Update app.py

app.py

@@ -1,174 +1,11 @@
-import os
-os.system('git clone --recursive https://github.com/dmlc/xgboost')
-os.system('cd xgboost')
-os.system('sudo cp make/minimum.mk ./config.mk;')
-os.system('sudo make -j4;')
-os.system('sh build.sh')
-os.system('cd python-package')
-os.system('python setup.py install')
-os.system('pip install graphviz')
-os.system('pip install python-pydot')
-os.system('pip install python-pydot-ng')
-os.system('pip install -U scikit-learn scipy matplotlib')
-os.system('pip install wandb --upgrade')
-os.system('pip install tensorboardX --upgrade')
-os.system('pip install ipython --upgrade')
-os.system('wandb login 5a0e81f39777351977ce52cf57ea09c4f48f3d93 --relogin')
 
-from collections import namedtuple
-import altair as alt
-import math
 import streamlit as st
-import pandas
-import numpy
-import xgboost
-import graphviz
-from sklearn.metrics import mean_squared_error
-from sklearn.model_selection import train_test_split
-import matplotlib.pyplot
-os.system('load_ext tensorboard')
-import os
-import datetime
-from tensorboardX import SummaryWriter
-import wandb
-from wandb.xgboost import wandb_callback
-
-wandb.init(project="australian_rain", entity="epitech1")
 
 """
-# MLOPS
+# AI_ML
 """
 
+uploaded_file = st.file_uploader("Choose a picture")
 
-max_depth_input = st.slider("Max depth", 1, 100, 5)
-colsample_bytree_input = st.slider("Colsample bytree", 0.0, 1.0, 0.5)
-learning_rate_input = st.slider("Learning rate", 0.0, 1.0, 0.2)
-alpha_input = st.slider("Alpha", 1, 100, 10)
-n_estimators_input = st.slider("n estimators", 1, 100, 20)
-city_input = st.selectbox(
-     'Which city do you want to predict rain ?',
-     ("Canberra",
-    "Albury",
-    "Penrith",
-    "Sydney",
-    "MountGinini",
-    "Bendigo",
-    "Brisbane",
-    "Portland"), index=0)
-
-dataset = pandas.read_csv('weatherAUS.csv')
-
-location_dataset = dataset["Location"].unique()
-wind_dataset = dataset["WindGustDir"].unique()
-date_dataset = dataset["Date"].unique()
-
-dataset.drop(dataset.loc[dataset['Location'] != city_input].index, inplace=True)
-
-i_RainTomorrow = dataset.columns.get_loc("RainTomorrow")
-#i_Location = dataset.columns.get_loc("Location")
-i_WindGustDir = dataset.columns.get_loc("WindGustDir")
-i_Date = dataset.columns.get_loc("Date")
-yes = dataset.iat[8, dataset.columns.get_loc("RainTomorrow")]
-no = dataset.iat[0, dataset.columns.get_loc("RainTomorrow")]
-
-for i in range(len(dataset)):
-    if (dataset.iat[i, i_RainTomorrow] == yes):
-        dataset.iat[i, i_RainTomorrow] = True
-    else:
-        dataset.iat[i, i_RainTomorrow] = False
-    #dataset.iat[i, i_Location] = numpy.where(location_dataset == dataset.iat[i, i_Location])[0][0]
-    if (pandas.isna(dataset.iat[i, i_WindGustDir])):
-        dataset.iat[i, i_WindGustDir] = 0
-    else:
-        dataset.iat[i, i_WindGustDir] = numpy.where(wind_dataset == dataset.iat[i, i_WindGustDir])[0][0] + 1
-    dataset.iat[i, i_Date] = numpy.where(date_dataset == dataset.iat[i, i_Date])[0][0]
-    
-    
-dataset = dataset.astype({'RainTomorrow': 'bool'})
-#dataset = dataset.astype({'Location': 'int'})
-dataset = dataset.astype({'WindGustDir': 'int'})
-dataset = dataset.astype({'Date': 'int'})
-
-dataset.drop(columns=["WindDir9am", "WindDir3pm", "WindSpeed9am", "WindSpeed3pm", "Temp9am", "Temp3pm", "RainToday"], inplace=True)
-dataset.drop(dataset.index[dataset.isnull().any(axis=1)], 0, inplace=True)
-
-dataset["Humidity"] = 0.0
-dataset["Pressure"] = 0.0
-dataset["Cloud"] = 0.0
-
-for i in dataset.index:
-    humidity = (dataset["Humidity9am"][i] + dataset["Humidity3pm"][i]) / 2
-    dataset.at[i, "Humidity"] = humidity
-    pressure = (dataset["Pressure9am"][i] + dataset["Pressure3pm"][i]) / 2
-    dataset.at[i, "Pressure"] = pressure
-    cloud = (dataset["Cloud9am"][i] + dataset["Cloud3pm"][i]) / 2
-    dataset.at[i, "Cloud"] = cloud
-
-dataset.drop(columns=["Humidity9am", "Humidity3pm", "Pressure9am", "Pressure3pm", "Cloud9am", "Cloud3pm"], inplace=True)
-
-x, y = dataset.iloc[:,[False, False, True, True, False, True, True, True, True, True, True, True, True]],dataset.iloc[:,4]
-
-data_dmatrix = xgboost.DMatrix(data=x,label=y)
-
-X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=123)
-
-class TensorBoardCallback(xgboost.callback.TrainingCallback):
-    def __init__(self, experiment: str = None, data_name: str = None):
-        self.experiment = experiment or "logs"
-        self.data_name = data_name or "test"
-        self.datetime_ = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
-        self.log_dir = f"runs/{self.experiment}/{self.datetime_}"
-        self.train_writer = SummaryWriter(log_dir=os.path.join(self.log_dir, "train/"))
-        if self.data_name:
-            self.test_writer = SummaryWriter(log_dir=os.path.join(self.log_dir, f"{self.data_name}/"))
-
-    def after_iteration(
-        self, model, epoch: int, evals_log: xgboost.callback.TrainingCallback.EvalsLog
-    ) -> bool:
-        if not evals_log:
-            return False
-
-        for data, metric in evals_log.items():
-            for metric_name, log in metric.items():
-                score = log[-1][0] if isinstance(log[-1], tuple) else log[-1]
-                if data == "train":
-                    self.train_writer.add_scalar(metric_name, score, epoch)
-                else:
-                    self.test_writer.add_scalar(metric_name, score, epoch)
-
-        return False
-        
-xg_reg = xgboost.XGBRegressor(colsample_bytree = colsample_bytree_input, learning_rate = learning_rate_input, max_depth = max_depth_input, alpha = alpha_input, n_estimators = n_estimators_input, eval_metric = ['rmse', 'error', 'logloss', 'map'],
-        callbacks=[TensorBoardCallback(experiment='exp_1', data_name='test')])
-
-xg_reg.fit(X_train,y_train, eval_set=[(X_train, y_train)])
-
-preds = xg_reg.predict(X_test)
-
-rmse = numpy.sqrt(mean_squared_error(y_test, preds))
-st.write("RMSE: %f" % (rmse))
-
-params = {'colsample_bytree': colsample_bytree_input,'learning_rate': learning_rate_input,
-                'max_depth': max_depth_input, 'alpha': alpha_input}
-
-cv_results = xgboost.cv(dtrain=data_dmatrix, params=params, nfold=3,
-                    num_boost_round=50,early_stopping_rounds=10,metrics="rmse", as_pandas=True, seed=123)
-
-st.write((cv_results["test-rmse-mean"]).tail(1))
-
-xg_reg = xgboost.train(params=params, dtrain=data_dmatrix, num_boost_round=10)
-
-os.system('tensorboard --logdir runs')
-
-#xgboost.plot_tree(xg_reg,num_trees=0)
-#matplotlib.pyplot.rcParams['figure.figsize'] = [200, 200]
-#matplotlib.pyplot.show()
-
-#xgboost.plot_importance(xg_reg)
-#matplotlib.pyplot.rcParams['figure.figsize'] = [5, 5]
-#matplotlib.pyplot.show()
-
-#xg_reg = xgboost.train(params=params, dtrain=data_dmatrix, num_boost_round=10, callbacks=[wandb_callback()])
 
-# MLOPS - W&B analytics
-# added the wandb to the callbacks
+st.image(load_image(uploaded_file),width=250)