Upload app.py

app.py

@@ -0,0 +1,206 @@
+import onnxruntime as ort
+import pickle
+import joblib
+import numpy as np
+import json
+import os
+import requests
+import pandas as pd
+from flask import Flask,render_template,jsonify,request
+import plotly.graph_objects as go
+from dotenv import load_dotenv
+from ta.momentum import RSIIndicator
+from ta.trend import MACD, EMAIndicator
+from ta.volatility import AverageTrueRange
+import plotly.io as pio
+import ChatBot as cb
+
+load_dotenv()
+api=os.environ['COIN_GECKO']
+head={
+    "x-cg-demo-api-key":api
+}
+params={
+    "vs_currency":"usd",
+    "days":1
+}
+lines=requests.get("https://api.coingecko.com/api/v3/coins/bitcoin/ohlc",params=params,headers=head).json()
+
+def preprocess(lines):
+    cols=["open_time","open","high","low","close"]
+    data=pd.DataFrame(lines,columns=cols)
+    data['open_time']=pd.to_datetime(data['open_time'],unit='ms')
+    data['rsi']=RSIIndicator(close=data["close"],window=14,fillna=0).rsi()
+    macd=MACD(close=data["close"])
+    data["macd"]=macd.macd()
+    data['macd_signal']=macd.macd_signal()
+    data['ema_20']=EMAIndicator(close=data['close'],window=14,fillna=0).ema_indicator()
+    data['atr']=AverageTrueRange(high=data['high'],low=data['low'],close=data['close'],window=14).average_true_range()
+    data['weekday']=data['open_time'].dt.day_of_week
+    data['day_of_year']=data['open_time'].dt.day_of_year
+    data['hour']=data['open_time'].dt.hour
+    data['month_end']=data['open_time'].dt.is_month_end.astype(np.float32)
+    data['month_start']=data['open_time'].dt.is_month_start.astype(np.float32)
+    data['quarter_start']=data['open_time'].dt.is_quarter_start.astype(np.float32)
+    data["quarter_end"]=data['open_time'].dt.is_quarter_end.astype(np.float32)
+    data["year"]=data['open_time'].dt.year
+    data['month']=data['open_time'].dt.month
+    data['is_recent']=data['open_time'].dt.year>2023
+    data['is_recent']=data['is_recent'].astype(np.float64)
+    data=data[data["open_time"].dt.minute==0].reset_index(drop=True)
+    return data
+data=preprocess(lines)
+
+session=ort.InferenceSession("models/lstm_model.onnx")
+
+with open("models/hourly_stat.pkl","rb") as f:
+    hourly_model=pickle.load(f)
+
+with open("models/daily_stat.pkl","rb") as f:
+    daily_model=pickle.load(f)
+
+input_scaler=joblib.load("models/input_scaler.pkl")
+
+output_scaler=joblib.load("models/ouput_scaler.pkl")
+
+with open("Data/portfolio.json","r") as f:
+    portfolio=json.load(f)
+
+with open("Data/news.json","r") as f:
+    news=json.load(f)
+
+def predict_onnx(data):
+    ourdata=data.drop(columns=['open_time'])
+    impfactor=np.linspace(0,1,15).reshape(15,1)
+    scaled=input_scaler.transform(ourdata)
+    X=np.append(scaled,impfactor,axis=1).reshape(1,15,20)
+    input_name = session.get_inputs()[0].name
+    output = session.run(None, {input_name: X.astype(np.float32)})
+    return pd.DataFrame({
+        "ds":data[-15:]['open_time']+pd.Timedelta(hours=1),
+        "x":output_scaler.inverse_transform(output[0][0].reshape(1,15)).reshape(15)
+    })
+def precict_ml(data):
+    prediction=hourly_model.predict(data.rename(columns={"open_time":"ds","close":"previous_close"}).fillna(0))
+    return pd.DataFrame({
+        "ds":data['open_time']+pd.Timedelta(hours=1),
+        "y":prediction['yhat']
+    })
+def plot_chart():
+    temp=pd.DataFrame()
+    batchsize=15
+    for i in range(0,len(data),batchsize):
+        input_data=data[len(data)-i-batchsize:len(data)-i].fillna(0)
+        if len(input_data)<batchsize:
+            prediction=predict_onnx(data[:15].fillna(0))[:-(batchsize-(len(data)-i))]
+            temp=pd.concat([prediction,temp])
+            break
+        prediction=predict_onnx(input_data)
+        temp=pd.concat([prediction,temp])
+    predicted=temp
+    mlprediction=precict_ml(data)
+    fig=go.Figure()
+    fig.add_trace(go.Candlestick(
+         x=data["open_time"],
+         open=data["open"],
+         high=data['high'],
+         low=data['low'],
+         close=data['close'],
+         name="liveprice"
+    ))
+    fig.add_trace(go.Scatter(
+         x=predicted['ds'],
+         y=predicted['x'],
+         name="lstm_prediction"
+    ))
+    fig.add_trace(go.Scatter(
+        x=mlprediction['ds'],
+        y=mlprediction['y'],
+        name="MlPrediction"
+    )
+    )
+    fig.update_layout(
+        xaxis=dict(
+            range=[data['open_time'].iloc[-15],data['open_time'].iloc[-1]],
+            rangeslider=dict(visible=False),
+            fixedrange=False
+        ),
+        paper_bgcolor='rgba(113, 78, 161, 0.5)',
+        plot_bgcolor='rgba(113, 78, 161, 0.5)',
+        font_color="white",
+        dragmode="pan"
+    )
+    return fig
+
+def Predict20Days():
+    today=pd.to_datetime("today").normalize()
+    timerange=pd.date_range(start=today,end=today+pd.Timedelta(days=20),freq="d")
+    input_data=pd.DataFrame(timerange,columns=['ds'])
+    pred=daily_model.predict(input_data)
+    return {
+        "ds":timerange,
+        "y":pred['yhat']
+    }
+
+def make_20_days_chart():
+    df=Predict20Days()
+    fig=go.Figure()
+    fig.add_trace(go.Scatter(
+        x=df['ds'],
+        y=df['y']
+    ))
+    fig.update_layout(
+        paper_bgcolor='rgba(113, 78, 161, 0.5)',
+        plot_bgcolor='rgba(113, 78, 161, 0.5)',
+        font_color="white",
+        dragmode="pan"
+    )
+    return fig
+
+app=Flask(__name__)
+app.config['TEMPLATES_AUTO_RELOAD'] = True
+@app.route("/")
+def front():
+    figure=plot_chart()
+    next20days=make_20_days_chart()
+    config={
+        "displayModeBar":False
+    }
+    plot=pio.to_html(figure,full_html=False,config=config)
+    plot2=pio.to_html(next20days,full_html=False,config=config)
+    return render_template("index.html",chart=plot,newsset=news,pred20=plot2)
+
+@app.route("/update_chart")
+def give_update():
+    global lines
+    global data
+    newlines=requests.get("https://api.coingecko.com/api/v3/coins/bitcoin/ohlc",params=params,headers=head).json()
+    lines.append(newlines)
+    data=preprocess(lines)
+    data=data.drop_duplicates()
+    predonnx=predict_onnx(data.iloc[-15:].fillna(0))
+    predml=precict_ml(data.iloc[[-1]])
+    return jsonify({
+        "x":[str(data['open_time'].iloc[-1]),str(predonnx['ds']),str(predml['ds'])],
+        "open":float(data['open'].iloc[-1]),
+        "high":float(data["high"].iloc[-1]),
+        "low":float(data['low'].iloc[-1]),
+        "close":float(data['close'].iloc[-1]),
+        "y":[float(predonnx['x']),float(predml['y'])]
+    })
+
+@app.route("/get_response",methods=['POST'])
+def chat():
+    res=request.get_json()
+    query=res['text']
+    inputinvoke={
+        "userinput":query,
+        "movewhere":"",
+        "aimessages":[],
+        "query":"",
+        "finalanswer":""
+    }
+    response=cb.graph.invoke(inputinvoke)
+    return jsonify(response=response['finalanswer'])
+if __name__=="__main__":
+    app.run()