)) | |
| except ValueError: | |
| # Handle unknown categories | |
| input_df[col] = 0 | |
| # Predict | |
| prediction = self.xgb_model.predict(input_df.values)[0] | |
| results.append({ | |
| "data": date_obj.strftime('%d-%m-%Y'), | |
| "predictions": float(prediction) | |
| }) | |
| return json.dumps(results, indent=2) | |
| except json.JSONDecodeError: | |
| return "Error: Invalid JSON format" | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| # Initialize predictor | |
| predictor = EnergyMLPredictor() | |
| def make_prediction(model_choice, json_input): | |
| """Make prediction based on model choice""" | |
| if not predictor.models_loaded: | |
| load_msg = predictor.load_models() | |
| if "Error" in load_msg: | |
| return load_msg | |
| if model_choice == "Threshold Detection": | |
| return predictor.predict_threshold(json_input) | |
| elif model_choice == "Energy Prediction (Random Forest)": | |
| return predictor.predict_energy_rf(json_input) | |
| elif model_choice == "Energy Prediction (XGBoost)": | |
| return predictor.predict_energy_xgb(json_input) | |
| else: | |
| return "Error: Please select a model" | |
| # Default examples | |
| threshold_example = """{ | |
| "data": "2023-01-01", | |
| "cor": 0, | |
| "espessura": 8.0, | |
| "ext_boosting": 65.0, | |
| "extracao_forno": 851.1, | |
| "porcentagem_caco": 15.0, | |
| "pot_boost": 3.0, | |
| "Prod_E": 1, | |
| "Prod_L": 1 | |
| }""" | |
| energy_example = """[ | |
| { | |
| "data": "2023-01-01", | |
| "boosting": "0,0", | |
| "cor": "incolor", | |
| "espessura": 10, | |
| "extracao_forno": "651,6", | |
| "porcentagem_caco": 10.0, | |
| "prod_e": 1, | |
| "prod_l": 1, | |
| "autoclave": 1 | |
| } | |
| ]""" | |
| # Test data from holdout period (last 2 months used in training) | |
| week_test_data = """[ | |
| {"data": "2025-04-19", "cor": 0, "espessura": 8.0, "ext_boosting": 1.2007, "extracao_forno": 699.561202512973, "porcentagem_caco": 10.0062724674475, "pot_boost": 3.0, "Prod_E": 1, "Prod_L": 0, "autoclave": 1}, | |
| {"data": "2025-04-20", "cor": 0, "espessura": 8.0, "ext_boosting": 1.2026, "extracao_forno": 699.169485837721, "porcentagem_caco": 9.99757589767354, "pot_boost": 3.0, "Prod_E": 1, "Prod_L": 0, "autoclave": 0}, | |
| {"data": "2025-04-21", "cor": 0, "espessura": 8.0, "ext_boosting": 1.201, "extracao_forno": 699.134346519477, "porcentagem_caco": 9.99807838764974, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 0, "autoclave": 0}, | |
| {"data": "2025-04-22", "cor": 0, "espessura": 8.0, "ext_boosting": 1.2074, "extracao_forno": 701.318973743488, "porcentagem_caco": 9.99545180216949, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 0, "autoclave": 1}, | |
| {"data": "2025-04-23", "cor": 0, "espessura": 8.0, "ext_boosting": 1.2028, "extracao_forno": 702.765143096952, "porcentagem_caco": 9.97488288777139, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 1, "autoclave": 1}, | |
| {"data": "2025-04-24", "cor": 0, "espessura": 8.0, "ext_boosting": 1.3973, "extracao_forno": 700.8439481142, "porcentagem_caco": 10.002226628142, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 1, "autoclave": 1}, | |
| {"data": "2025-04-25", "cor": 0, "espessura": 8.0, "ext_boosting": 1.6005, "extracao_forno": 702.032548397562, "porcentagem_caco": 9.98529201530728, "pot_boost": 3.0, "Prod_E": 1, "Prod_L": 0, "autoclave": 1} | |
| ]""" | |
| month_test_data = """[ | |
| {"data": "2025-04-19", "cor": 0, "espessura": 8.0, "ext_boosting": 1.2007, "extracao_forno": 699.561202512973, "porcentagem_caco": 10.0062724674475, "pot_boost": 3.0, "Prod_E": 1, "Prod_L": 0, "autoclave": 1}, | |
| {"data": "2025-04-20", "cor": 0, "espessura": 8.0, "ext_boosting": 1.2026, "extracao_forno": 699.169485837721, "porcentagem_caco": 9.99757589767354, "pot_boost": 3.0, "Prod_E": 1, "Prod_L": 0, "autoclave": 0}, | |
| {"data": "2025-04-21", "cor": 0, "espessura": 8.0, "ext_boosting": 1.201, "extracao_forno": 699.134346519477, "porcentagem_caco": 9.99807838764974, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 0, "autoclave": 0}, | |
| {"data": "2025-04-22", "cor": 0, "espessura": 8.0, "ext_boosting": 1.2074, "extracao_forno": 701.318973743488, "porcentagem_caco": 9.99545180216949, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 0, "autoclave": 1}, | |
| {"data": "2025-04-23", "cor": 0, "espessura": 8.0, "ext_boosting": 1.2028, "extracao_forno": 702.765143096952, "porcentagem_caco": 9.97488288777139, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 1, "autoclave": 1}, | |
| {"data": "2025-04-24", "cor": 0, "espessura": 8.0, "ext_boosting": 1.3973, "extracao_forno": 700.8439481142, "porcentagem_caco": 10.002226628142, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 1, "autoclave": 1}, | |
| {"data": "2025-04-25", "cor": 0, "espessura": 8.0, "ext_boosting": 1.6005, "extracao_forno": 702.032548397562, "porcentagem_caco": 9.98529201530728, "pot_boost": 3.0, "Prod_E": 1, "Prod_L": 0, "autoclave": 1}, | |
| {"data": "2025-04-26", "cor": 0, "espessura": 8.0, "ext_boosting": 1.7549, "extracao_forno": 703.33718364331, "porcentagem_caco": 9.96677008271902, "pot_boost": 3.0, "Prod_E": 1, "Prod_L": 0, "autoclave": 1}, | |
| {"data": "2025-04-27", "cor": 0, "espessura": 8.0, "ext_boosting": 1.8022, "extracao_forno": 698.519152270116, "porcentagem_caco": 10.0355158154479, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 0, "autoclave": 0}, | |
| {"data": "2025-04-28", "cor": 0, "espessura": 8.0, "ext_boosting": 1.8023, "extracao_forno": 699.802291106822, "porcentagem_caco": 10.0171149610168, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 0, "autoclave": 1}, | |
| {"data": "2025-04-29", "cor": 0, "espessura": 8.0, "ext_boosting": 1.803, "extracao_forno": 702.213883737496, "porcentagem_caco": 9.98271347568585, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 1, "autoclave": 0}, | |
| {"data": "2025-04-30", "cor": 0, "espessura": 8.0, "ext_boosting": 1.801, "extracao_forno": 701.164091438783, "porcentagem_caco": 9.99765972843181, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 1, "autoclave": 0}, | |
| {"data": "2025-05-01", "cor": 0, "espessura": 8.0, "ext_boosting": 1.7999, "extracao_forno": 701.096395285213, "porcentagem_caco": 9.99862507800837, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 1, "autoclave": 0}, | |
| {"data": "2025-05-02", "cor": 0, "espessura": 8.0, "ext_boosting": 1.8016, "extracao_forno": 701.004721690124, "porcentagem_caco": 9.99993264396119, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 1, "autoclave": 0}, | |
| {"data": "2025-05-03", "cor": 0, "espessura": 8.0, "ext_boosting": 1.8023, "extracao_forno": 699.505291072901, "porcentagem_caco": 10.021368086077, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 0, "autoclave": 0}, | |
| {"data": "2025-05-04", "cor": 0, "espessura": 8.0, "ext_boosting": 1.8036, "extracao_forno": 700.073447985429, "porcentagem_caco": 10.0132350686523, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 0, "autoclave": 0}, | |
| {"data": "2025-05-05", "cor": 0, "espessura": 8.0, "ext_boosting": 0.689, "extracao_forno": 700.60585295748, "porcentagem_caco": 10.0056258028798, "pot_boost": 3.0, "Prod_E": 1, "Prod_L": 0, "autoclave": 1}, | |
| {"data": "2025-05-06", "cor": 0, "espessura": 8.0, "ext_boosting": 0.0, "extracao_forno": 699.123418185867, "porcentagem_caco": 10.026841924692, "pot_boost": 3.0, "Prod_E": 1, "Prod_L": 0, "autoclave": 1}, | |
| {"data": "2025-05-07", "cor": 0, "espessura": 8.0, "ext_boosting": 0.0, "extracao_forno": 699.086556585488, "porcentagem_caco": 10.0273706223712, "pot_boost": 3.0, "Prod_E": 1, "Prod_L": 0, "autoclave": 1}, | |
| {"data": "2025-05-08", "cor": 0, "espessura": 8.0, "ext_boosting": 0.0, "extracao_forno": 698.120389195209, "porcentagem_caco": 10.0412480547676, "pot_boost": 3.0, "Prod_E": 1, "Prod_L": 0, "autoclave": 1}, | |
| {"data": "2025-05-09", "cor": 0, "espessura": 8.0, "ext_boosting": 0.0, "extracao_forno": 697.228099576186, "porcentagem_caco": 9.9680434627127, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 0, "autoclave": 0}, | |
| {"data": "2025-05-10", "cor": 0, "espessura": 8.0, "ext_boosting": 0.0, "extracao_forno": 697.37935572186, "porcentagem_caco": 9.96588147179382, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 0, "autoclave": 0}, | |
| {"data": "2025-05-11", "cor": 0, "espessura": 8.0, "ext_boosting": 0.0, "extracao_forno": 699.563378916139, "porcentagem_caco": 10.0205359675357, "pot_boost": 3.0, "Prod_E": 1, "Prod_L": 0, "autoclave": 1}, | |
| {"data": "2025-05-12", "cor": 0, "espessura": 8.0, "ext_boosting": 0.0, "extracao_forno": 698.733542903546, "porcentagem_caco": 10.0324366436888, "pot_boost": 3.0, "Prod_E": 1, "Prod_L": 0, "autoclave": 1}, | |
| {"data": "2025-05-13", "cor": 0, "espessura": 8.0, "ext_boosting": 0.0, "extracao_forno": 699.509702244859, "porcentagem_caco": 10.0213048904162, "pot_boost": 3.0, "Prod_E": 1, "Prod_L": 0, "autoclave": 1}, | |
| {"data": "2025-05-14", "cor": 0, "espessura": 8.0, "ext_boosting": 0.0, "extracao_forno": 701.657766576732, "porcentagem_caco": 9.99062553558067, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 0, "autoclave": 1}, | |
| {"data": "2025-05-15", "cor": 0, "espessura": 8.0, "ext_boosting": 0.0, "extracao_forno": 674.645706945424, "porcentagem_caco": 10.0052515424159, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 0, "autoclave": 1}, | |
| {"data": "2025-05-16", "cor": 0, "espessura": 8.0, "ext_boosting": 0.0, "extracao_forno": 653.148421891636, "porcentagem_caco": 9.95179622600148, "pot_boost": 3.0, "Prod_E": 0, "Prod_L": 0, "autoclave": 1}, | |
| {"data": "2025-05-17", "cor": 0, "espessura": 6.0, "ext_boosting": 0.0, "extracao_forno": 611.090907286899, "porcentagem_caco": 9.98214819965588, "pot_boost": 3.0, "Prod_E": 1, "Prod_L": 0, "autoclave": 1}, | |
| {"data": "2025-05-18", "cor": 0, "espessura": 6.0, "ext_boosting": 0.0, "extracao_forno": 599.399563235682, "porcentagem_caco": 10.0100173040013, "pot_boost": 3.0, "Prod_E": 1, "Prod_L": 0, "autoclave": 1} | |
| ]""" | |
| # Create Gradio interface | |
| with gr.Blocks(title="Energy ML Cloud", theme=gr.themes.Default()) as app: | |
| gr.Markdown("# Energy ML Prediction System") | |
| gr.Markdown("Cloud deployment with embedded models") | |
| with gr.Row(): | |
| with gr.Column(): | |
| model_choice = gr.Radio( | |
| choices=[ | |
| "Threshold Detection", | |
| "Energy Prediction (Random Forest)", | |
| "Energy Prediction (XGBoost)" | |
| ], | |
| label="Select Model", | |
| value="Threshold Detection" | |
| ) | |
| # Quick test data options | |
| gr.Markdown("### Quick Test Options") | |
| with gr.Row(): | |
| load_week_btn = gr.Button("Load Week Test Data", size="sm") | |
| load_month_btn = gr.Button("Load Month Test Data", size="sm") | |
| clear_btn = gr.Button("Clear", size="sm") | |
| json_input = gr.Textbox( | |
| label="JSON Input", | |
| placeholder="Enter JSON data here...", | |
| lines=15, | |
| value=threshold_example | |
| ) | |
| predict_btn = gr.Button("Make Prediction", variant="primary") | |
| with gr.Column(): | |
| output = gr.Textbox( | |
| label="Prediction Result", | |
| lines=20, | |
| interactive=False | |
| ) | |
| def update_example(choice): | |
| if "Threshold" in choice: | |
| return threshold_example | |
| else: | |
| return energy_example | |
| def load_week_data(): | |
| return week_test_data | |
| def load_month_data(): | |
| return month_test_data | |
| def clear_data(): | |
| return "" | |
| model_choice.change(update_example, inputs=[model_choice], outputs=[json_input]) | |
| load_week_btn.click(fn=load_week_data, outputs=json_input) | |
| load_month_btn.click(fn=load_month_data, outputs=json_input) | |
| clear_btn.click(fn=clear_data, outputs=json_input) | |
| predict_btn.click(make_prediction, inputs=[model_choice, json_input], outputs=[output]) | |
| with gr.Accordion("Model Information", open=False): | |
| gr.Markdown(""" | |
| ## Available Models | |
| - **Threshold Detection**: Predict probability of exceeding 8.3 and 9.0 MWh | |
| - **Random Forest**: Energy consumption prediction (R² = 0.72) | |
| - **XGBoost**: Energy consumption prediction (R² = 0.56, winner model) | |
| ## Input Formats | |
| See examples that change when you select different models. | |
| """) | |
| if __name__ == "__main__": | |
| app.launch( | |
| auth=("admin", "energy123"), | |
| share=True, | |
| ssr_mode=False | |
| ) |