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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
+mobile01.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,42 @@
+import streamlit as st
+import pickle
+import numpy as np
+
+# import the model
+pipe = pickle.load(open('pipe.pkl','rb'))
+df = pickle.load(open('df.pkl','rb'))
+
+st.title("Smartphone Price Predictor")
+# Main page content
+st.image('mobile.png', use_column_width=True)
+
+# brand
+Company = st.selectbox('Brand',df['Brand'].unique())
+
+# year
+Released_Year = st.selectbox('Released Year',df['Released Year'].unique())
+
+# OS
+Operating_System = st.selectbox('OS',df['OS'].unique())
+
+# size
+Display = st.number_input('Display (Inches)')
+
+# Camera
+Camera = st.number_input('Camera (MP)')
+
+# resolution
+Camera_Resolution= st.selectbox('Camera Resolution',df['Camera Resolution'].unique())
+
+# Ram
+Ram = st.number_input('Ram (GB)')
+
+# Battery
+Battery = st.number_input('Battery (mAh)')
+
+
+
+if st.button('Predict Price'):
+    query = np.array([Company, Released_Year, Operating_System, Display, Camera, Camera_Resolution, Ram, Battery])
+    query = query.reshape(1, -1)
+    st.title("The predicted price of this configuration mobile is " + str(int(np.exp(pipe.predict(query)[0]))) + ' TK.')

df.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bdf31763fce8631ef59884d498005c2348358433b440fddf80a86bc21daa20d1
+size 133166

pipe.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4ea307b7a098fd96750149272ace12963121e89ab8bad9075131f21d3f4504f
+size 588657