Upload pycaret_training.py

pycaret_training.py

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+import pandas as pd
+from pycaret.classification import *
+import streamlit as st
+
+import streamlit as st
+import pandas as pd
+
+def app():
+    import numpy as np
+    from importlib import import_module
+    import joblib
+
+    st.title('Streamlit Example')
+
+    st.write("Titanic Dataset")
+
+    #classifier_name = st.text_input("Classifier_name")
+
+    file_upload = st.file_uploader("Upload csv file for train", type=["csv"])
+
+    if file_upload is not None:
+        train = pd.read_csv(file_upload)
+
+    """file_upload = st.file_uploader("Upload csv file for Y_train", type=["csv"])
+
+    if file_upload is not None:
+        Y_train= pd.read_csv(file_upload) """
+
+    
+
+    if st.button("Train"):
+
+        train["Survived"]=train["Survived"].apply(lambda x:"Survived" if x==1 else "Dead")
+        s=setup(train,target = 'Survived',
+             numeric_imputation = 'mean',
+             categorical_features = ['Sex','Embarked'], 
+             ignore_features = ['Name','Ticket','Cabin'],
+             silent = True,
+             log_experiment = True, 
+             experiment_name = 'titanic')
+        g_boost  = create_model('gbc') 
+        tuned_gb = tune_model(g_boost)
+        rand_for=create_model('rf') 
+        log_reg=create_model('lr')
+        save_model(g_boost , 'deploy_gboost')
+        save_model(rand_for,'deploy_rand_for')
+        
+        save_model(log_reg,'deploy_log_reg')
+
+
+        #model = create_model(model_name)
+       
+        #filename = model_name + "trained.sav"
+        #joblib.dump(model,filename)
+
+        """directory = os.path.abspath(os.getcwd())
+        with open(os.path.join(directory,model_name),"wb") as f: 
+          f.write(model_name.getbuffer())"""
+        st.text("Models saved")
+
+
+    
+
+    
+
+    
+  
+"""
+def app():
+    st.title('PYCARET')
+    st.write('Welcome to pycaret training')
+
+    #train = pd.read_csv('train.csv')
+    #test = pd.read_csv('test.csv')
+    file_upload = st.file_uploader("Upload csv file for X_train", type=["csv"])
+
+    if file_upload is not None:
+        train = pd.read_csv(file_upload)
+
+        train["Survived"]=train["Survived"].apply(lambda x:"Survived" if x==1 else "Dead")
+        clf1 = setup(data = train, target='Survived'
+               
+                )
+    if st.button("Train"):
+        model_name = classifier_name.split(".")
+        model = create_model(model_name)
+        
+        filename = model_name + "trained.sav"
+        joblib.dump(model,filename)
+
+        directory = os.path.abspath(os.getcwd())
+        with open(os.path.join(directory,model_name),"wb") as f: 
+          f.write(model_name.getbuffer())
+        st.text("Model saved")"""
+