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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+src/charts1.jpg filter=lfs diff=lfs merge=lfs -text

src/eda.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from phik.report import plot_correlation_matrix
+from PIL import Image
+
+#membuat function untuk nantinya dipanggil di app.py
+def run():
+    st.title('Welcome to Explaration Data Analysis')
+# #Memanggil data csv 
+#     df= pd.read_csv(r'rideshare_kaggle.csv')
+
+# #menampilakn 5 data teratas
+#     st.table(df.head(5))
+
+
+#menampilakn phik matrix
+    st.title('phik correlation matrix')
+    image = Image.open('charts1.jpg')
+    st.image(image, caption='figure 1')
+
+#menampilkan penjelasan 
+    with st.expander('Explanation'):
+        st.caption('lorem ipsum')
+
+
+
+

src/models.py

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+import streamlit as st
+import pandas as pd
+import pickle
+from PIL import Image
+
+def run():
+# Load All Files
+
+    with open('pipeline_model.pkl', 'rb') as file:
+        full_process = pickle.load(file)
+
+
+    distance = st.number_input(label='input your distance here',min_value=0.0,max_value=7.5)
+    surge_multiplier = st.selectbox(label='choose your surge_multiplier here',options=[1.  , 1.25, 2.5 , 2.  , 1.75, 1.5 , 3.  ])
+    name = st.selectbox(label='choose your cab name here',options=['Shared', 'Lux', 'Lyft', 'Lux Black XL', 'Lyft XL', 'Lux Black',
+       'UberXL', 'Black', 'UberX', 'WAV', 'Black SUV', 'UberPool'])
+    product_id = st.selectbox(label='choose your product id here',options=['lyft_line', 'lyft_premier', 'lyft', 'lyft_luxsuv', 'lyft_plus',
+       'lyft_lux', 'uber_line', 'uber_premier', 'uber', 'uber_luxsuv',
+       'uber_plus', 'uber_lux'])
+    
+    st.write('In the following is the result of the data you have input : ')
+    
+    data_inf = pd.DataFrame({
+         'distance' : distance,
+        'surge_multiplier' : surge_multiplier,
+        'name' : name ,
+        'product_id' : product_id,
+        }, index=[0])
+
+    st.table(data_inf)
+
+
+    if st.button(label='predict'):
+    
+        # Melakukan prediksi data dummy
+        y_pred_inf = full_process.predict(data_inf)
+
+        
+        st.metric(label="Here is a prediction of your travel costs : ", value = y_pred_inf[0])
+
+    # If your data is a classification, you can follow the example below 
+        # if y_pred_inf[0] == 0:
+        #     st.write('Pasien tidak terkena jantung')
+        #     st.markdown("[Cara Cegah Serangan Jantung](https://www.siloamhospitals.com/informasi-siloam/artikel/cara-cegah-serangan-jantung-di-usia-muda)")
+
+        # else:
+        #     st.write('Pasien kemungkinan terkena jantung')
+        #     st.markdown("[Cara Hidup Sehat Sehabis Terkena Serangan Jantung](https://lifestyle.kompas.com/read/2021/11/09/101744620/7-pola-hidup-sehat-setelah-mengalami-serangan-jantung?page=all)")

src/pipeline_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f9376ef7f21c5852bd0495963a0f41a34f34fb18174f2fa45667da8d931e56a4
+size 2648