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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)
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