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- @app.route('/ian')
- def ian():
- from sklearn.datasets import load_iris
- import pandas as pd
- data = load_iris()
- df = pd.DataFrame(data['data'], columns=['sepal_len', 'sepal_width', 'petal_lengh', 'petal_width'])
- y = data['target']
- from sklearn.tree import DecisionTreeClassifier
- model = DecisionTreeClassifier(criterion='gini', random_state=42)
- input_sepal_len = request.args.get("sepal_len")
- input_sepal_width = request.args.get("sepal_width")
- input_petal_lengh = request.args.get("petal_lengh")
- input_petal_width = request.args.get("petal_width")
- if input_sepal_len:
- model.fit(df, y)
- list_of_data_to_fit = [
- float(input_sepal_len),
- float(input_sepal_width),
- float(input_petal_lengh),
- float(input_petal_width)
- ]
- predicted = model.predict(list_of_data_to_fit).tolist()
- probabilities = model.predict_proba(list_of_data_to_fit).tolist()
- result = {
- "response": "ok",
- "predictions": predicted,
- "score": model.score(df, y),
- "probabilities": {flower: probabilities[0][index] for index, flower in enumerate(model.classes_.tolist())}
- }
- else:
- return "Please pass an input"
- return jsonify(result)
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