Create app.py

app.py

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+import pandas as pd
+from sklearn.linear_model import LinearRegression
+import gradio as gr
+
+# Load dataset
+teams = pd.read_csv("teams.csv")
+teams = teams[['team', 'country', 'year', 'athletes', 'age', 'prev_medals', 'medals']]
+teams = teams.dropna()
+
+# Split data into training and testing sets
+train = teams[teams['year'] < 2012].copy()
+test = teams[teams['year'] >= 2012].copy()
+
+# Define predictors and target
+predictors = ['athletes', 'prev_medals']
+target = 'medals'
+
+# Train the Linear Regression model
+reg = LinearRegression()
+reg.fit(train[predictors], train['medals'])
+
+# Define the prediction function
+def predict_medals(athletes: int, prev_medals: int):
+    input_data = pd.DataFrame({'athletes': [athletes], 'prev_medals': [prev_medals]})
+    prediction = reg.predict(input_data)[0]
+    return max(0, round(prediction))
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=predict_medals,
+    inputs=[
+        gr.Number(label="Number of Athletes"),
+        gr.Number(label="Previous Medals Won"),
+    ],
+    outputs="number",
+    title="Olympics Medal Prediction",
+    description="Predict the number of medals a team might win based on athletes and previous medals."
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    interface.launch()