# app.py
import gradio as gr
import pandas as pd
from xgboost import XGBClassifier
import pickle

# Load model
model = pickle.load(open('xgboost_model.pkl', 'rb'))

def predict_defects(loc, complexity, operators, operands, comments):
    """
    Simplified prediction using only key metrics
    """
    # Create a complete input data with reasonable defaults
    input_data = pd.DataFrame([[
        loc,            # loc
        complexity,     # v(g)
        complexity,     # ev(g)
        complexity,     # iv(g)
        operators + operands,  # n
        operators * operands,  # v
        0.5,           # l (default)
        2.0,           # d (default)
        100,           # i (default)
        200,           # e (default)
        0.2,           # b (default)
        30,            # t (default)
        loc,           # lOCode
        comments,      # lOComment
        loc * 0.1,     # lOBlank
        comments,      # locCodeAndComment
        operators,     # uniq_Op
        operands,      # uniq_Opnd
        operators * 2, # total_Op
        operands * 2,  # total_Opnd
        complexity     # branchCount
    ]], columns=['loc', 'v(g)', 'ev(g)', 'iv(g)', 'n', 'v',
                'l', 'd', 'i', 'e', 'b', 't', 'lOCode',
                'lOComment', 'lOBlank', 'locCodeAndComment',
                'uniq_Op', 'uniq_Opnd', 'total_Op',
                'total_Opnd', 'branchCount'])
    
    # Make prediction
    prediction = model.predict_proba(input_data)[0][1]
    
    return f"Defect Probability: {prediction:.2%}"

# Gradio interface
iface = gr.Interface(
    fn=predict_defects,
    inputs=[
        gr.Slider(minimum=1, maximum=1000, value=100, 
                 label="Lines of Code", info="Total number of code lines"),
        gr.Slider(minimum=1, maximum=50, value=5, 
                 label="Code Complexity", info="How complex is your code"),
        gr.Slider(minimum=1, maximum=100, value=20, 
                 label="Unique Operators", info="Number of different operators (+, -, *, etc.)"),
        gr.Slider(minimum=1, maximum=100, value=20, 
                 label="Unique Operands", info="Number of different variables and constants"),
        gr.Slider(minimum=0, maximum=500, value=50, 
                 label="Comment Lines", info="Number of comment lines"),
    ],
    outputs="text",
    title="Software Defect Predictor",
    description="Predict the probability of software defects based on code metrics",
    theme="soft"
)

if __name__ == "__main__":
    iface.launch()