import joblib
from sklearn.preprocessing import StandardScaler
from flask import Flask,render_template,request, redirect, url_for
import numpy as np
import requests


app=Flask(__name__)

model=joblib.load('crypto.pkl')
scaler_x=joblib.load('scaler_x.pkl')

@app.route('/')
def home():
    print("HOME ROUTE ACCESSED!")  # Debug print
    try:
           response = requests.get('https://api.coingecko.com/api/v3/simple/price?ids=bitcoin&vs_currencies=usd', timeout=5)
           response.raise_for_status()
           data = response.json()
           btc_price = data['bitcoin']['usd']
           print(f"BTC Price: {btc_price}")  # Debug print
    except Exception as e:
           print("Error fetching price:", e)
           btc_price = "Error fetching price"
       
    print("Rendering home.html")  # Debug print
    return render_template('home.html', btc_price=btc_price)

@app.route('/about')
def about():
    return render_template('about.html')
    
@app.route('/canva')
def canva():
    return render.template('canva.html')

@app.route('/start')
def startt():
    return render_template('index.html')
    
@app.route('/predict',methods=['POST','GET'])
def pred():
    Open=float(request.form.get('open'))
    High=float(request.form.get('high'))
    Low=float(request.form.get('low'))
    Close=float(request.form.get('close'))
    Volume=float(request.form.get('volume'))

    user_input=[[Open,High,Low,Close,Volume]]
    scaled_input=scaler_x.transform(user_input)
    prediction=model.predict(scaled_input)
    
    return render_template('index.html',result=prediction[0])
    
if __name__=='__main__':
    app.run(debug=True)