app file

app.py

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+import streamlit as st
+import pickle
+import pandas as pd
+import numpy as np
+
+# for laptop we use the model is randomforest r2_score:-89%
+def Laptop():
+    pipe = pickle.load(open('pipe.pkl','rb'))
+
+    df = pickle.load(open('df.pkl','rb'))
+
+    st.title('Laptop Price Predictor')
+
+    # brand
+    company = st.selectbox("Brand",df['Company'].unique())
+
+    # type of laptop
+    type = st.selectbox("Type",df['TypeName'].unique())
+
+    # ram
+    ram = st.selectbox("RAM(in GB)",[2,4,6,8,12,16,24,32,64])
+
+    # weight
+    weight = st.number_input('Weight of the Laptop')
+
+    # touchscreen
+    touchscreen = st.selectbox("TouchScreen",['No','Yes'])
+
+    # IPS
+    ips = st.selectbox('IPS',['No','Yes'])
+
+    # screen size
+    screen_size = st.number_input("Screen Size")
+
+    # resolution
+    resolution = st.selectbox('Screen Resolution',['1920x1080','1366x768','1600x900','3840x2160','3200x1800','2880x1800','2560x1600','2560x1440','2304x1440'])
+
+    #cpu
+    cpu = st.selectbox('CPU',df['Cpu brand'].unique())
+
+    hdd = st.selectbox('HDD(in GB)',[0,128,256,512,1024,2048])
+
+    ssd = st.selectbox('SSD(in GB)',[0,8,128,256,512,1024])
+
+    gpu = st.selectbox('GPU',df['Gpu brand'].unique())
+
+    os = st.selectbox('OS',df['os'].unique())
+
+    if st.button('Predict Price'):
+        # query
+        ppi = None
+        if touchscreen == 'Yes':
+            touchscreen = 1
+        else:
+            touchscreen = 0
+
+        if ips == 'Yes':
+            ips = 1
+        else:
+            ips = 0
+
+        X_res = int(resolution.split('x')[0])
+        Y_res = int(resolution.split('x')[1])
+        ppi = ((X_res**2) + (Y_res**2))**0.5/screen_size
+        query = np.array([company,type,ram,weight,touchscreen,ips,ppi,cpu,hdd,ssd,gpu,os])
+
+        query = query.reshape(1,12)
+        st.title("The predicted price of this configuration is " + str(int(np.exp(pipe.predict(query)[0]))))
+
+        # st.markdown('<b><font color="orange" size="30">The predicted price of this configuration is: </font></b>', unsafe_allow_html=True)
+
+        # st.title(str(int(np.exp(pipe.predict(query)[0]))))
+
+
+def Mobile():
+        pipe = pickle.load(open('pipe8.pkl','rb'))
+
+        df = pickle.load(open('X_train.pkl','rb'))
+
+        st.title('Mobile Price Predictor')
+
+        # ['mobile_color', 'disp_size', 'os', 'num_cores', 'mp_speed',
+        #        'int_memory', 'ram', 'battery_power', 'mob_width', 'mob_height',
+        #        'mob_depth', 'mob_weight', 'res_dim_1', 'res_dim_2', 'p_cam_max',
+        #        'p_cam_count', 'f_cam_max', 'f_cam_count', '2G', '3G', '4G', '4GVOLTE',
+        #        '5G']
+
+        # mobile color
+        color = st.selectbox("Color",df['mobile_color'].unique())
+
+        # disp_size
+        disp_size = st.number_input('Display Size(in inches)')
+
+        # os
+        os = st.selectbox("Operating System",sorted(df['os'].unique()))
+
+        # num_cores
+        num_cores = st.selectbox("No.of Cores",sorted(df['num_cores'].unique()))
+
+        # speed of cpu
+        mp_speed = disp_size = st.number_input('processor speed',help='2GHz processor')
+
+        # memory
+        int_memory = st.selectbox("Internal Memory",sorted(df['int_memory'].unique()))
+
+        # ram
+        ram = st.selectbox("RAM",sorted(df['ram'].unique(),reverse=True))
+
+        # battery_power
+        battery_power = st.selectbox("Battery",sorted(df['battery_power'].unique(),reverse=True))
+
+
+        # mob_width
+        mob_width = st.number_input('Mobile Width(mm)')
+
+        # mob_height
+        mob_height = st.number_input('Mobile Height(mm)')
+
+        # mob_depth
+        mob_depth = st.number_input('Mobile Depth(mm)')
+
+        # mob_weight
+        mob_weight = st.number_input('Mobile Weight')
+        
+        # resolution
+        resolution = st.text_input("Enter Resulution")
+
+        # p_cam_max
+        p_cam_max = st.selectbox("Max rear camera",sorted(df['p_cam_max'].unique(),reverse=True),help='Primay Max camera')
+
+        # p_cam_count
+        p_cam_count = st.selectbox("Count of rear cameras",sorted(df['p_cam_count'].unique()))
+
+        # f_cam_max
+        f_cam_max = st.selectbox("Max front camera",sorted(df['f_cam_max'].unique()),help='Secondary Max camera')
+
+        # f_cam_count
+        f_cam_count = st.selectbox("Toatl no. of front cameras",[1,2],help='total no.of cameras including max camera')
+
+        # Network
+        network_choices = {
+        "2G": df['2G'].unique(),
+        "3G": df['3G'].unique(),
+        "4G": df['4G'].unique(),
+        "4GVOLTE": df['4GVOLTE'].unique(),
+        "5G": df['5G'].unique()
+        }
+
+        selected_network = st.selectbox("Select Network", network_choices.keys())
+        # selected_value = 1
+        if selected_network == '2G':
+                G2 = 1
+                G3 = 0
+                G4 = 0
+                G4VOLTE = 0
+                G5 = 0
+        elif selected_network == '3G':
+                G2 = 0
+                G3 = 1
+                G4 = 0
+                G4VOLTE = 0
+                G5 = 0
+        elif selected_network == '4G':
+                G2 = 0
+                G3 = 0
+                G4 = 1
+                G4VOLTE = 0
+                G5 = 0
+        elif selected_network == '4GVOLTE':
+                G2 = 0
+                G3 = 0
+                G4 = 0
+                G4VOLTE = 1
+                G5 = 0
+        else:
+                G2 = 0
+                G3 = 0
+                G4 = 0
+                G4VOLTE = 0
+                G5 = 1
+                
+
+        # 'mobile_color', 'dual_sim', 'disp_size', 'os', 'num_cores', 'mp_speed',
+        #        'int_memory', 'ram', 'battery_power', 'mob_width', 'mob_height',
+        #        'mob_depth', 'mob_weight', 'res_dim_1', 'res_dim_2', 'p_cam_max',
+        #        'p_cam_count', 'f_cam_max', 'f_cam_count', '2G', '3G', '4G', '4GVOLTE',
+        #        '5G'
+        if st.button('Predict Mobile Price'):
+                res_dim_1 = int(resolution.split('x')[0])
+                res_dim_2 = int(resolution.split('x')[1])
+
+
+                query = np.array([color,disp_size,os,num_cores,mp_speed,int_memory,ram,battery_power,mob_width,mob_height,mob_depth,mob_weight,res_dim_1,res_dim_2,p_cam_max,p_cam_count,f_cam_max,f_cam_count,G2,G3,G4,G4VOLTE,G5])
+
+                query = query.reshape(1,23)
+                
+                st.title("The predicted price of this configuration is " + str(int(pipe.predict(query)[0])))
+
+# pip install pandas==1.5.3
+
+
+
+# Define two buttons with unique keys
+button_clicked1 = st.button("Click For Mobile Price Predictor!", key="button1")
+button_clicked2 = st.button("Click For Laptop Price Predictor!", key="button2")
+
+# Use a session state to track whether each button has been clicked
+if 'button1_click_state' not in st.session_state:
+    st.session_state.button1_click_state = False
+
+if 'button2_click_state' not in st.session_state:
+    st.session_state.button2_click_state = False
+
+# Check if each button was clicked
+if button_clicked1:
+    st.session_state.button1_click_state = True
+    st.session_state.button2_click_state = False
+
+if button_clicked2:
+    st.session_state.button2_click_state = True
+    st.session_state.button1_click_state = False
+
+# Display content based on button clicks
+if st.session_state.button1_click_state:
+    # Clear previous content
+    st.empty()
+    # Display content for the first button
+    Mobile()
+
+if st.session_state.button2_click_state:
+    # Clear previous content
+    st.empty()
+    # Display content for the second button
+    Laptop()
+
+
+