import pandas as pd import subprocess subprocess.call(["pip", "install", "scikit-learn"]) subprocess.call(["python", "-m", "pip" ,"install", "--upgrade pip"]) from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression import numpy as np import gradio as gr #ooooo df=pd.read_csv('GOOG.csv') y=df['Close'] p=df.drop('Close',axis=1) q=p.drop('Date',axis=1) gw=q.drop('High',axis=1) g=q.drop('Adj Close',axis=1) x=g x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=100) lr=LinearRegression() lr.fit(x_train,y_train) y_lr_train_pred=lr.predict(x_train) y_lr_test_pred=lr.predict(x_test) def pre(features): a = np.array(list(map(float, features.split(",")))) a_reshaped = a.reshape(1, -1) prediction = lr.predict(a_reshaped) return prediction[0] iface = gr.Interface(fn=pre, inputs="text", outputs="text") iface.launch()