sepal petal + readme

sepal_petal detection/app.py

@@ -0,0 +1,33 @@
+import numpy as np
+import pandas as pd
+from flask import Flask, request, jsonify, render_template
+import pickle
+
+app = Flask(__name__)
+
+model = pickle.load(open("model.pkl", "rb"))
+
+@app.route("/")
+def home():
+    return render_template("index.html")
+
+# @app.route("/predict", methods=["POST"])
+# def predict():
+#     float_features = [float(x) for x in request.form.values()]
+#     features = [np.array(float_features)]
+#     prediction = model.predict(features)
+
+#     return render_template("index.html", prediction_text="The flower is {}".format(prediction))
+
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    json_ = request.json
+    query_df = pd.DataFrame(json_)
+    prediction = model.predict(query_df)
+    # result = prediction.tolist()
+    return jsonify({"placement_stat":prediction.tolist()})
+
+
+if __name__ == "__main__":
+    app.run(debug=True)

sepal_petal detection/iris.csv

@@ -0,0 +1,151 @@
+Sepal_Length,Sepal_Width,Petal_Length,Petal_Width,Class
+5.1,3.5,1.4,0.2,Setosa
+4.9,3,1.4,0.2,Setosa
+4.7,3.2,1.3,0.2,Setosa
+4.6,3.1,1.5,0.2,Setosa
+5,3.6,1.4,0.2,Setosa
+5.4,3.9,1.7,0.4,Setosa
+4.6,3.4,1.4,0.3,Setosa
+5,3.4,1.5,0.2,Setosa
+4.4,2.9,1.4,0.2,Setosa
+4.9,3.1,1.5,0.1,Setosa
+5.4,3.7,1.5,0.2,Setosa
+4.8,3.4,1.6,0.2,Setosa
+4.8,3,1.4,0.1,Setosa
+4.3,3,1.1,0.1,Setosa
+5.8,4,1.2,0.2,Setosa
+5.7,4.4,1.5,0.4,Setosa
+5.4,3.9,1.3,0.4,Setosa
+5.1,3.5,1.4,0.3,Setosa
+5.7,3.8,1.7,0.3,Setosa
+5.1,3.8,1.5,0.3,Setosa
+5.4,3.4,1.7,0.2,Setosa
+5.1,3.7,1.5,0.4,Setosa
+4.6,3.6,1,0.2,Setosa
+5.1,3.3,1.7,0.5,Setosa
+4.8,3.4,1.9,0.2,Setosa
+5,3,1.6,0.2,Setosa
+5,3.4,1.6,0.4,Setosa
+5.2,3.5,1.5,0.2,Setosa
+5.2,3.4,1.4,0.2,Setosa
+4.7,3.2,1.6,0.2,Setosa
+4.8,3.1,1.6,0.2,Setosa
+5.4,3.4,1.5,0.4,Setosa
+5.2,4.1,1.5,0.1,Setosa
+5.5,4.2,1.4,0.2,Setosa
+4.9,3.1,1.5,0.2,Setosa
+5,3.2,1.2,0.2,Setosa
+5.5,3.5,1.3,0.2,Setosa
+4.9,3.6,1.4,0.1,Setosa
+4.4,3,1.3,0.2,Setosa
+5.1,3.4,1.5,0.2,Setosa
+5,3.5,1.3,0.3,Setosa
+4.5,2.3,1.3,0.3,Setosa
+4.4,3.2,1.3,0.2,Setosa
+5,3.5,1.6,0.6,Setosa
+5.1,3.8,1.9,0.4,Setosa
+4.8,3,1.4,0.3,Setosa
+5.1,3.8,1.6,0.2,Setosa
+4.6,3.2,1.4,0.2,Setosa
+5.3,3.7,1.5,0.2,Setosa
+5,3.3,1.4,0.2,Setosa
+7,3.2,4.7,1.4,Versicolor
+6.4,3.2,4.5,1.5,Versicolor
+6.9,3.1,4.9,1.5,Versicolor
+5.5,2.3,4,1.3,Versicolor
+6.5,2.8,4.6,1.5,Versicolor
+5.7,2.8,4.5,1.3,Versicolor
+6.3,3.3,4.7,1.6,Versicolor
+4.9,2.4,3.3,1,Versicolor
+6.6,2.9,4.6,1.3,Versicolor
+5.2,2.7,3.9,1.4,Versicolor
+5,2,3.5,1,Versicolor
+5.9,3,4.2,1.5,Versicolor
+6,2.2,4,1,Versicolor
+6.1,2.9,4.7,1.4,Versicolor
+5.6,2.9,3.6,1.3,Versicolor
+6.7,3.1,4.4,1.4,Versicolor
+5.6,3,4.5,1.5,Versicolor
+5.8,2.7,4.1,1,Versicolor
+6.2,2.2,4.5,1.5,Versicolor
+5.6,2.5,3.9,1.1,Versicolor
+5.9,3.2,4.8,1.8,Versicolor
+6.1,2.8,4,1.3,Versicolor
+6.3,2.5,4.9,1.5,Versicolor
+6.1,2.8,4.7,1.2,Versicolor
+6.4,2.9,4.3,1.3,Versicolor
+6.6,3,4.4,1.4,Versicolor
+6.8,2.8,4.8,1.4,Versicolor
+6.7,3,5,1.7,Versicolor
+6,2.9,4.5,1.5,Versicolor
+5.7,2.6,3.5,1,Versicolor
+5.5,2.4,3.8,1.1,Versicolor
+5.5,2.4,3.7,1,Versicolor
+5.8,2.7,3.9,1.2,Versicolor
+6,2.7,5.1,1.6,Versicolor
+5.4,3,4.5,1.5,Versicolor
+6,3.4,4.5,1.6,Versicolor
+6.7,3.1,4.7,1.5,Versicolor
+6.3,2.3,4.4,1.3,Versicolor
+5.6,3,4.1,1.3,Versicolor
+5.5,2.5,4,1.3,Versicolor
+5.5,2.6,4.4,1.2,Versicolor
+6.1,3,4.6,1.4,Versicolor
+5.8,2.6,4,1.2,Versicolor
+5,2.3,3.3,1,Versicolor
+5.6,2.7,4.2,1.3,Versicolor
+5.7,3,4.2,1.2,Versicolor
+5.7,2.9,4.2,1.3,Versicolor
+6.2,2.9,4.3,1.3,Versicolor
+5.1,2.5,3,1.1,Versicolor
+5.7,2.8,4.1,1.3,Versicolor
+6.3,3.3,6,2.5,Virginica
+5.8,2.7,5.1,1.9,Virginica
+7.1,3,5.9,2.1,Virginica
+6.3,2.9,5.6,1.8,Virginica
+6.5,3,5.8,2.2,Virginica
+7.6,3,6.6,2.1,Virginica
+4.9,2.5,4.5,1.7,Virginica
+7.3,2.9,6.3,1.8,Virginica
+6.7,2.5,5.8,1.8,Virginica
+7.2,3.6,6.1,2.5,Virginica
+6.5,3.2,5.1,2,Virginica
+6.4,2.7,5.3,1.9,Virginica
+6.8,3,5.5,2.1,Virginica
+5.7,2.5,5,2,Virginica
+5.8,2.8,5.1,2.4,Virginica
+6.4,3.2,5.3,2.3,Virginica
+6.5,3,5.5,1.8,Virginica
+7.7,3.8,6.7,2.2,Virginica
+7.7,2.6,6.9,2.3,Virginica
+6,2.2,5,1.5,Virginica
+6.9,3.2,5.7,2.3,Virginica
+5.6,2.8,4.9,2,Virginica
+7.7,2.8,6.7,2,Virginica
+6.3,2.7,4.9,1.8,Virginica
+6.7,3.3,5.7,2.1,Virginica
+7.2,3.2,6,1.8,Virginica
+6.2,2.8,4.8,1.8,Virginica
+6.1,3,4.9,1.8,Virginica
+6.4,2.8,5.6,2.1,Virginica
+7.2,3,5.8,1.6,Virginica
+7.4,2.8,6.1,1.9,Virginica
+7.9,3.8,6.4,2,Virginica
+6.4,2.8,5.6,2.2,Virginica
+6.3,2.8,5.1,1.5,Virginica
+6.1,2.6,5.6,1.4,Virginica
+7.7,3,6.1,2.3,Virginica
+6.3,3.4,5.6,2.4,Virginica
+6.4,3.1,5.5,1.8,Virginica
+6,3,4.8,1.8,Virginica
+6.9,3.1,5.4,2.1,Virginica
+6.7,3.1,5.6,2.4,Virginica
+6.9,3.1,5.1,2.3,Virginica
+5.8,2.7,5.1,1.9,Virginica
+6.8,3.2,5.9,2.3,Virginica
+6.7,3.3,5.7,2.5,Virginica
+6.7,3,5.2,2.3,Virginica
+6.3,2.5,5,1.9,Virginica
+6.5,3,5.2,2,Virginica
+6.2,3.4,5.4,2.3,Virginica
+5.9,3,5.1,1.8,Virginica

sepal_petal detection/model.py

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+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+import pickle
+
+# Load the csv file
+df = pd.read_csv("iris.csv")
+
+print(df.head())
+
+# Select independent and dependent variable
+X = df[["Sepal_Length", "Sepal_Width", "Petal_Length", "Petal_Width"]]
+y = df["Class"]
+
+# Split the dataset into train and test
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=50)
+
+# Feature scaling
+sc = StandardScaler()
+X_train = sc.fit_transform(X_train)
+X_test= sc.transform(X_test)
+
+# Instantiate the model
+classifier = RandomForestClassifier()
+
+# Fit the model
+classifier.fit(X_train, y_train)
+
+# Make pickle file of our model
+pickle.dump(classifier, open("model.pkl", "wb"))