moezali1

import plotly.express as px

fig = px.scatter(x=data['tenure'], y=data['TotalCharges'], 
                 color = data['Churn'], template = 'presentation', 
                 opacity = 0.5, facet_col = data['Contract'], 
                 title = 'Customer Churn by Tenure, Charges, and Contract Type',
                 labels = {'x' : 'Customer Tenure', 'y' : 'Total Charges $'})
                
fig.show()

moezali1

import requests

def get_predictions(carat_weight, cut, color, clarity, polish, symmetry, report):
    url = 'http://localhost:8000/predict?carat_weight={carat_weight}&cut={cut}&color={color}&clarity={clarity}&polish={polish}&symmetry={symmetry}&report={report}'\
    .format(carat_weight = carat_weight, cut = cut,\
     color = color, clarity = clarity, polish = polish, symmetry = symmetry, report = report)
    
    x = requests.post(url)
    print(x.text)

moezali1

# 1. Library imports
import pandas as pd
from pycaret.regression import load_model, predict_model
from fastapi import FastAPI
import uvicorn

# 2. Create the app object
app = FastAPI()

#. Load trained Pipeline

moezali1

import gradio as gr

model = gr.inputs.Dropdown(list(compare_model_results['Model']), label="Model")
sepal_length = gr.inputs.Slider(minimum=1, maximum=10, default=data['sepal_length'].mean(), label = 'sepal_length')
sepal_width = gr.inputs.Slider(minimum=1, maximum=10, default=data['sepal_width'].mean(), label = 'sepal_width')
petal_length = gr.inputs.Slider(minimum=1, maximum=10, default=data['petal_length'].mean(), label = 'petal_length')
petal_width = gr.inputs.Slider(minimum=1, maximum=10, default=data['petal_width'].mean(), label = 'petal_width')

gr.Interface(predict, [model,sepal_length,sepal_width,petal_length,petal_width], "label", live=True).launch()

moezali1

# creating a predict function to be passed into gradio UI
def predict(model, sepal_length, sepal_width, petal_length, petal_width):
  
    df = pd.DataFrame.from_dict({'sepal_length': [sepal_length], 'sepal_width': [sepal_width], 
                                 'petal_length': [petal_length], 'petal_width': [petal_width]})
    
    model_index = list(compare_model_results['Model']).index(model)
    model = best[model_index]
    pred = predict_model(model, df, raw_score=True)
    return {'Iris-setosa': pred['Score_Iris-setosa'][0].astype('float64'), 

moezali1

from tqdm import tqdm
from pycaret.regression import *

all_ts = data['time_series'].unique()

all_results = []
final_model = {}

for i in tqdm(all_ts):