added app.py

app.py

@@ -0,0 +1,49 @@
+import gradio as gr
+import joblib
+import pandas as pd
+
+# Load pipeline components
+scaler, pca, clf = joblib.load("tennis_model.pkl")
+
+# All possible categorical values (must match training one-hot encoding)
+outlook_options = ["Sunny", "Overcast", "Rain"]
+temp_options = ["Hot", "Mild", "Cool"]
+humidity_options = ["High", "Normal"]
+wind_options = ["Weak", "Strong"]
+
+def predict_play(outlook, temp, humidity, wind):
+    # Build input row
+    data = pd.DataFrame([[outlook, temp, humidity, wind]],
+                        columns=["outlook", "temp", "humidity", "wind"])
+    
+    # One-hot encode to match training
+    data_enc = pd.get_dummies(data)
+    
+    # Ensure all training columns exist
+    for col in scaler.feature_names_in_:
+        if col not in data_enc:
+            data_enc[col] = 0
+    
+    data_enc = data_enc[scaler.feature_names_in_]  # reorder
+    
+    # Scale + PCA + Predict
+    X_scaled = scaler.transform(data_enc)
+    X_pca = pca.transform(X_scaled)
+    pred = clf.predict(X_pca)[0]
+    
+    return f"Prediction: {pred}"
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 🎾 Play Tennis Predictor")
+    outlook = gr.Dropdown(outlook_options, label="Outlook")
+    temp = gr.Dropdown(temp_options, label="Temperature")
+    humidity = gr.Dropdown(humidity_options, label="Humidity")
+    wind = gr.Dropdown(wind_options, label="Wind")
+    
+    btn = gr.Button("Predict")
+    output = gr.Textbox(label="Result")
+    
+    btn.click(fn=predict_play, inputs=[outlook, temp, humidity, wind], outputs=output)
+
+demo.launch(server_host="0.0.0.0",server_port=7860,debug=True)