import gradio as gr
import joblib
import pandas as pd

# Load the model and encoders
model = joblib.load('random_forest_model.joblib')
venue_mapping = {
    "MCG": 0,
    "Eden Gardens": 1,
    "Lords": 2
}

match_type_mapping = {
    "ODI": 0,
    "T20": 1,
    "Test": 2
}

team_mapping = {
    "India": 0,
    "Australia": 1,
    "England": 2,
    "Pakistan": 3
}

def predict_score(venue, match_type, team_batting, team_bowling):
 
        # Use the mappings to convert categorical values to numbers
        venue_encoded = venue_mapping[venue]
        match_type_encoded = match_type_mapping[match_type]
        team_batting_encoded = team_mapping[team_batting]
        team_bowling_encoded = team_mapping[team_bowling]

        # Prepare the input data for prediction
        new_match = pd.DataFrame({
            'Venue': [venue_encoded],
            'Match_Type': [match_type_encoded],
            'Team_Batting': [team_batting_encoded],
            'Team_Bowling': [team_bowling_encoded]
        })

        # Make prediction
        predicted_score = model.predict(new_match)
        return round(predicted_score[0])

   
# Create Gradio interface
interface = gr.Interface(
    fn=predict_score, 
    inputs=[
        gr.Dropdown(['MCG', 'Eden Gardens', 'Wankhede'], label='Venue'),
        gr.Dropdown(['ODI', 'T20'], label='Match Type'),
        gr.Dropdown(['India', 'Australia', 'England'], label='Team Batting'),
        gr.Dropdown(['Australia', 'India', 'England'], label='Team Bowling')
    ], 
    outputs=gr.Textbox(label="Predicted Score"),
    title="Cricket Match Score Predictor",
    description="Enter match details to predict the final score."
)

# Launch the interface
if __name__ == "__main__":
    interface.launch()