app.py

import gradio as gr
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import torch


batting = pd.read_csv('Batting.csv')
salaries = pd.read_csv('Salaries.csv')

df = pd.merge(batting, salaries, on=['playerID', 'yearID', 'teamID'])


df['BA'] = df['H'] / df['AB']


df['OBP'] = (df['H'] + df['BB'] + df['HBP']) / (df['AB'] + df['BB'] + df['HBP'] + df['SF'])

# Slugging percentage
df['SLG'] = (df['H'] + 2*df['2B'] + 3*df['3B'] + 4*df['HR']) / df['AB']

df = df.dropna()

features = ['BA', 'OBP', 'SLG', 'HR', 'RBI', 'SB']
X = df[features]
y = df['salary']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

X_train_tensor = torch.tensor(X_train.fillna(0).values, dtype=torch.float32)
y_train_tensor = torch.tensor(y_train.fillna(0).values, dtype=torch.float32)
X_test_tensor = torch.FloatTensor(X_test.values)
y_test_tensor = torch.FloatTensor(y_test.values)

class LinearRegression(torch.nn.Module):
    def __init__(self, input_dim):
        super(LinearRegression, self).__init__()
        self.linear = torch.nn.Linear(input_dim, 1)

    def forward(self, x):
        return self.linear(x)

model = LinearRegression(len(features))
criterion = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.0001)

num_epochs = 1000
for epoch in range(num_epochs):
    outputs = model(X_train_tensor)
    loss = criterion(outputs, y_train_tensor.unsqueeze(1))
    
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

# Define prediction function for Gradio
def predict_salary(BA, OBP, SLG, HR, RBI, SB):
    stats = [BA, OBP, SLG, HR, RBI, SB]
    with torch.no_grad():
        stats_tensor = torch.FloatTensor([stats])
        predicted_salary = model(stats_tensor).item()
    return f'${predicted_salary:,.2f}'

# Gradio interface
demo = gr.Interface(
    fn=predict_salary,
    inputs=[
        gr.components.Number(label="Batting Average (BA)"),
        gr.components.Number(label="On-base Percentage (OBP)"),
        gr.components.Number(label="Slugging Percentage (SLG)"),
        gr.components.Number(label="Home Runs (HR)"),
        gr.components.Number(label="Runs Batted In (RBI)"),
        gr.components.Number(label="Stolen Bases (SB)")
    ],
    outputs="text",
    title="Baseball Player Salary Predictor"
)

# Launch the app
demo.launch(share=True)