Create app.py
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app.py
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# app.py
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import gradio as gr
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import pandas as pd
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from xgboost import XGBClassifier
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import pickle
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# Load model
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model = pickle.load(open('xgboost_model.pkl', 'rb'))
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def predict_defects(loc, complexity, operators, operands, comments):
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"""
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Simplified prediction using only key metrics
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"""
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# Create a complete input data with reasonable defaults
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input_data = pd.DataFrame([[
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loc, # loc
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complexity, # v(g)
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complexity, # ev(g)
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complexity, # iv(g)
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operators + operands, # n
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operators * operands, # v
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0.5, # l (default)
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2.0, # d (default)
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100, # i (default)
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200, # e (default)
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0.2, # b (default)
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30, # t (default)
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loc, # lOCode
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comments, # lOComment
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loc * 0.1, # lOBlank
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comments, # locCodeAndComment
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operators, # uniq_Op
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operands, # uniq_Opnd
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operators * 2, # total_Op
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operands * 2, # total_Opnd
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complexity # branchCount
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]], columns=['loc', 'v(g)', 'ev(g)', 'iv(g)', 'n', 'v',
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'l', 'd', 'i', 'e', 'b', 't', 'lOCode',
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'lOComment', 'lOBlank', 'locCodeAndComment',
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'uniq_Op', 'uniq_Opnd', 'total_Op',
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'total_Opnd', 'branchCount'])
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# Make prediction
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prediction = model.predict_proba(input_data)[0][1]
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return f"Defect Probability: {prediction:.2%}"
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# Gradio interface
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iface = gr.Interface(
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fn=predict_defects,
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inputs=[
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gr.Slider(minimum=1, maximum=1000, value=100,
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label="Lines of Code", info="Total number of code lines"),
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gr.Slider(minimum=1, maximum=50, value=5,
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label="Code Complexity", info="How complex is your code"),
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gr.Slider(minimum=1, maximum=100, value=20,
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label="Unique Operators", info="Number of different operators (+, -, *, etc.)"),
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gr.Slider(minimum=1, maximum=100, value=20,
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label="Unique Operands", info="Number of different variables and constants"),
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gr.Slider(minimum=0, maximum=500, value=50,
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label="Comment Lines", info="Number of comment lines"),
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],
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outputs="text",
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title="Software Defect Predictor",
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description="Predict the probability of software defects based on code metrics",
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theme="soft"
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)
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if __name__ == "__main__":
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iface.launch()
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