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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.pkl'):
with open('threshold_model_83.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.pkl'):
with open('threshold_model_90.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)
# Parse input data
date_obj = datetime.strptime(data['data'], '%Y-%m-%d')
# Color mapping
color_mapping = {0: 'incolor', 1: 'verde', 2: 'cinza', 3: 'bronze'}
cor_str = color_mapping.get(data['cor'], 'incolor')
# Create input features
input_data = {
'boosting': data['pot_boost'],
'espessura': data['espessura'],
'extracao_forno': data['extracao_forno'],
'porcentagem_caco': data['porcentagem_caco'],
'cor': cor_str,
'prod_e': data['Prod_E'],
'prod_l': data['Prod_L'],
'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
}
# 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)
# Format response
next_date = (date_obj + pd.Timedelta(days=1)).strftime('%Y-%m-%d')
result = {
"predictions": {
"prediction_1": [
{
"datetime": data['data'],
"probabilidade_de_estouro": float(prob_83),
"estouro_previsto": pred_83
},
{
"datetime": next_date,
"probabilidade_de_estouro": float(prob_83 * 0.98),
"estouro_previsto": int(prob_83 * 0.98 > 0.5)
}
],
"prediction_2": [
{
"datetime": data['data'],
"probabilidade_de_estouro": float(prob_90),
"estouro_previsto": pred_90
},
{
"datetime": next_date,
"probabilidade_de_estouro": float(prob_90 * 0.99),
"estouro_previsto": int(prob_90 * 0.99 > 0.5)
}
]
}
}
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')
boosting_val = float(item['boosting'].replace(',', '.'))
extracao_val = float(item['extracao_forno'].replace(',', '.'))
# Create features
input_data = {
'boosting': boosting_val,
'espessura': item['espessura'],
'extracao_forno': extracao_val,
'porcentagem_caco': item['porcentagem_caco'],
'cor': item['cor'].lower(),
'prod_e': item.get('prod_e', 1),
'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')
boosting_val = float(item['boosting'].replace(',', '.'))
extracao_val = float(item['extracao_forno'].replace(',', '.'))
# 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': item['cor'].lower(),
'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
}
]"""
# 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"
)
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
model_choice.change(update_example, inputs=[model_choice], 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
) |