from flask import Flask, render_template, request, redirect, url_for, send_file
import os
import shutil
import pandas as pd
from werkzeug.utils import secure_filename
from joblib import load, dump
import numpy as np
from sklearn.preprocessing import LabelEncoder
from time import time
from huggingface_hub import hf_hub_download
import pickle
import uuid
from pathlib import Path
import numpy as np 
import pandas as pd 
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from xgboost import XGBRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_squared_error
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PowerTransformer, StandardScaler
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split, cross_val_score, RandomizedSearchCV
import lightgbm as lgb
from catboost import CatBoostRegressor
from sklearn.ensemble import StackingRegressor
import json
import imblearn

app = Flask(__name__)

# Set the secret key for session management
app.secret_key = os.urandom(24)

# Configurations
UPLOAD_FOLDER = "uploads/"
DATA_FOLDER = "data/"
MODEL_FOLDER = "models/"

os.makedirs(MODEL_FOLDER, exist_ok=True)

# Define the model directory and label encoder directory
MODEL_DIR = r'./Model'
LABEL_ENCODER_DIR = r'./Label_encoders'  # Renamed for clarity

# Global file names for outputs; these will be updated per prediction.
# Note: we now include a unique id to avoid overwriting.
PRED_OUTPUT_FILE = None
CLASS_OUTPUT_FILE = None

ALLOWED_EXTENSIONS = {'csv', 'xlsx'}

# Create directories if they do not exist.
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)

app.config['DATA_FOLDER'] = DATA_FOLDER
os.makedirs(app.config['DATA_FOLDER'], exist_ok=True)

os.makedirs("data", exist_ok=True)

app.config['MODEL_FOLDER'] = MODEL_FOLDER
os.makedirs(app.config['MODEL_FOLDER'], exist_ok=True)

# Prediction analysis models loaded from Hugging Face.


#classsification model on the task
# ----------------------------------------------
# Code classification models for real data.
# ----------------------------------------------
#black code change
src_path = hf_hub_download(
    repo_id="WebashalarForML/Diamond_model_",
    filename="CLASS_DUMMY/DT_best__2_class_blk(M)_change.pkl",
    cache_dir=MODEL_FOLDER
)
dst_path = os.path.join(MODEL_FOLDER, "DT_best__2_class_blk(M)_change.pkl")
shutil.copy(src_path, dst_path)
blk_change = load(dst_path)

# white code change
src_path = hf_hub_download(
    repo_id="WebashalarForML/Diamond_model_",
    filename="CLASS_DUMMY/DT_best__2_class_wht(M)_change.pkl",
    cache_dir=MODEL_FOLDER
)
dst_path = os.path.join(MODEL_FOLDER, "DT_best__2_class_wht(M)_change.pkl")
shutil.copy(src_path, dst_path)
wht_change = load(dst_path)

# pav code change
src_path = hf_hub_download(
    repo_id="WebashalarForML/Diamond_model_",
    filename="CLASS_DUMMY/DT_best__2_class_pav(M)_change.pkl",
    cache_dir=MODEL_FOLDER
)
dst_path = os.path.join(MODEL_FOLDER, "DT_best__2_class_pav(M)_change.pkl")
shutil.copy(src_path, dst_path)
pav_change = load(dst_path)

#open code change
src_path = hf_hub_download(
    repo_id="WebashalarForML/Diamond_model_",
    filename="CLASS_DUMMY/DT_best__2_class_open(M)_change.pkl",
    cache_dir=MODEL_FOLDER
)
dst_path = os.path.join(MODEL_FOLDER, "DT_best__2_class_open(M)_change.pkl")
shutil.copy(src_path, dst_path)
open_change = load(dst_path)

# ----------------------------------------------
# parameter classification models for real data.
# ----------------------------------------------

#shape change
src_path = hf_hub_download(
    repo_id="WebashalarForML/Diamond_model_",
    filename="CLASS_DUMMY/DT_best__2_class_shp_change.pkl",
    cache_dir=MODEL_FOLDER
)
dst_path = os.path.join(MODEL_FOLDER, "DT_best__2_class_shp_change.pkl")
shutil.copy(src_path, dst_path)
shape_change = load(dst_path)

# color change
src_path = hf_hub_download(
    repo_id="WebashalarForML/Diamond_model_",
    filename="CLASS_DUMMY/DT_best__2_class_col_change.pkl",
    cache_dir=MODEL_FOLDER
)
dst_path = os.path.join(MODEL_FOLDER, "DT_best__2_class_col_change.pkl")
shutil.copy(src_path, dst_path)
col_change = load(dst_path)

# quality change
src_path = hf_hub_download(
    repo_id="WebashalarForML/Diamond_model_",
    filename="CLASS_DUMMY/DT_best__2_class_qua_change.pkl",
    cache_dir=MODEL_FOLDER
)
dst_path = os.path.join(MODEL_FOLDER, "DT_best__2_class_qua_change.pkl")
shutil.copy(src_path, dst_path)
qua_change = load(dst_path)

# cut change
src_path = hf_hub_download(
    repo_id="WebashalarForML/Diamond_model_",
    filename="CLASS_DUMMY/DT_best__2_class_cut_change.pkl",
    cache_dir=MODEL_FOLDER
)
dst_path = os.path.join(MODEL_FOLDER, "DT_best__2_class_cut_change.pkl")
shutil.copy(src_path, dst_path)
cut_change = load(dst_path)

print("================================")

# List of label encoder names.
encoder_list = [
    'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo',
    'EngNts', 'EngMikly','EngBlk', 'EngWht', 'EngOpen','EngPav',
    'Change_cts_value_v2', 'Change_shape_value_v2', 'Change_quality_value_v2', 'Change_color_value_v2',
    'Change_cut_value_v2', 'Change_Blk_Eng_to_Mkbl_value_2', 'Change_Wht_Eng_to_Mkbl_value_2',
    'Change_Open_Eng_to_Mkbl_value_2', 'Change_Pav_Eng_to_Mkbl_value_2', 'Change_Blk_Eng_to_Grd_value_2',
    'Change_Wht_Eng_to_Grd_value_2', 'Change_Open_Eng_to_Grd_value_2', 'Change_Pav_Eng_to_Grd_value_2',
    'Change_Blk_Eng_to_ByGrd_value_2', 'Change_Wht_Eng_to_ByGrd_value_2', 'Change_Open_Eng_to_ByGrd_value_2',
    'Change_Pav_Eng_to_ByGrd_value_2', 'Change_Blk_Eng_to_Gia_value_2', 'Change_Wht_Eng_to_Gia_value_2',
    'Change_Open_Eng_to_Gia_value_2', 'Change_Pav_Eng_to_Gia_value_2'
]

# Load label encoders using pathlib for cleaner path management.
loaded_label_encoder = {}
enc_path = Path(LABEL_ENCODER_DIR)
for val in encoder_list:
    encoder_file = enc_path / f"label_encoder_{val}.joblib"
    loaded_label_encoder[val] = load(encoder_file)

# -----------------------------------------
# Utility: Allowed File Check
# -----------------------------------------
def allowed_file(filename):
    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS

# -----------------------------------------
# Routes
# -----------------------------------------
@app.route('/')
def index():
    return render_template('index.html')

@app.route('/predict', methods=['POST'])
def predict():
    if 'file' not in request.files:
        print('No file part', 'error')
        return redirect(url_for('index'))
    
    file = request.files['file']
    if file.filename == '':
        print('No selected file', 'error')
        return redirect(url_for('index'))
    
    if file and allowed_file(file.filename):
        filename = secure_filename(file.filename)
        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
        file.save(filepath)
        
        # Convert file to DataFrame
        try:
            if filename.endswith('.csv'):
                df = pd.read_csv(filepath)
            else:
                df = pd.read_excel(filepath)
        except Exception as e:
            print(f'Error reading file: {e}', 'error')
            return redirect(url_for('index'))
        
        # Process the DataFrame and generate predictions and classification analysis.
        df_pred, dx_class = process_dataframe(df)
        if df_pred.empty:
            print("Processed prediction DataFrame is empty. Check the input file and processing logic.", "error")
            return redirect(url_for('index'))
        
        # Save output files with a timestamp and unique id.
        current_date = pd.Timestamp.now().strftime("%Y-%m-%d")
        unique_id = uuid.uuid4().hex[:8]
        global PRED_OUTPUT_FILE, CLASS_OUTPUT_FILE
        PRED_OUTPUT_FILE = f'data/prediction_output_{current_date}_{unique_id}.csv'
        CLASS_OUTPUT_FILE = f'data/classification_output_{current_date}_{unique_id}.csv'
        df_pred.to_csv(PRED_OUTPUT_FILE, index=False)
        dx_class.to_csv(CLASS_OUTPUT_FILE, index=False)
        
        # Redirect to report view; default to prediction report, page 1.
        return redirect(url_for('report_view', report_type='pred', page=1))
    else:
        print('Invalid file type. Only CSV and Excel files are allowed.', 'error')
        return redirect(url_for('index'))

def process_dataframe(df):
    try:
        #df = df[df["MkblAmt"].notna()]
        
        # Define the columns needed for two parts.
        required_columns = ['EngCts', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol',
                           'EngSym', 'EngFlo', 'EngNts', 'EngMikly', 'EngBlk', 'EngWht', 'EngOpen',
                           'EngPav', 'EngAmt']
        required_columns_2 = ['EngCts', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol',
                           'EngSym', 'EngFlo', 'EngNts', 'EngMikly', 'EngAmt']
        
        # Create two DataFrames: one for prediction and one for classification.
        df_pred = df[required_columns].copy()
        #df_pred = df_pred[(df_pred[['EngCts']] > 0.99).all(axis=1) & (df_pred[['EngCts']] < 1.50).all(axis=1)] 
        df_pred[['EngBlk', 'EngWht', 'EngOpen', 'EngPav']]=df_pred[['EngBlk', 'EngWht', 'EngOpen', 'EngPav']].fillna("NA")
        df_class = df[required_columns_2].fillna("NA").copy()
        
        # Transform categorical columns for prediction DataFrame using the label encoders.
        for col in ['EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo', 'EngNts', 'EngMikly','EngBlk', 'EngWht', 'EngOpen', 'EngPav']:
            try:
                encoder = loaded_label_encoder[col]
                df_pred[col] = df_pred[col].map(lambda x: encoder.transform([x])[0] if x in encoder.classes_ else -1)
                # df_pred[col] = loaded_label_encoder[col].transform(df_pred[col])
            except ValueError as e:
                print(f'Invalid value in column {col}: {e}', 'error')
                return pd.DataFrame(), pd.DataFrame()
        
        # Update the classification DataFrame with the transformed prediction columns.
        for col in ['EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo', 'EngNts', 'EngMikly']:
            df_class[col] = df_pred[col]
        
        df_pred = df_pred.astype(float)
        df_class = df_class.astype(float)
        
        # ------------------------------------
        # Prediction Report Section
        # ------------------------------------
        try:
        
            # for model BLK CODE
            df_pred_0 = df_pred.copy()
            df_pred_0['Change_Blk_Eng_to_Mkbl_value_2'] = pd.DataFrame(blk_change.predict(df_pred), columns=["Change_Blk_Eng_to_Mkbl_value_2"])
            print(df_pred_0.columns)
            
            # for model WHT CODE
            df_pred_0['Change_Wht_Eng_to_Mkbl_value_2'] = pd.DataFrame(wht_change.predict(df_pred), columns=["Change_Wht_Eng_to_Mkbl_value_2"])
            print(df_pred_0.columns)

            # for model PAV CODE (need change)
            df_pred_0['Change_Pav_Eng_to_Mkbl_value_2'] = pd.DataFrame(pav_change.predict(df_pred), columns=["Change_Pav_Eng_to_Mkbl_value_2"])
            print(df_pred_0.columns)

            # for model OPEN CODE  (need change)
            df_pred_0['Change_Open_Eng_to_Mkbl_value_2'] = pd.DataFrame(open_change.predict(df_pred), columns=["Change_Open_Eng_to_Mkbl_value_2"])
            print(df_pred_0.columns)

            # for model SHP CODE (need change)
            df_pred_0['Change_shape_value_v2'] = pd.DataFrame(shape_change.predict(df_class), columns=["Change_shape_value_v2"])
            print(df_pred_0.columns)

            # for model COL CODE (need change)
            df_pred_0['Change_color_value_v2'] = pd.DataFrame(col_change.predict(df_class), columns=["Change_color_value_v2"])
            print(df_pred_0.columns)

            # for model CUT CODE (need change)
            df_pred_0['Change_cut_value_v2'] = pd.DataFrame(cut_change.predict(df_class), columns=["Change_cut_value_v2"])
            print(df_pred_0.columns)

            # for model QUA CODE (need change)
            df_pred_0['Change_quality_value_v2'] = pd.DataFrame(qua_change.predict(df_class), columns=["Change_quality_value_v2"])
            print(df_pred_0.columns)            
            
            # Concatenate the DataFrames row-wise
            #df_pred_main = pd.concat([df_pred_0, df_pred_1, df_pred_0], ignore_index=True)
            df_pred_main = df_pred_0.copy()
    
            for col in ['EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo',
                        'EngNts', 'EngMikly','EngBlk', 'EngWht', 'EngOpen','EngPav',
                        'Change_shape_value_v2','Change_quality_value_v2', 'Change_color_value_v2', 'Change_cut_value_v2',
                        #'Change_cts_value_v2',
                        'Change_Blk_Eng_to_Mkbl_value_2', 
                        'Change_Wht_Eng_to_Mkbl_value_2',
                        'Change_Open_Eng_to_Mkbl_value_2',
                        'Change_Pav_Eng_to_Mkbl_value_2', 
                        #'Change_Blk_Eng_to_Grd_value_2','Change_Wht_Eng_to_Grd_value_2', 'Change_Open_Eng_to_Grd_value_2', 'Change_Pav_Eng_to_Grd_value_2',
                        #'Change_Blk_Eng_to_ByGrd_value_2', 'Change_Wht_Eng_to_ByGrd_value_2', 'Change_Open_Eng_to_ByGrd_value_2', 'Change_Pav_Eng_to_ByGrd_value_2', 
                        #'Change_Blk_Eng_to_Gia_value_2', 'Change_Wht_Eng_to_Gia_value_2', 'Change_Open_Eng_to_Gia_value_2', 'Change_Pav_Eng_to_Gia_value_2'
                       ]:
                
                try:
                    #def safe_inverse_transform(le: LabelEncoder, codes: np.ndarray):
                    #    known = set(le.classes_)
                    #    return np.array([le.inverse_transform([c])[0] if c in known else "Unknown" for c in codes])
                    #df_pred_main[col] = safe_inverse_transform(loaded_label_encoder[col], df_pred_0[col])    
                    df_pred_main[col] = loaded_label_encoder[col].inverse_transform(df_pred_main[col].astype(int))
                except ValueError as e:
                    print(f'inverse transform fails value in column {col}: {e}', 'error')
                    
        except ValueError as e:
            print(f'pred model error----->: {e}', 'error')

        print("EngBlk", df_pred_main['EngBlk'].unique())
        print("EngWht", df_pred_main['EngWht'].unique())
        print("EngOpen", df_pred_main['EngOpen'].unique())
        print("EngPav", df_pred_main['EngPav'].unique())
           
        # Final return with full data for pagination.
        df_pred_main['EngBlk'] = df_pred_main['EngBlk'].fillna("-")
        df_pred_main['EngWht'] = df_pred_main['EngWht'].fillna("-")
        df_pred_main['EngOpen'] = df_pred_main['EngOpen'].fillna("-")
        df_pred_main['EngPav'] = df_pred_main['EngPav'].fillna("-")
        df_pred_main['EngBlk'] = df_pred_main['EngBlk'].replace("NA", "-", regex=True)
        df_pred_main['EngWht'] = df_pred_main['EngWht'].replace("NA", "-", regex=True)
        df_pred_main['EngOpen'] = df_pred_main['EngOpen'].replace("NA", "-", regex=True)
        df_pred_main['EngPav'] = df_pred_main['EngPav'].replace("NA", "-", regex=True)
        # Final step to replace NaN or empty values with "-"
        df_pred_main = df_pred_main.fillna("-")
        df_pred_main = df_pred_main.replace(r'^\s*$', "-", regex=True)
        return df_pred_main, df_pred_main     
    except Exception as e:
        print(f'Error processing file: {e}', 'error')
        return pd.DataFrame(), pd.DataFrame()

# ----------------------------------------------------
# Report View Route with Pagination & Toggle
# ----------------------------------------------------

@app.route("/report")
def report_view():
    report_type = request.args.get('report_type', 'pred')
    try:
        page = int(request.args.get('page', 1))
    except ValueError:
        page = 1
    per_page = 15

    # load CSV
    if report_type == 'pred':
        df = pd.read_csv(PRED_OUTPUT_FILE)
    else:
        df = pd.read_csv(CLASS_OUTPUT_FILE)

    # page slice
    start = (page - 1) * per_page
    end   = start + per_page
    df_page = df.iloc[start:end].copy()

    # optional: colored arrow in Makable_Predicted
    def add_colored_arrow(row):
        try:
            pred = float(row['Makable_Predicted'])
            diff = float(row['Makable_Diff'])
            arrow = '↑' if diff > 0 else '↓'
            color = 'green' if diff > 0 else 'red'
            return f"{pred:.3f} <span style='color:{color};'>{arrow}</span>"
        except:
            return row.get('Makable_Predicted', '')

    df_page['Makable_Predicted'] = df_page.apply(add_colored_arrow, axis=1)

    # render to HTML (allow our <span> tags)
    table_html = df_page.to_html(
        classes="report-table",
        index=False,
        escape=False
    )

    has_prev = page > 1
    has_next = end < len(df)

    return render_template(
        "output.html",
        report_type=report_type,
        page=page,
        has_prev=has_prev,
        has_next=has_next,
        table_html=table_html
    )

# ------------------------------
# Download Routes
# ------------------------------
@app.route('/download_pred', methods=['GET'])
def download_pred():
    return send_file(PRED_OUTPUT_FILE, as_attachment=True)

@app.route('/download_class', methods=['GET'])
def download_class():
    return send_file(CLASS_OUTPUT_FILE, as_attachment=True)

if __name__ == "__main__":
    app.run(debug=True)