app.py

import gradio as gr
import pandas as pd
import random
from keras.models import load_model
import tensorflow as tf
import numpy as np

data = pd.read_pickle("merged_all_table.pkl", compression='bz2')

home_team_id = sorted(data["home_team_long_name"].unique())
away_team_id = sorted(data["away_team_long_name"].unique())

nn_model = load_model('models/nn_model.h5')


def main_process(model, Home_team, Away_team):

    home_temp = data[data["home_team_long_name"] == Home_team]
    home_temp = home_temp[["home_team_overall_score", "home_total_goal", "home_players_avg_overall_rating", "home_players_avg_overall_score", "home_players_avg_ideal_body_rate", "home_total_win", "home_total_loose", "home_total_draw", "league_home_total_win", "league_home_total_loose", "league_home_total_draw"]]
    print("Home Team Data Geathring ✅")

    away_temp = data[data["away_team_long_name"] == Away_team]
    away_temp = away_temp[["away_team_overall_score", "away_total_goal", "away_players_avg_overall_rating", "away_players_avg_overall_score", "away_players_avg_ideal_body_rate", "away_total_win", "away_total_loose", "away_total_draw", "league_away_total_win", "league_away_total_loose", "league_away_total_draw"]]
    print("Away Team Data Geathring ✅")

    table = pd.concat([home_temp.mean(), away_temp.mean()], axis=0)
    table = table[["home_team_overall_score", "away_team_overall_score", "home_total_goal", "away_total_goal", "home_players_avg_overall_rating", "home_players_avg_overall_score", "home_players_avg_ideal_body_rate", "away_players_avg_overall_rating", "away_players_avg_overall_score", "away_players_avg_ideal_body_rate", "home_total_win", "home_total_loose", "home_total_draw", "away_total_win", "away_total_loose", "away_total_draw", "league_home_total_win", "league_home_total_loose", "league_home_total_draw", "league_away_total_win", "league_away_total_loose", "league_away_total_draw"]]
    print("Table Concatination ✅")

    X = table.to_frame().T

    pred = model.predict(X)
    predicted_labels = np.argmax(pred)
    print("Data Prediction ✅")

    print(predicted_labels)

    return predicted_labels


def predict(Home_team, Away_team, Model_name):

    if Home_team == "":
        raise gr.Error("Home Team is required, Please Select The Home Team!")
    
    if Away_team  == "":
        raise gr.Error("Away Team is required, Please Select The Away Team!")
    
    if Model_name  == "":
        raise gr.Error("Model is required, Please Select The Model!")
    
    if Model_name == "Simple Nueral Network Model":
        model = nn_model

    prediction = main_process(model, Home_team, Away_team)

    if prediction == 0:
        return "🥳 Home Team Win 🎉"

    if prediction == 1:
        return "🥳 Away Team Win 🎉"

    if prediction == 2:
        return "😑 Match Draw 😑"


# markup table for markdown
# # Members:
#     | Students Name      | Student ID |
#     |    :---    |    :----:   |
#     | Zeel Karshanbhai Sheladiya      | 500209119       | 
#     | Ravikumar Chandrakantbhai Patel   | 500196861        |
#     | Dharma Teja Reddy Bandreddi   | 500209454        |
#     | Sai Charan Reddy Meda  | 500201602        |
#     | Aditya Babu   | 500209122        |
#     | Sudip Bhattarai   | 500198055        |
#     | NOMAN FAZAL MUKADAM   | 500209115        |
#     | Leela Prasad Kavuri   | 500209550        |
#     | Vamsi Dasari   | 500200775        |

with gr.Blocks() as demo:
    gr.Markdown("""
    # Subject: Data Science Project Management and Requirement Gathering 02 (Group 4)
    [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/ravi7522/Football-Prediction)
    """)
    with gr.Row():
        gr.Label("⚽️ Football Prediction ⚽️", container=False)

    with gr.Row():
        with gr.Column():

            dd_home_team = gr.Dropdown(
                label="Home Team",
                choices=home_team_id,
                info="Select Your Home Team:",
                multiselect=False,
            )

        with gr.Column(): 
        
            dd_away_team = gr.Dropdown(
                label="Away Team",
                choices=away_team_id,
                info="Select Your Away Team:",
                multiselect=False,
            )

    with gr.Row():

        with gr.Column(): 
        
            dd_model = gr.Dropdown(
                label="Model ( Feature Under Construction 🚧 )",
                choices=["Simple Nueral Network Model"],
                info="Select Your Model:",
                multiselect=False,
            )

    with gr.Row():
        predict_btn = gr.Button(value="Predict")
            
    with gr.Row():
        Answer = gr.Label("👋 Hello, Let us predict the Football Match 💁‍♂️", container=False)

    predict_btn.click(
        predict,
        inputs=[
            dd_home_team,
            dd_away_team,
            dd_model,
        ],
        outputs=[Answer],
    )

demo.launch()