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BABF_ec_ML

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  1. .gitattributes +1 -0
  2. .gradio/certificate.pem +31 -0
  3. README.md +1 -0
  4. app.py +33 -0
  5. cleaned_flat_terraced_data_final.csv +0 -0
  6. cleaned_with_labels.csv +0 -0
  7. code.ipynb +0 -0
  8. data/Camden_data.csv +3 -0
  9. data/Test_data.xlsx +0 -0
  10. energy_predictor.ipynb +0 -0
  11. gb_features.joblib +3 -0
  12. gb_model.joblib +3 -0
  13. requirements.txt.txt +8 -0
  14. rf_features.joblib +3 -0
  15. rf_model.joblib +3 -0
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ data/Camden_data.csv filter=lfs diff=lfs merge=lfs -text
.gradio/certificate.pem ADDED
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+ -----END CERTIFICATE-----
README.md ADDED
@@ -0,0 +1 @@
 
 
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+ # BABF_ec_ML
app.py ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import gradio as gr
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+ import pandas as pd
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+ import joblib
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+ import numpy as np
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+ from sklearn.ensemble import RandomForestRegressor
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+
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+ # 模拟加载或创建模型
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+ try:
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+ model = joblib.load('model.pkl')
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+ except:
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+ # 模拟数据训练
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+ X = pd.DataFrame(np.random.rand(100, 2), columns=['feature1', 'feature2'])
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+ y = X['feature1'] * 3 + X['feature2'] * 2 + np.random.rand(100)
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+ model = RandomForestRegressor()
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+ model.fit(X, y)
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+ joblib.dump(model, 'model.pkl')
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+
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+ # 预测函数
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+ def predict_energy(feature1, feature2):
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+ input_data = pd.DataFrame([[feature1, feature2]], columns=['feature1', 'feature2'])
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+ prediction = model.predict(input_data)
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+ return f'预测能耗为：{prediction[0]:.2f}'
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+
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+ # Gradio 接口定义
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+ demo = gr.Interface(
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+ fn=predict_energy,
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+ inputs=[gr.Number(label='Feature 1'), gr.Number(label='Feature 2')],
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+ outputs='text',
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+ title='Energy Predictor',
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+ description='根据输入特征预测能耗'
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+ )
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+
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+ demo.launch()
cleaned_flat_terraced_data_final.csv ADDED
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cleaned_with_labels.csv ADDED
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code.ipynb ADDED
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requirements.txt.txt ADDED
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+ gradio
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+ pandas
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+ numpy
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+ scikit-learn
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+ seaborn
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+ plotly
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+ matplotlib
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+ joblib
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