Create app.py

app.py

@@ -1,34 +1,25 @@
-from sklearn.datasets import load_iris
-from sklearn.model_selection import train_test_split
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score
+# app.py
 
-# Load dataset (for demonstration)
-data = load_iris()
-X, y = data.data, data.target
+from flask import Flask, request, jsonify
+import joblib
 
-# Split the dataset into training and testing sets
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+app = Flask(__name__)
 
-# Initialize RandomForestClassifier
-classifier = RandomForestClassifier(n_estimators=100, random_state=42)
+# Load trained model
+model = joblib.load('model.pkl')
 
-# Train the classifier
-classifier.fit(X_train, y_train)
+@app.route('/predict', methods=['POST'])
+def predict():
+    # Get input data
+    data = request.json['data']
+    
+    # Make predictions
+    predictions = model.predict(data)
+    
+    # Return predictions
+    return jsonify({'predictions': predictions.tolist()})
 
+if __name__ == '__main__':
+    app.run(debug=True)
 
-# Make predictions on the test set
-y_pred = classifier.predict(X_test)
 
-# Calculate accuracy
-accuracy = accuracy_score(y_test, y_pred)
-print("Accuracy:", accuracy)
-
-
-# prompt: save the model object in pickle file 
-
-import pickle
-
-# Save the trained model to a pickle file
-with open('model.pkl', 'wb') as f:
-    pickle.dump(classifier, f)