crop_prediction.py

# -*- coding: utf-8 -*-
"""crop_prediction.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1ciQ49ohhHAZssJOd9FcX2_EcRoUYIG6X

## Imports
"""

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import pickle
from sklearn.metrics import classification_report, accuracy_score

print("imported")

"""## Data"""

df = pd.read_csv("Crop_recommendation.csv")
df.head()
df.dtypes

df["label"].unique()
df["label"].value_counts()

features = df[["N", "P", "K", "temperature", "humidity", "ph", "rainfall"]]
target = df["label"]
features.head()
target.head()

Xtrain, Xtest, Ytrain, Ytest = train_test_split(
    features, target, test_size=0.2, random_state=2
)

"""# Decision Tree

"""

from sklearn.tree import DecisionTreeClassifier

DecisionTree = DecisionTreeClassifier(criterion="entropy", random_state=2, max_depth=7)

DecisionTree.fit(Xtrain, Ytrain)

predicted_values = DecisionTree.predict(Xtest)
x = accuracy_score(Ytest, predicted_values)
print(x)

# Dump the trained Naive Bayes classifier with Pickle
DT_pkl_filename = "content/models/DecisionTree.pkl"
# Open the file to save as pkl file
DT_Model_pkl = open(DT_pkl_filename, "wb")
pickle.dump(DecisionTree, DT_Model_pkl)
# Close the pickle instances
DT_Model_pkl.close()

"""# Random Forest"""

from sklearn.ensemble import RandomForestClassifier

RandomForest = RandomForestClassifier(n_estimators=10, max_depth=7, random_state=0)
RandomForest.fit(Xtrain, Ytrain)
predicted_values = RandomForest.predict(Xtest)
x = accuracy_score(Ytest, predicted_values)
print(x)

# Dump the trained Random Forest with Pickle
RF_pkl_filename = "content/models/RandomForest.pkl"
# Open the file to save as pkl file
RF_Model_pkl = open(RF_pkl_filename, "wb")
pickle.dump(RandomForest, RF_Model_pkl)
# Close the pickle instances
RF_Model_pkl.close()

"""# Naive Bayes classifier"""

from sklearn.naive_bayes import GaussianNB

NaiveBayes = GaussianNB()

NaiveBayes.fit(Xtrain, Ytrain)

predicted_values = NaiveBayes.predict(Xtrain)
x = accuracy_score(Ytrain, predicted_values)
print(x)

# Dump the trained Naive Bayes classifier with Pickle
NB_pkl_filename = "content/models/NaiveBayes.pkl"
# Open the file to save as pkl file
NB_Model_pkl = open(NB_pkl_filename, "wb")
pickle.dump(NaiveBayes, NB_Model_pkl)
# Close the pickle instances
NB_Model_pkl.close()

"""# SVM"""

from sklearn.svm import SVC

# data normalization with sklearn
from sklearn.preprocessing import MinMaxScaler

# fit scaler on training data
norm = MinMaxScaler().fit(Xtrain)
X_train_norm = norm.transform(Xtrain)
# transform testing dataabs
X_test_norm = norm.transform(Xtest)
SVM = SVC(kernel="poly", degree=3, C=1)
SVM.fit(X_train_norm, Ytrain)
predicted_values = SVM.predict(X_test_norm)
x = accuracy_score(Ytest, predicted_values)
print(x)

# Dump the trained Naive Bayes classifier with Pickle
SVM_pkl_filename = "content/models/SVM.pkl"
# Open the file to save as pkl file
SVM_Model_pkl = open(SVM_pkl_filename, "wb")
pickle.dump(SVM, SVM_Model_pkl)
# Close the pickle instances
SVM_Model_pkl.close()
    # print(predict(data))

# # Dependencies
# from flask import Flask, request, jsonify
# import joblib
# import traceback
# import pandas as pd
# import numpy as np
# import sys

# # Your API definition
# app = Flask(__name__)

# @app.route('/predict', methods=['POST'])
# def predict():
#     if lr:
#         try:
#             json_ = request.json
#             print(json_)
#             query = pd.get_dummies(pd.DataFrame(json_))
#             # query = query.reindex(columns=model_columns, fill_value=0)

#             prediction = list(lr.predict(query))

#             return jsonify({'prediction': str(prediction)})

#         except:

#             return jsonify({'trace': traceback.format_exc()})
#     else:
#         print ('Train the model first')
#         return ('No model here to use')

# if __name__ == '__main__':
#     try:
#         port = int(sys.argv[1]) # This is for a command-line input
#     except:
#         port = 12345 # If you don't provide any port the port will be set to 12345

#     lr = joblib.load('content/models/DecisionTree.pkl') # Load "model.pkl"
#     print ('Model loaded')
#     # model_columns = joblib.load("model_columns.pkl") # Load "model_columns.pkl"
#     print ('Model columns loaded')

#     app.run(port=port, debug=True)

from flask import Flask, request

# from sklearn.externals import joblib
import joblib

app = Flask(__name__)


@app.route("/")
def predict():
    return "Hello world"


if __name__ == "__main__":
    app.run(port=8082, debug=True)

    # print(predict(data))