Add Gradio app and requirements file

app.py

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+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('moneyball project\Batting.csv')
+salaries = pd.read_csv('moneyball project\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)