diff --git a/Dockerfile b/Dockerfile
index 7d40e45..543bb11 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,31 +1,20 @@
 FROM ubuntu:latest
+FROM python:3.8
 RUN apt update 
 
 
 # Stwórzmy w kontenerze (jeśli nie istnieje) katalog /app i przejdźmy do niego (wszystkie kolejne polecenia RUN, CMD, ENTRYPOINT, COPY i ADD będą w nim wykonywane)
 COPY ./requirements.txt .
 RUN apt-get update
-RUN apt-get install -y python3-pip
-RUN pip3 install --upgrade pip  
-RUN pip3 install pandas
-RUN pip3 install numpy
-RUN pip install  kaggle
-RUN apt-get install zip unzip
+RUN pip3 install -r requirements.txt
+RUN apt-get install zip unzip --yes
 
-ARG CUTOFF
-ARG KAGGLE_USERNAME
-ARG KAGGLE_KEY
-ENV CUTOFF=${CUTOFF}
-ENV KAGGLE_USERNAME=${KAGGLE_USERNAME}
-ENV KAGGLE_KEY=${KAGGLE_KEY}
-
-# Skopiujmy nasz skrypt do katalogu /app w kontenerze
-RUN mkdir /data
 
 WORKDIR /app
 
 COPY ./process_data.sh .
 COPY ./download_data_and_process.py .
 COPY ./stats.py .
+COPY ./real-or-fake-fake-jobposting-prediction.zip .
 RUN chmod +x process_data.sh
-RUN ./process_data.sh
+CMD python3 download_data_and_process.py
diff --git a/Jenkinsfile b/Jenkinsfile
index 027f3d2..7d09333 100644
--- a/Jenkinsfile
+++ b/Jenkinsfile
@@ -34,10 +34,6 @@ pipeline {
                 withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
                          "KAGGLE_KEY=${params.KAGGLE_KEY}",
                          "CUTOFF=${params.CUTOFF}"]) {
-                            sh './process_data.sh'
-                            sh 'ls'
-                            sh 'wc data_train.csv'
-                            sh 'cat column_titles.csv'
                             sh 'python3 ./download_data_and_process.py'
                             archiveArtifacts artifacts: "data_test.csv, data_dev.csv, data_train.csv, column_titles.csv, data.csv"
                 }
diff --git a/download_data_and_process.py b/download_data_and_process.py
index b4dd4f2..c46f28b 100644
--- a/download_data_and_process.py
+++ b/download_data_and_process.py
@@ -1,6 +1,10 @@
 import subprocess
 import pandas as pd
 import numpy as np
+
+import subprocess
+
+rc = subprocess.call("./process_data.sh")
 # import kaggle
 
 # kaggle.api.authenticate()
@@ -8,9 +12,6 @@ import numpy as np
 
 
 
-
-
-
 data=pd.read_csv('fake_job_postings.csv')
 data = data.replace(np.nan, '', regex=True)
 
diff --git a/process_data.sh b/process_data.sh
index f152fcd..8895f0c 100755
--- a/process_data.sh
+++ b/process_data.sh
@@ -1,14 +1,8 @@
 #!/bin/bash
 echo "Download data from kaggle"
 echo $KAGGLE_USERNAME
-kaggle datasets download -d shivamb/real-or-fake-fake-jobposting-prediction
+# kaggle datasets download -d shivamb/real-or-fake-fake-jobposting-prediction
 unzip -o real-or-fake-fake-jobposting-prediction.zip
-ls
-wc fake_job_postings.csv
-cp fake_job_postings.csv /data/fake_job_postings.csv
-cd data 
-ls 
-wc fake_job_postings.csv
 echo "Save column titles"
 head -n 1 fake_job_postings.csv > column_titles.csv
 tail -n +2 fake_job_postings.csv > data_not_shuf.csv
diff --git a/real-or-fake-fake-jobposting-prediction.zip b/real-or-fake-fake-jobposting-prediction.zip
new file mode 100644
index 0000000..d8b4583
Binary files /dev/null and b/real-or-fake-fake-jobposting-prediction.zip differ
diff --git a/requirements.txt b/requirements.txt
index 84dc8b2..c37d049 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,2 +1,3 @@
-pandas==1.1.5
-numpy==1.22.3 
\ No newline at end of file
+pandas
+numpy 
+kaggle
\ No newline at end of file