livesensor_ML/main.py at main · kshitizdass/livesensor_ML · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
from sensor.configuration.mongodb_db_connection import MongoDBClient
from sensor.exception import SensorException
import os , sys
from sensor.logger import logging


from sensor.pipeline.training_pipeline import TrainPipeline
from sensor.utils.main_utils import load_object
from sensor.ml.model.estimator import ModelResolver,TargetValueMapping
from sensor.configuration.mongodb_db_connection import MongoDBClient
from sensor.exception import SensorException
import os,sys
from sensor.logger import logging
from sensor.pipeline import training_pipeline
from sensor.pipeline.training_pipeline import TrainPipeline
import os
from sensor.utils.main_utils import read_yaml_file
from sensor.constant.training_pipeline import SAVED_MODEL_DIR


from  fastapi import FastAPI
from sensor.constant.application import APP_HOST, APP_PORT
from starlette.responses import RedirectResponse
from uvicorn import run as app_run
from fastapi.responses import Response
from sensor.ml.model.estimator import ModelResolver,TargetValueMapping
from sensor.utils.main_utils import load_object
from fastapi.middleware.cors import CORSMiddleware
import os
from fastapi import FastAPI, File, UploadFile, Response
import pandas as pd


app = FastAPI()


origins = ["*"]
#Cross-Origin Resource Sharing (CORS)
app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


@app.get("/",tags=["authentication"])
async def  index():
    return RedirectResponse(url="/docs")


@app.get("/train")
async def train():
    try:

        training_pipeline = TrainPipeline()

        if training_pipeline.is_pipeline_running:
            return Response("Training pipeline is already running.")

        training_pipeline.run_pipeline()
        return Response("Training successfully completed!")
    except Exception as e:
        return Response(f"Error Occurred! {e}")


@app.get("/predict")
async def predict():
    try:

    # get data and from the csv file
    # covert it into dataframe

        df =None

        Model_resolver = ModelResolver(model_dir=SAVED_MODEL_DIR)
        if not Model_resolver.is_model_exists():
            return Response("Model is not available")

        best_model_path = Model_resolver.get_best_model_path()
        model= load_object(file_path=best_model_path)
        y_pred=model.predict(df)
        df['predicted_column'] = y_pred
        df['predicted_column'].replace(TargetValueMapping().reverse_mapping,inplace=True)


        # get the prediction output as you wnat


    except  Exception as e:
        raise  SensorException(e,sys)


def main():
    try:

        training_pipeline = TrainPipeline()
        training_pipeline.run_pipeline()
    except Exception as e:
        print(e)
        logging.exception(e)


if __name__ == "__main__":

    app_run(app ,host=APP_HOST,port=APP_PORT)