import os
import time
import warnings
from sklearn import metrics, preprocessing
from sklearn.calibration import LabelEncoder
import streamlit as st
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer,TfidfVectorizer
from sklearn.pipeline import Pipeline
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import confusion_matrix
from sklearn import tree
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn import svm
from sklearn.naive_bayes import GaussianNB
from joblib import dump, load
from matplotlib import pyplot as plt
from sklearn.tree import plot_tree
import sweetviz as sv
from pathlib import Path
import hashlib
import google.generativeai as genai
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer,TfidfVectorizer
from sklearn.pipeline import Pipeline
from streamlit_extras.metric_cards import style_metric_cards
from streamlit_extras.colored_header import colored_header

st.set_page_config(
    page_title="Codeless-ML",
    page_icon=":📈:",
    layout="wide",
         menu_items={
        'About': "# Under Construction"
    }
    )
@st.experimental_fragment
def main():
    # Set up Streamlit page
    warnings.filterwarnings("ignore")
    hide_st_style = """
            <style>
            MainMenu {visibility: hidden;}
            footer {visibility: hidden;}
            # header {visibility: hidden;}
            </style>
            """
    st.markdown(hide_st_style, unsafe_allow_html=True)

    page_bg_img = '''
        <style>
        [data-testid = "stAppViewContainer"] {
        background-image: url("https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRvIlgLZYLc2E7gKTaK1kETo-amo5l6mrWQlh7owYgWXw&s");
        background-size: cover;
        }
        [data-testid = "stFileUploaderDropzone"]{
        # background-image: url("https://cdn.dribbble.com/users/1076456/screenshots/17355064/media/1486af881edbacd6db74d34b8e8190f4.png?resize=1000x750&vertical=center");
        background-size: cover;
        }
        [data-testid = "stHeader"] {
        background-color : rgba(0,0,0,0);
        }
        [data-testid = "stToolbar"] {
        right: 2rem;
        }
        </style>
      '''

    st.markdown(page_bg_img, unsafe_allow_html=True)
    

    custom_css = '''
    <style>
        [data-testid = "stDataFrameResizable"] {
            width: 1800px !important;
            height: 320px !important;
        }
    </style>
    '''

    st.markdown(custom_css, unsafe_allow_html=True)

    st.title("Codeless Machine Learning..")
    colored_header(
        label="Train Your Model Online",
        description="We currently support csv file format only",
        color_name="violet-70",
    )

    st.text("Add Dataset in CSV format only")

    st.divider()

    upload_dir = "./uploads"
    if not os.path.exists(upload_dir):
        os.makedirs(upload_dir)

    try:
        uploaded_file = st.file_uploader("Choose a CSV file",type=['csv'],accept_multiple_files=False)
    except:
        st.warning("Only Csv format supported")

    left_column, right_column = st.columns([0.4,1])
    with left_column:
        st.text("Use Example Dataset")
    with right_column:
        on0 = st.toggle('Yes!! (drug200.csv)',key="check0")
        onn1 = st.toggle('Yes!! (mentalhealth.csv)',key="checkk1")
        # if on0:
        #     uploaded_file = ".\drug200.csv"
    st.divider()
    df=None
    if on0:
        uploaded_file = "./drug200.csv"  # Adjust the path as necessary
        if os.path.exists(uploaded_file):  # Check if the file exists
            df = pd.read_csv(uploaded_file)
            st.subheader("CSV Data")
            st.write(df)
        else:
            st.error("File 'drug200.csv' does not exist.")
    elif onn1:
        uploaded_file = "./mentalhealth.csv"  # Adjust the path as necessary
        if os.path.exists(uploaded_file):  # Check if the file exists
            df = pd.read_csv(uploaded_file)
            st.subheader("CSV Data")
            st.write(df)
        else:
            st.error("File 'drug200.csv' does not exist.")
    else:
        if uploaded_file is not None:
            file_path = os.path.join(upload_dir, "new.csv")

            if os.path.exists(file_path):
                os.remove(file_path)

            with open(file_path, "wb") as f:
                f.write(uploaded_file.getbuffer())
            
            df = pd.read_csv(file_path)

            newfile = os.path.join(upload_dir, "half.csv")
            if os.path.exists(newfile):
                os.remove(newfile)

            if len(df) > 1000:
                rows_to_keep = len(df) // 12
            elif len(df) > 500 or len(df)<1000:
                rows_to_keep = len(df) // 8
            elif len(df) < 500 or len(df) > 100:
                rows_to_keep = len(df) // 5 
            else:
                rows_to_keep = len(df) // 2

            df_half = df.iloc[:rows_to_keep]
            df_half.to_csv(newfile, index=False)

            st.subheader("CSV Data")
            st.write(df)
        
    if uploaded_file is not None:
        st.divider()
        with st.container():
            st.subheader("Get Ai Suggestion")
            left_column, right_column = st.columns([0.4,1])
            with left_column:
                st.write("This will take few seconds :->")
            with right_column:
                agree23 = st.button('Get Suggestion',key="check23")
            
            if agree23:
                try:
                    api_key1 = os.getenv("OPENAI_API_KEY")
                    genai.configure(api_key=os.environ["OPENAI_API_KEY"])
                    
                    def upload_to_gemini(path, mime_type=None):
                        return genai.upload_file(path, mime_type=mime_type)

                    def wait_for_files_active(files):
                        for file in files:
                            while file.state.name == "PROCESSING":
                                time.sleep(10)
                                file = genai.get_file(file.name)
                            if file.state.name != "ACTIVE":
                                raise Exception(f"File {file.name} failed to process")

                    # Generation configuration
                    generation_config = {
                        "temperature": 1,
                        "top_p": 0.95,
                        "top_k": 64,
                        "max_output_tokens": 5000,
                        "response_mime_type": "text/plain",
                    }

                    model = genai.GenerativeModel(
                        model_name="gemini-1.5-flash",
                        generation_config=generation_config,
                    )
                    if on0:
                        files = [upload_to_gemini(uploaded_file, mime_type="text/csv")]
                        wait_for_files_active(files)
                    elif onn1:
                        files = [upload_to_gemini(uploaded_file, mime_type="text/csv")]
                        wait_for_files_active(files)
                    else:
                        csv_path = "./uploads/half.csv"
                        if os.path.exists(file_path):
                            files = [upload_to_gemini(csv_path, mime_type="text/csv")]
                            wait_for_files_active(files)

                    chat_session = model.start_chat(
                        history=[
                            {
                                "role": "user",
                                "parts": [files[0]],
                            },
                        ]
                    )
                    response = chat_session.send_message("Summarize the dataset and suggest which among these machine learning model to use.(LinearRegression,LogisticRegression,DecisionTreeClassifier,DecisionTreeRegressor,RandomForestClassifier,RandomForestRegressor,SVC,Gaussian Naive Bayes)")
                    with st.chat_message("assistant"):
                        st.write(response.text)
                except Exception as e:
                    st.error("Oops, something went wrong here")
        
        st.divider()

        with st.container():
           
            st.subheader("Visualise Data")
            left_column, right_column = st.columns([0.4,1])
            with left_column:
                st.write("This will take few seconds :->")
            with right_column:
                agree22 = st.button('Visualise',key="check22")
            
            if agree22:
                try:
                    report_file = 'report.html'
                    
                    if os.path.exists(report_file):
                        os.remove(report_file)

                    report = sv.analyze(df)
                    report.show_html(report_file, layout='vertical', open_browser=True)

                    st.write("Exploratory Data Analysis with Sweetviz")
                    with open(report_file, 'r', encoding='utf-8') as HtmlFile:
                        source_code = HtmlFile.read()
                        st.components.v1.html(source_code, height=1000, scrolling=True)
                except:
                    st.error("Oops Something went wrong here")

        st.divider()

        with st.container():
            left_column, right_column = st.columns(2)
            with left_column:
                st.subheader("Data Description:")
                st.write(df.describe())
                
            with right_column:
                st.subheader("Null Values:")
                null_values = df.isnull().sum()
                st.write(null_values)


        st.divider()
        with st.container():
            st.subheader("Handle Null Values if you have")
            on = st.toggle('Activate feature',key="check1")
            if on:
                left_column, right_column,col3 = st.columns(3)
                with left_column:
                    
                    genre = st.selectbox(
                        "Select Options To Handle Null Values",
                        ["None","dropna","fillna","ffill","bfill"]
                    )
                    if genre == "None":
                        None
                    if genre == "dropna":
                        df = df.dropna()
                        st.write(df)
                    if genre == "fillna":
                        df = df.fillna(df.mean())  
                        st.write(df)
                    if genre == "ffill":
                        df = df.ffill()
                        st.write(df)
                    if genre ==  "bfill":
                        df = df.bfill()
                        st.write(df)

                with right_column:
                    st.subheader("Null Values Now:")
                    null_values = df.isnull().sum()
                    st.write(null_values)
                with col3:
                    st.subheader("Present Columns:")
                    st.dataframe(df.columns)

            st.divider()

        tenext = False
        with st.container():
            st.subheader("Turn This On If You Have Fully Text Dataset")
            st.write("To Create a Simple Model choose only 1 Row")
            te = st.toggle('Activate feature',key="te1")
            if te:
                tenext = True
                def clean_text(series):
                    return series.str.lower().str.replace('[^\w\s]', '', regex=True)

                with st.container():
                    left_column, right_column,col3 = st.columns(3)
                    
                    with left_column:
                        st.subheader("Training Column 1 E.g. Questions:")
                        training_col1 = list(df.columns)
                        options2 = st.multiselect(
                            'Select training column (only one)',
                            training_col1,
                            key="tefirst"
                        )

                        if options2:
                            st.write("Training Columns:")
                            questions = df[options2]
                            # Clean the questions
                            cleaned_questions = clean_text(questions.astype(str).agg(' '.join, axis=1)).values.flatten()
                            st.write(cleaned_questions)

                    with right_column:
                        st.subheader("Training Column 2 E.g. Answers")
                        tel1 = [col for col in training_col1 if col not in options2]
                        
                        options = st.multiselect(
                            'Select training column (only one)',
                            tel1,
                            key="te2"
                        )
                        
                        if options:
                            st.write("Test Column:")
                            answers = df[options]
                            # Clean the answers
                            cleaned_answers = clean_text(answers.astype(str).agg(' '.join, axis=1)).values.flatten()
                            answer = cleaned_answers  # Already flattened
                            st.write(answer)
                    with col3:
                        st.subheader("Enter n_estimators")
                        
                        n_estimators = st.number_input(f"Select Value (default: 50)",value=50,min_value=10, max_value=500)

                        st.subheader("Random State")
                        randomstate = st.number_input(f"Select Value (default: 10)",value=10,min_value=1, max_value=50)

                    st.divider()

                    with st.container():
                        st.subheader("Select an Algorithm(Training Can Take Time!!)")
                        try:
                            options1 = st.selectbox(
                                'Select From DropDown',
                                ('None', 'DecisionTreeClassifier','RandomForestClassifier','GradientBoostingClassifier')
                            )

                            st.write('You selected:', options1)
                            st.divider()

                            # Initialize variables
                            model = None
                            type2 = None
                            jo = False
                            max_depth = 3

                            if options1 == 'None':
                                pass
                            elif options1 == "DecisionTreeClassifier":
                                model = DecisionTreeClassifier(max_depth=5,random_state=randomstate)
                                type2 = 'classifier'
                                jo = True
                            elif options1 == "RandomForestClassifier":
                                model = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, random_state=randomstate)
                                type2 = 'classifier'
                                jo = True
                            elif options1 == "GradientBoostingClassifier":
                                model = GradientBoostingClassifier(n_estimators=n_estimators, learning_rate=0.1, max_depth=max_depth, random_state=randomstate)
                                type2 = 'gbc'
                            Pipe = Pipeline([
                                ('bow', CountVectorizer()),
                                ('tfidf', TfidfTransformer()),
                                (type2, model)
                            ])
                            J= Pipe.fit(cleaned_questions, answer)
                        except:
                            pass
                        
                        
                        if jo == True:
                            col1, col2 = st.columns(2)
                            col1.metric(label="Model trained successfully.", value=options1)
                            col2.metric(label="Model score:", value=J.score(cleaned_questions, answer), delta=None)
                            try:
                                st.caption("")
                                st.divider()
                                st.subheader("Predict")
                                with st.container():
                                    left_column1, right_column1 = st.columns([3,1])
                                    with left_column1:
                                        gg = st.text_input("Enter Values Here",placeholder="Enter")
                                    with right_column1:
                                        bu1 = st.button("Predict")
                                    if bu1:
                                        prediction = Pipe.predict([gg])[0]
                                        with st.chat_message("assistant"):
                                            st.write(f"Model Predicted: {prediction}")
                            except Exception as e:
                                st.error("Model Not Selected Properly")

                        st.divider() 

                        with st.container():
                            left_column, right_column = st.columns([1,1])
                            try:
                                with left_column:
                                    st.subheader("Download Trained Model")
                                with right_column:
                                    if st.button("Download"):
                                        pass 
                            except:
                                pass
        st.divider()

                
        if tenext == False:

            with st.container():
                st.subheader("Handle Labelled Data")
                on1 = st.toggle('Activate feature',key="check2")
                if on1:
                    left_column, right_column = st.columns(2)
                    with left_column:
                        lab = list(df.columns)
                        options1 = st.multiselect(
                            'Select only labelled columns',
                            lab,
                            key = "third"
                        )
                        if options1:
                            le = preprocessing.LabelEncoder()
                            df[options1] = df[options1].apply(le.fit_transform)
                    with right_column:
                        st.write(df)
            st.divider()
            #split train test
            with st.container():
                left_column, right_column,col3 = st.columns(3)
                with left_column:
                    st.subheader("Training Column Names")
                    # Use df.columns directly as options for the multiselect widget
                    training_col = list(df.columns)
                    options2 = st.multiselect(
                        'Select training columns',
                        training_col,
                        key = "first"
                    )

                    # Use the selected options directly as column names
                    if options2:
                        st.write("Training Columns:")
                        # Display DataFrame with the selected columns
                        x = df[options2]
                        st.write(x)

                with right_column:
                    tel = []
                    st.subheader("Test Column Name")
                    for i in training_col :
                        if i not in options2:
                            tel.append(i)
                    options = st.multiselect(
                        'Select training columns',
                        tel,
                        key="second"
                    )
                    if options:
                        st.write("Test Column:")
                        l = df[options]
                        y = np.ravel(l)
                        st.write(y)

                with col3:
                    st.subheader("Enter Weight")
                    
                    f = st.number_input(f"Select Value (default: 0.25)",value=0.25,min_value=0.1, max_value=0.9)

                    st.subheader("Random State")
                    g = st.number_input(f"Select Value (default: 3)",value=3,min_value=1, max_value=10)

                st.divider()
                st.subheader("Select an Algorithm")
                option = st.selectbox(
                    'Select From DropDown',
                    ('None','LinearRegression', 'LogisticRegression', 'DecisionTreeClassifier','DecisionTreeRegressor','RandomForestClassifier','RandomForestRegressor','SVC','Gaussian Naive Bayes'))

                st.write('You selected:', option)

                st.divider()

                with st.container():
                    jo = False
                    try:
                        if f>0.1:
                            st.subheader("Model")

                            X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=f,random_state=g)

                            if option == 'None':
                                pass
                            elif option == "DecisionTreeClassifier":
                                model = tree.DecisionTreeClassifier(max_depth = 5)
                                jo = True
                            elif option == "DecisionTreeRegressor":
                                model = tree.DecisionTreeRegressor(max_depth = 5)
                                jo = True
                            elif option == "RandomForestClassifier":
                                model = RandomForestClassifier(max_depth = 5)
                                jo = True
                            elif option == "RandomForestRegressor":
                                model = RandomForestRegressor(max_depth = 5)
                                jo = True
                            elif option == 'SVC':
                                model = svm.SVC()
                                jo = True
                            elif option == "Gaussian Naive Bayes":
                                model = GaussianNB()
                                jo = True
                            else:
                                model = eval(option + '()') #model name assign linear,logistic
                                jo = True

                            m = model.fit(X_train, y_train)
                    except:
                        st.error("Something went wrong We think model is not selected properly")

                    if jo == True:
                        try:
                            col1, col2 = st.columns(2)
                            col1.metric(label="Model trained successfully.", value=option)
                            col2.metric(label="Model score:", value=m.score(X_test, y_test), delta = None)
                            style_metric_cards()
                            st.caption("Tip: You can change the Score Using Weight")

                            st.divider()

                            st.subheader("Predict")
                            str1 = ", ".join(options2)  #column name
                            st.write("Enter the following values separated by commas -->" + "  " + str1)
                            with st.container():
                                left_column, right_column = st.columns([3,1])
                                with left_column:
                                    g = st.text_input("Enter Values Here",placeholder="Enter")
                                with right_column:
                                    if st.button("Predict"):
                                        values = list(map(float, g.split(',')))
                                        values_df = pd.DataFrame([values], columns=X_train.columns)
                                        prediction = m.predict(values_df)[0]
                                        st.write(f"Model Predicted: {prediction}")
                                
                            st.divider()
                            with st.container():
                                left_column, right_column = st.columns([1,1])
                                with left_column:
                                    st.subheader("Download Trained Model")
                                with right_column:
                                    if st.button("Download"):
                                        pass

                        except:
                            st.error("Something went wrong Select Parameters Correctly")
                                
                st.divider()


if __name__ == "__main__":
    main()