app.py

import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pickle
import seaborn as sns
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from mpl_toolkits.mplot3d import Axes3D
from PIL import Image
from plotly.offline import iplot
import plotly as py
import plotly.tools as tls
import cufflinks as cf
import plotly.figure_factory as ff
import plotly.express as px
import streamlit.components.v1 as components


py.offline.init_notebook_mode(connected = True)
cf.go_offline()
cf.set_config_file(theme='white')

df = pd.read_csv("forestfires.csv")
#all functions
# @st.cache
def welcome():

    return "Welcome All"
# @st.cache    
def predict_forest_fire(x,y,month,ffmc,dmc,dc,temp):
    prediction = model.predict([[x,y,month,ffmc,dmc,dc,temp]])
    print(prediction)
    return prediction

# @st.cache
def main_prediction():
    # st.title("Forest Fire Prediction")
    # st.markdown("""<center><img src='https://upload.wikimedia.org/wikipedia/commons/d/d8/Deerfire_high_res_edit.jpg' width='500px'></center>""",unsafe_allow_html=True)
    # st.markdown("""<br>""",unsafe_allow_html=True)
    
    html_temp = """
    <div style="background-color:#00b3ff;padding:10px">
    <h2 style="color:white;text-align:center;">Forest Fire Prediction</h2>
    </div>
    """
    st.markdown(html_temp,unsafe_allow_html=True)
    st.markdown("""<br>""",unsafe_allow_html=True)
    x = st.text_input("X","Enter an integer e.g. 5")
    y = st.text_input("Y","Enter an integer e.g. 5")
    month = st.text_input("Month","e.g. for February enter 2")
    ffmc = st.text_input("FFMC","e.g. 91.7")
    dmc = st.text_input("DMC","e.g. 26.2")
    dc = st.text_input("DC","e.g. 77.5")
    temp = st.text_input("Temp","e.g. 14.6")
    # num = st.number_input("Enter a number",5)
    result=""
    if st.button("Predict"):
        try:
            result=predict_forest_fire(x,y,month,ffmc,dmc,dc,temp)
            st.success('The output is {}'.format(result))
            st.balloons()
            
        except:
            st.error("Please fill up all the inputs and make sure all are in numeric form")
    # st.balloons()
    if st.button("About"):
        """The Project is Called `Forest Fire Prediction`, this is the [GitHub Repository README](https://github.com/dsc-iem/AI-Hacktoberfest/blob/master/README.md) 
        ,from here you can learn more about the input parameters."""
        
        
# @st.cache
def corr_plot():
    # st.write("You selected",len(location),"locations")
    
    # st.text("see plots")
    # df = pd.read_csv("forestfires.csv")
    # st.dataframe(df)
    
    corr = df.corr()

    mask = np.triu(np.ones_like(corr, dtype=np.bool))

    # Set up the matplotlib figure
    f, ax = plt.subplots(figsize=(5, 5))

    # Generate a custom diverging colormap
    cmap = sns.diverging_palette(220, 10, as_cmap=True)

    # Draw the heatmap with the mask and correct aspect ratio
    sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
                square=True, linewidths=.5, cbar_kws={"shrink": .5})
    plt.title('Correlation Plot',size=15)

    st.pyplot(f)

@st.cache(suppress_st_warning=True)
def pair_plot():
    # df = pd.read_csv("forestfires.csv")

    # fig = plt.figure(figsize=(3,4))
    st.set_option('deprecation.showPyplotGlobalUse', False)

    # fig = plt.figure()
    # sns.set_style('whitegrid')
    sns.pairplot(df)
    plt.title("Pair Plot")
    # plt.show()
    st.pyplot()


def V_corr_plot():
    # df = pd.read_csv("forestfires.csv")
    corr_new_train=df.corr()
    fig = plt.figure(figsize=(3,4))
    sns.heatmap(corr_new_train[['area']].sort_values(by=['area'],ascending=False).head(60),vmin=-1, cmap='seismic', annot=True)
    plt.title("Vertical Correlation plot")
    st.pyplot(fig)   

def threeD_Plot():
    # df = pd.read_csv("forestfires.csv")
    dmc = list(df['DMC'])
    temp = list(df['temp'])
    area = list(df['area'])
    train_f1 = pd.DataFrame(dmc,columns=['Dmc'])
    train_f2 = pd.DataFrame(temp,columns=['Temp'])
    train_target = pd.DataFrame(area,columns=['Target'])
    train_data = pd.concat([train_f1,train_f2,train_target],axis = 1)
    # train_data.head(5)
    sns.set_style("whitegrid", {'axes.grid' : False})

    fig = plt.figure(figsize=(3,3))

    ax = Axes3D(fig) # Method 1
    # ax = fig.add_subplot(111, projection='3d') # Method 2

    x = np.random.uniform(1,20,size=20)
    y = np.random.uniform(1,100,size=20)
    z = np.random.uniform(1,100,size=20)


    ax.scatter(train_data['Dmc'], train_data['Temp'], train_data['Target'], c=train_data['Dmc'],marker='o')
    ax.set_xlabel('Dmc')
    ax.set_ylabel('Temp')
    ax.set_zlabel('Target')
    ax.set_title("3D Scatter plot of DMC,temp and area")

    # plt.show()
    st.pyplot(fig)

def get_pie_plot(feature):
  df['area'] = df['area'].apply(lambda x : np.log(x+1))
  df1 = df.groupby(feature).sum()  
  df1 = df1[df1.area >0 ]

  theme = plt.get_cmap('hsv')
  cs = theme(np.arange(len(df1.area))/len(df1.area))
  fig, ax = plt.subplots(figsize = (3, 3))
  plt.pie(df1.area,labels = df1.index, colors = cs)
  plt.title("Relative amount of area affected per "+feature, fontsize = 14)
  # plt.show()
  st.pyplot(fig)

def load_img(img_name):
    img = Image.open(img_name)
    st.image(img,width=800)

#sidebar Edit
# st.title("hello")
html_img = """<center><img src="https://i.ibb.co/VY5wCkN/47480912-png.png" width="130px"></center>"""
st.sidebar.markdown(html_img,unsafe_allow_html=True)

st.sidebar.markdown("""## Navigation Bar: <br> """,unsafe_allow_html=True)
st.markdown("""<br><br>""",unsafe_allow_html=True)
red = st.sidebar.radio(" ",["Prediction","Exploratory Data Analysis","About the Project","Collaborators of the Project"])
# st.markdown("""<br></br> <br>""",unsafe_allow_html=True)
st.sidebar.markdown("""<br><br><br><br><br> Thank you for visiting the site🤗""",unsafe_allow_html=True)
st.sidebar.markdown(""" [  Our Github Repository](https://github.com/dsc-iem/AI-Hacktoberfest)""",unsafe_allow_html=True)



if red=="Prediction":
    pkl_file = open("ffp2_model.pkl","rb")
    model = pickle.load(pkl_file)
    main_prediction()

if red == "Exploratory Data Analysis":
    
    html_header = """
    <div style="background-color:#00ff2a;padding:10px">
    <h2 style="color:white;text-align:center;">Exploratory Data Analysis</h2>
    </div>
    """
    st.markdown(html_header,unsafe_allow_html=True)
    st.markdown("""<br>""",unsafe_allow_html=True)
    tab = st.checkbox("Check out Data")
    if tab:
        st.dataframe(df)
        
    st.subheader("Select an Analysis Type")
    red2 = st.radio(" ",["Uni-variate Analysis","Bi-variate Analysis","Multi-variate Analysis"])
    
    if red2 == "Multi-variate Analysis":
    

        # MultiSelect
        plot_type = st.selectbox("Choose a plot type",["Correlational plot","Pair Plot","Vertical Correlation Plot","3D Plot"])
        
        if plot_type=="Correlational plot":
            corr_plot()
        
        
        if plot_type == "Pair Plot":
            pair_plot()
            
        if plot_type == "Vertical Correlation Plot":
            V_corr_plot()
            
        if plot_type == "3D Plot":
            threeD_Plot()
                
    if red2 == "Bi-variate Analysis":
        
        plot_type1 = st.selectbox("Choose a plot type",["Pie plot of amount of area affected per month","Pie Plot of amount of area affected per day","Box Plot for affected forest area per month","Box Plot for affected forest area per day","Plot for impact of FFMC on Forest Fire","Plot for impact of DMC on Forest Fire","Plot for impact of DC on Forest Fire","Plot for impact of ISI on Forest Fire","Plot for impact of Temperature on Forest Fire","Plot for impact of RH on Forest Fire","Plot for impact of Wind on Forest Fire","Plot for impact of Rain on Forest Fire"])
        if plot_type1=="Pie plot of amount of area affected per month":
            get_pie_plot("month")
        
        if plot_type1=="Pie Plot of amount of area affected per day":
            get_pie_plot("day")
            
        if plot_type1 == "Box Plot for affected forest area per month":
            html_temp = """<center><img src="https://i.imgur.com/Gd26hjy.png" width="700px"></center>"""
            st.markdown(html_temp,unsafe_allow_html=True)
            
        if plot_type1=="Box Plot for affected forest area per day":
            html_temp = """<center><img src="https://i.imgur.com/Uf2avSv.png" width="700px"></center>"""
            st.markdown(html_temp,unsafe_allow_html=True)
            
        if plot_type1=="Plot for impact of FFMC on Forest Fire":
            # load_img("impact1.png")
            html_temp = """<center><img src="https://i.imgur.com/v1mEJxr.png" width="850px"></center>"""
            st.markdown(html_temp,unsafe_allow_html=True)
            
        if plot_type1=="Plot for impact of DMC on Forest Fire":
            # load_img("impact2.png")
            html_temp = """<center><img src="https://i.imgur.com/rQS688N.png" width="850px"></center>"""
            st.markdown(html_temp,unsafe_allow_html=True)
            
        if plot_type1=="Plot for impact of DC on Forest Fire":
            # load_img("impact3.png")
            html_temp = """<center><img src="https://i.imgur.com/D7iLsh9.png" width="850px"></center>"""
            st.markdown(html_temp,unsafe_allow_html=True)
            
        if plot_type1=="Plot for impact of ISI on Forest Fire":
            # load_img("impact4.png")
            html_temp = """<center><img src="https://i.imgur.com/9elPIJQ.png" width="850px"></center>"""
            st.markdown(html_temp,unsafe_allow_html=True)
            
        if plot_type1=="Plot for impact of Temperature on Forest Fire":
            # load_img("impact5.png")
            html_temp = """<center><img src="https://i.imgur.com/Y32p4bE.png" width="850px"></center>"""
            st.markdown(html_temp,unsafe_allow_html=True)
        
        if plot_type1=="Plot for impact of RH on Forest Fire":
            # load_img("impact6.png")
            html_temp = """<center><img src="https://i.imgur.com/nKXQT3z.png" width="850px"></center>"""
            st.markdown(html_temp,unsafe_allow_html=True)
        if plot_type1=="Plot for impact of Wind on Forest Fire":
            # load_img("impact7.png")
            html_temp = """<center><img src="https://i.imgur.com/Uzl040u.png" width="850px"></center>"""
            st.markdown(html_temp,unsafe_allow_html=True)
            
            
        if plot_type1=="Plot for impact of Rain on Forest Fire":
            # load_img("impact7.png")
            html_temp = """<center><img src="https://i.imgur.com/QPOBOVU.png" width="850px"></center>"""
            st.markdown(html_temp,unsafe_allow_html=True)
            
    if red2 == "Uni-variate Analysis":
        
        plot_type2 = st.selectbox("Choose a plot type",["Scatter Plot of Area",
                                    "Line plot of Area",
                                    "Density(PDF) Plot of Area",
                                    "Histogram of Area",
                                    "Violin Plot of Area"])

        if plot_type2=="Scatter Plot of Area":
            fig = df['area'].iplot(kind = 'scatter' , mode = 'markers',title="Scatter plot of area",
                            yTitle='area',xTitle = 'id',asFigure=True)

            st.plotly_chart(fig)
            
        if plot_type2=="Line plot of Area":
            fig = df['area'].iplot(title="Line plot of area",
                            yTitle='area',xTitle = 'id',asFigure=True)
                            
            st.plotly_chart(fig)
            
        if plot_type2=="Density(PDF) Plot of Area":
            np.random.seed(1)


            x = np.array(df['area'])
            hist_data = [x]
            group_labels = ['area'] 

            fig = ff.create_distplot(hist_data, group_labels)
            st.plotly_chart(fig)
            
            
        if plot_type2=="Histogram of Area":
            fig = pd.DataFrame(df["area"]).iplot(kind="histogram", 
                bins=40, 
                theme="white",
                title="Histogram of area",
                xTitle='area', 
                yTitle='Count',
                asFigure=True)
            
            st.plotly_chart(fig)
            
        if plot_type2=="Violin Plot of Area":
            fig = px.violin(df, y="area", box=True, 
                points='all'
               )
               
            st.plotly_chart(fig)
            
            
            
        
if red == "About the Project":
    # st.text("hello world")
    components.html(
    """
    <div style="background-color:#ff0055;padding:10px">
    <h1 style="color:white;text-align:center;">About the Project</h1>
    </div>
    
    """
    )
    img_top = """<center><img src="https://i.imgur.com/yOS7IGv.png" width="700px"></center>"""
    st.markdown(img_top,unsafe_allow_html=True)
    topic= """
    
    <br>
    
    The main motive of this project is to predict the amount of area that can get 
    burned in a forest fire based on some parameters like `Humidity(RH)`, `Wind(wind)`,`Rain(rain)`, 
    `Temperature(temp)` etc. 
    
    The project is a part of Hacktoberfest contribution and it has been initiated by <a href="https://github.com/dsc-iem">DSC-IEM</a> .
    We used different Model Building techniques for building the model and did an in-depth exploratory analysis 
    of the provided data. And except these things, creating a user-friendly web-app and deploying it in cloud is 
    also an integral part of a Data Science life cycle. So, we also have put together this web-app to show that.
    
    <p style="color:blue;">If you liked this project then it will be really motivating for us if you can star our repository😄.</p>
     
    
    <br>
    
    [![ReadMe Card](https://github-readme-stats.vercel.app/api/pin/?username=soumya997&repo=AI-Hacktoberfest&theme=light)](https://github.com/dsc-iem/AI-Hacktoberfest)

    """
    
    st.markdown(topic,unsafe_allow_html=True)
    
    
    
    
if red=="Collaborators of the Project":
    
    # html_colab = """
    # <div style="background-color:#29ffea;padding:10px">
    # <h2 style="color:white;text-align:center;">Collaborators:</h2>
    # </div><br>
    # """
    # st.markdown(html_colab,unsafe_allow_html=True)
    # img_top = """<center><img src=" https://github.com/debangeedas.png?sixe=40" width="700px"></center>"""
    # st.markdown(img_top,unsafe_allow_html=True)
    # st.markdown("""<br>""",unsafe_allow_html=True)
    
    components.html(
    """
    <div style="background-color:#ffe100;padding:10px">
    <h1 style="color:white;text-align:center;">Collaborators:</h1>
    </div>
    <br>
    <br>
    """
    )
    
    components.html(
    
    """
    <html>
        <head>
            
        </head>

        <body>
            <a>
                <strong style="font-size:20px">
                    <!-- 1khanfarhan10 &nbsp;&nbsp;-->
                    <pre class="tab">1khanfarhan10 <a style="font-size:14px">26 commits </a><a style="color: #2bff00;font-size:10px">18,069++ </a><a style="color: #FF0000;font-size:10px">9,501--</a>   soumya997 <a style="font-size:14px">3 commits </a><a style="color: #2bff00;font-size:10px">19,938++ </a><a style="color: #FF0000;font-size:10px">3,460--</a></pre>
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                    <script src="//cdn.jsdelivr.net/github-cards/latest/widget.js"></script>
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                    <script src="//cdn.jsdelivr.net/github-cards/latest/widget.js"></script>
                </strong> 
                <br>
                <strong style="font-size:20px">
                    <!-- 1khanfarhan10 &nbsp;&nbsp;-->
                    <pre class="tab">Nibba2018 <a style="font-size:14px">2 commits </a><a style="color: #2bff00;font-size:10px">25++ </a><a style="color: #FF0000;font-size:10px">27--</a>           Dsantra92 <a style="font-size:14px">1 commits  </a><a style="color: #2bff00;font-size:10px">1,129++ </a><a style="color: #FF0000;font-size:10px">0--</a></pre>
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                </strong>
                <br> 
                <strong style="font-size:20px">
                    <!-- 1khanfarhan10 &nbsp;&nbsp;-->
                    <pre class="tab">debangeedas <a style="font-size:14px">1 commits </a><a style="color: #2bff00;font-size:10px">40++ </a><a style="color: #FF0000;font-size:10px">0--</a>          BALaka-18 <a style="font-size:14px">1 commits </a><a style="color: #2bff00;font-size:10px">5++ </a><a style="color: #FF0000;font-size:10px">4--</a></pre>
                    <div class="github-card" data-github="debangeedas" data-width="350" data-height="" data-theme="default"></div>
                    <script src="//cdn.jsdelivr.net/github-cards/latest/widget.js"></script>
                    <div class="github-card" data-github="BALaka-18" data-width="350" data-height="" data-theme="default"></div>
            <script src="//cdn.jsdelivr.net/github-cards/latest/widget.js"></script>
                </strong> 
                
        </body>
    </html>


    


        """,
        height=700,scrolling=True,width=800
    )