Content-Type space change

Attendence/Dockerfile

@@ -0,0 +1,18 @@
+# Use an official Python runtime as a parent image
+FROM python:3.8.10
+
+# Set the working directory in the container
+WORKDIR /opt/attendance
+
+# Copy the current directory contents into the container at /opt/attendance
+COPY . .
+
+# Install any needed packages specified in requirements.txt
+RUN apt update && apt install -y libgl1-mesa-glx && apt --fix-broken install && \
+    pip install --no-cache-dir -r requirements.txt
+
+# Make port 5003 available to the world outside this container
+EXPOSE 5003
+
+# Run app.py when the container launches
+CMD ["python", "./attendence.py"]

Attendence/attendence.py

@@ -36,9 +36,11 @@ def createEncodings(image):
     # Create encodings for all faces in an image
     known_encodings = face_recognition.face_encodings(image, known_face_locations=face_locations)
     return known_encodings, face_locations
-#@app.route('/registered', methods=["POST","GET"])
-def registered(url_list):
-    input=url_list
+@app.route('/register', methods=["POST","GET"])
+def registered():
+    input=  request.get_json()
+    #input=url_list
+    #print(input)
 
     from pathlib import Path
     Path(People).mkdir(exist_ok=True)
@@ -225,8 +227,9 @@ def registered(url_list):
 
 
 # ********************************   COMPARUISION *********************************************************
-#@app.route('/submit', methods=["POST","GET"])
-def submit(url_list):
+@app.route('/detect', methods=["POST","GET"])
+def submit():
+    input=  request.get_json()
 
     from datetime import datetime
     import pytz
@@ -236,7 +239,7 @@ def submit(url_list):
     India_Date = str(India_Date)
     # India_Time = (datetime_NY.strftime("%I:%M:%S %p"))
     # India_Time = str(India_Time)
-    input=url_list
+    #input=url_list
     import pickle
     import cv2
 
@@ -278,9 +281,7 @@ def submit(url_list):
     else:
         print ("pickle File not exist")
         print(name)
-        return "Please get registered with your Profile Picture",500
-        # return "Face not found in profile (please change your profile)",500
-	   
+        return "Face not found in profile (please change your profile)"
 
     check_faces=People+"/" + y + "/" + y + ".jpg"
     print(check_faces)
@@ -334,7 +335,7 @@ def submit(url_list):
 
     if (len(number_of_faces))>1:
         print("Group Photo")
-        return "Too Many Faces in Profile Picture",500
+        return "Group Photo"
     elif (len(number_of_faces))==1:
         print("Single Photo")
         pass
@@ -508,11 +509,10 @@ def detect():
             try:
                 results = pool.map(submit, url_list)
             except FileNotFoundError:
-                return 'please get registered with your PhotoID',500
+                return 'plese get registered with your PhotoID'
             except IndexError:
                 #return 'unable to recognize face'
-                #return 'failed',500
-                return "Face does not Match with Profile Picture",500
+                return 'failed'
 
         pool.close()
 
@@ -520,7 +520,7 @@ def detect():
 
 
 
-@app.route('/register', methods=["POST"])
+#@app.route('/register', methods=["POST"])
 def register():
     print("hello start..........")
     if __name__ == "__main__":
@@ -539,22 +539,23 @@ def register():
 
 
         # multiprocessing
-        pool_size = multiprocessing.cpu_count() * 2
-        with multiprocessing.Pool(pool_size) as pool:
-            try:
-                results = pool.map(registered, url_list)
-            except IndexError:
-                pass
-                print('face not found')
-            except FileNotFoundError:
-                pass
+        # pool_size = multiprocessing.cpu_count() * 2
+        # with multiprocessing.Pool(pool_size) as pool:
+        #     try:
+        #         results = pool.map(registered, url_list)
+        #     except IndexError:
+        #         pass
+        #         print('face not found')
+        #     except FileNotFoundError:
+        #         pass
                 
             
-                #os.remove(img)
-               # return 'unable to recognize face'
+        #         #os.remove(img)
+        #        # return 'unable to recognize face'
 
-        pool.close()
+        # pool.close()
         #return results[0]
+        result=registered(url_list)
         return 'Successfully saved encoding.........'
 
 

Attendence/requirements.txt

@@ -1,402 +1,17 @@
-absl-py==1.3.0
-aio-pika==8.2.3
-aiofiles==23.1.0
-aiogram==2.25.1
-aiohttp @ file:///tmp/build/80754af9/aiohttp_1646806365504/work
-aiormq==6.4.2
-aiosignal @ file:///tmp/build/80754af9/aiosignal_1637843061372/work
-alabaster @ file:///home/ktietz/src/ci/alabaster_1611921544520/work
-anaconda-client @ file:///tmp/build/80754af9/anaconda-client_1635342557008/work
-anaconda-navigator==2.1.4
-anaconda-project @ file:///tmp/build/80754af9/anaconda-project_1637161053845/work
-anyio @ file:///tmp/build/80754af9/anyio_1644463572971/work/dist
-appdirs==1.4.4
-APScheduler==3.9.1.post1
-argon2-cffi @ file:///opt/conda/conda-bld/argon2-cffi_1645000214183/work
-argon2-cffi-bindings @ file:///tmp/build/80754af9/argon2-cffi-bindings_1644569679365/work
-arrow @ file:///opt/conda/conda-bld/arrow_1649166651673/work
-astroid @ file:///tmp/build/80754af9/astroid_1628063140030/work
-astropy @ file:///opt/conda/conda-bld/astropy_1650891077797/work
-asttokens @ file:///opt/conda/conda-bld/asttokens_1646925590279/work
-astunparse==1.6.3
-async-timeout==4.0.2
-atomicwrites==1.4.0
-attrs @ file:///opt/conda/conda-bld/attrs_1642510447205/work
-Automat @ file:///tmp/build/80754af9/automat_1600298431173/work
-autopep8 @ file:///opt/conda/conda-bld/autopep8_1639166893812/work
-Babel @ file:///tmp/build/80754af9/babel_1620871417480/work
-backcall @ file:///home/ktietz/src/ci/backcall_1611930011877/work
-backports.functools-lru-cache @ file:///tmp/build/80754af9/backports.functools_lru_cache_1618170165463/work
-backports.tempfile @ file:///home/linux1/recipes/ci/backports.tempfile_1610991236607/work
-backports.weakref==1.0.post1
-bcrypt @ file:///tmp/build/80754af9/bcrypt_1607022650461/work
-beautifulsoup4 @ file:///opt/conda/conda-bld/beautifulsoup4_1650462163268/work
-bidict==0.22.1
-binaryornot @ file:///tmp/build/80754af9/binaryornot_1617751525010/work
-bitarray @ file:///tmp/build/80754af9/bitarray_1648739490228/work
-bkcharts==0.2
-black==19.10b0
-bleach @ file:///opt/conda/conda-bld/bleach_1641577558959/work
-bokeh @ file:///tmp/build/80754af9/bokeh_1638362822154/work
-boto3 @ file:///opt/conda/conda-bld/boto3_1649078879353/work
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-Bottleneck @ file:///tmp/build/80754af9/bottleneck_1648028898966/work
-brotlipy==0.7.0
-CacheControl==0.12.11
-cachetools @ file:///tmp/build/80754af9/cachetools_1619597386817/work
-certifi==2021.10.8
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-chardet @ file:///tmp/build/80754af9/chardet_1607706775000/work
-charset-normalizer @ file:///tmp/build/80754af9/charset-normalizer_1630003229654/work
-click @ file:///tmp/build/80754af9/click_1646056590078/work
-cloudpickle @ file:///tmp/build/80754af9/cloudpickle_1632508026186/work
-clyent==1.2.2
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-colorcet @ file:///tmp/build/80754af9/colorcet_1611168489822/work
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-conda-token @ file:///tmp/build/80754af9/conda-token_1620076980546/work
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-cryptography @ file:///tmp/build/80754af9/cryptography_1633520369886/work
-cssselect==1.1.0
-cycler @ file:///tmp/build/80754af9/cycler_1637851556182/work
-Cython @ file:///tmp/build/80754af9/cython_1647850345254/work
-cytoolz==0.11.0
-daal4py==2021.5.0
-dask==2022.10.2
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-diff-match-patch @ file:///Users/ktietz/demo/mc3/conda-bld/diff-match-patch_1630511840874/work
-distributed @ file:///opt/conda/conda-bld/distributed_1647271944416/work
-dlib==19.24.0
-dnspython==1.16.0
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-docutils @ file:///tmp/build/80754af9/docutils_1620827980776/work
-entrypoints @ file:///tmp/build/80754af9/entrypoints_1649926439650/work
-et-xmlfile==1.1.0
-executing @ file:///opt/conda/conda-bld/executing_1646925071911/work
+blinker==1.7.0
+click==8.1.7
+colorama==0.4.6
+dlib==19.24.2
 face-recognition==1.3.0
 face-recognition-models==0.3.0
-fastjsonschema @ file:///tmp/build/80754af9/python-fastjsonschema_1620414857593/work/dist
-fbmessenger==6.0.0
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-fire==0.5.0
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-Flask @ file:///home/ktietz/src/ci/flask_1611932660458/work
-flatbuffers==23.3.3
-fonttools==4.25.0
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-fsspec @ file:///opt/conda/conda-bld/fsspec_1647268051896/work
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-gast==0.4.0
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-glob2 @ file:///home/linux1/recipes/ci/glob2_1610991677669/work
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-google-api-core @ file:///C:/ci/google-api-core-split_1613980333946/work
-google-auth @ file:///tmp/build/80754af9/google-auth_1626320605116/work
-google-auth-oauthlib==0.4.6
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-google-cloud-storage @ file:///tmp/build/80754af9/google-cloud-storage_1601307969662/work
-google-crc32c @ file:///tmp/build/80754af9/google-crc32c_1612242928148/work
-google-pasta==0.2.0
-google-resumable-media @ file:///tmp/build/80754af9/google-resumable-media_1624367812531/work
-googleapis-common-protos @ file:///tmp/build/80754af9/googleapis-common-protos-feedstock_1617957652138/work
-greenlet @ file:///tmp/build/80754af9/greenlet_1628888132713/work
-grpcio @ file:///tmp/build/80754af9/grpcio_1637590821884/work
-h5py @ file:///tmp/build/80754af9/h5py_1637138488546/work
-HeapDict @ file:///Users/ktietz/demo/mc3/conda-bld/heapdict_1630598515714/work
-holoviews @ file:///opt/conda/conda-bld/holoviews_1645454331194/work
-httptools==0.5.0
-humanfriendly==10.0
-hvplot @ file:///tmp/build/80754af9/hvplot_1627305124151/work
-hyperlink @ file:///tmp/build/80754af9/hyperlink_1610130746837/work
-idna @ file:///tmp/build/80754af9/idna_1637925883363/work
-imagecodecs @ file:///tmp/build/80754af9/imagecodecs_1635529108216/work
-imageio @ file:///tmp/build/80754af9/imageio_1617700267927/work
-imagesize @ file:///tmp/build/80754af9/imagesize_1637939814114/work
-importlib-metadata @ file:///tmp/build/80754af9/importlib-metadata_1648544546694/work
-incremental @ file:///tmp/build/80754af9/incremental_1636629750599/work
-inflection==0.5.1
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-intake @ file:///opt/conda/conda-bld/intake_1647436631684/work
-intervaltree @ file:///Users/ktietz/demo/mc3/conda-bld/intervaltree_1630511889664/work
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-ipython @ file:///tmp/build/80754af9/ipython_1648817057602/work
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-ipywidgets @ file:///tmp/build/80754af9/ipywidgets_1634143127070/work
-isort @ file:///tmp/build/80754af9/isort_1628603791788/work
-itemadapter @ file:///tmp/build/80754af9/itemadapter_1626442940632/work
-itemloaders @ file:///opt/conda/conda-bld/itemloaders_1646805235997/work
-itsdangerous @ file:///tmp/build/80754af9/itsdangerous_1621432558163/work
-jdcal @ file:///Users/ktietz/demo/mc3/conda-bld/jdcal_1630584345063/work
-jedi @ file:///tmp/build/80754af9/jedi_1644297102865/work
-jeepney @ file:///tmp/build/80754af9/jeepney_1627537048313/work
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-joblib @ file:///tmp/build/80754af9/joblib_1635411271373/work
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-jsonschema @ file:///tmp/build/80754af9/jsonschema_1650025953207/work
-jupyter @ file:///tmp/build/80754af9/jupyter_1607700846274/work
-jupyter-client @ file:///tmp/build/80754af9/jupyter_client_1616770841739/work
-jupyter-console @ file:///tmp/build/80754af9/jupyter_console_1616615302928/work
-jupyter-core @ file:///tmp/build/80754af9/jupyter_core_1646976314572/work
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-jupyterlab @ file:///opt/conda/conda-bld/jupyterlab_1647445413472/work
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-keras==2.11.0
-keyring @ file:///tmp/build/80754af9/keyring_1638531355686/work
-kiwisolver @ file:///opt/conda/conda-bld/kiwisolver_1638569886207/work
-lazy-object-proxy @ file:///tmp/build/80754af9/lazy-object-proxy_1616529027849/work
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-libclang==16.0.0
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-locket @ file:///tmp/build/80754af9/locket_1647006009810/work
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-qtconsole @ file:///opt/conda/conda-bld/qtconsole_1649078897110/work
-QtPy @ file:///opt/conda/conda-bld/qtpy_1649073884068/work
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-requests @ file:///opt/conda/conda-bld/requests_1641824580448/work
-requests-file @ file:///Users/ktietz/demo/mc3/conda-bld/requests-file_1629455781986/work
-requests-oauthlib==1.3.1
-requests-toolbelt==0.10.1
-rocketchat-API==1.28.1
-rope @ file:///opt/conda/conda-bld/rope_1643788605236/work
-rsa @ file:///tmp/build/80754af9/rsa_1614366226499/work
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-s3transfer @ file:///tmp/build/80754af9/s3transfer_1626435152308/work
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-Sanic-Cors==2.0.1
-sanic-jwt==1.8.0
-sanic-routing==0.7.2
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-scikit-learn @ file:///tmp/build/80754af9/scikit-learn_1642617106979/work
-scikit-learn-intelex==2021.20220215.212715
-scipy @ file:///tmp/build/80754af9/scipy_1641555004408/work
-Scrapy @ file:///tmp/build/80754af9/scrapy_1646837771788/work
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-SecretStorage @ file:///tmp/build/80754af9/secretstorage_1614022780358/work
-Send2Trash @ file:///tmp/build/80754af9/send2trash_1632406701022/work
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-yapf @ file:///tmp/build/80754af9/yapf_1615749224965/work
-yarl @ file:///tmp/build/80754af9/yarl_1606939947528/work
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-zipp @ file:///opt/conda/conda-bld/zipp_1641824620731/work
-zope.interface @ file:///tmp/build/80754af9/zope.interface_1625036153595/work
+Flask==3.0.0
+importlib-metadata==7.0.1
+itsdangerous==2.1.2
+Jinja2==3.1.2
+MarkupSafe==2.1.3
+numpy==1.24.4
+opencv-python==4.9.0.80
+pillow==10.2.0
+pytz==2023.3.post1
+Werkzeug==3.0.1
+zipp==3.17.0

Business_cards/Dockerfile

@@ -0,0 +1,18 @@
+# Use an official Python runtime as a parent image
+FROM python:3.8.10
+
+# Set the working directory in the container
+WORKDIR /opt/bussinesscards
+
+# Copy the current directory contents into the container at /usr/src/app
+COPY . .
+
+# Install any needed packages specified in requirements.txt
+RUN apt update && apt install -y libgl1-mesa-glx && apt --fix-broken install && \
+    pip install --no-cache-dir -r requirement.txt
+
+# Make port 80 available to the world outside this container
+EXPOSE 1112
+
+# Run app.py when the container launches
+CMD ["python", "./Business_cards.py"]

Demand_forcasting/Dockerfile

@@ -0,0 +1,17 @@
+# Use an official Python runtime as a parent image
+FROM python:3.8.10
+
+# Set the working directory in the container
+WORKDIR /opt/forcasting
+
+# Copy the current directory contents into the container at /usr/src/app
+COPY . .
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Make port 80 available to the world outside this container
+EXPOSE 8082
+
+# Run app.py when the container launches
+CMD ["python", "./forcasting.py"]

Demand_forcasting/data.csv

@@ -0,0 +1,101 @@
+journaldate,sum,itemid,itemname
+31-12-2021,1.0,101000000023776,175/70R14  Assurancer TripleMax 84H TL
+31-12-2021,3.0,101000000024158,205/55R16  Assurance TripleMax 91V TL
+31-12-2021,624.0,101000000015442,"MOBIL GLYGOYLE 680, 208 LT DRUM"
+31-12-2021,35.0,101000000037808,MOBILUBE GX 80W-90 PAIL 7L:IN
+31-12-2021,37.5,101000000047447,MOBIL DEL MX PLUS 15W-40 PL7.5L:IN-DP 2
+31-12-2021,7.0,101000000055448,205/55R17 91H ASSU TRIPLEMAX2 TL
+31-12-2020,224.0,101000000051526,MOBIL SUPER 1000 5W-30 CTN 4X3.5L:IN-CP
+31-12-2020,2562.0,101000000052959,MOBIL SUPER 3000 FI 5W-40 CTN 4X3.5L:IN-CP
+31-12-2020,7.0,101000000052695,MOBIL SUPER 1000 5W-30 PAIL 7L:IN-CP
+31-12-2020,1908.0,101000000051952,MOBIL SUPER 1000 5W-30 CTN 6X3L:IN-CP
+31-12-2020,80.0,101000000052367,M-SUP 1000 DIESEL 15W-40 CTN 4X5L:IN-CP
+31-12-2020,14.0,101000000052877,MOBIL SUPER XHP 10W-40 CTN 4X3.5L:IN-CP
+31-12-2020,20.0,101000000052769,MOBIL SUPER 1000 5W-30 CTN 4X5L:IN-CP
+31-12-2020,36.0,101000000053493,Mobil SUPER 3000 X1 FOR FE 5W30 CTN 6X3L:IN-CP
+31-12-2020,5.0,101000000023164,185 / 65R15 ASSURANCE TRIPLEMAX 88H TL
+31-12-2020,14.0,101000000008239,MOBIL SUPER MGDO 5W-40 CTN 4X3.5L:IN
+31-12-2020,36.0,101000000018815,MOBIL DELVAC TM G CH-4 15W-40 CTN 6X3L:IN
+31-12-2020,20.0,101000000019616,MOBIL DELVAC TM G CI-4PLUS 15W-40 CTN 20X1L:IN
+31-12-2020,11.0,101000000021875,145 / 80R12 DUCARO HI-MILER 74T TL
+31-12-2020,696.0,101000000012556,MOBILITH SHC 220 384LB(174KG) DRUM METAL
+31-12-2020,624.0,101000000014368,NUTO H 68 DRUM 208L:IN
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+31-12-2020,60.0,101000000009015,MOBILUBE GX 80W-90 CTN 20X1L:IN
+31-12-2020,4.0,101000000023637,175/65R14 ASSURANCE DURAPLUS 82T/H TL
+31-12-2020,416.0,101000000005360,MOBIL SUPER HP 10W-30 DRUM 208L:IN
+31-12-2020,4.0,101000000024284,185/70R14 88T ASSURANCE TRIPLEMAX
+31-12-2020,16.0,101000000008576,MOBIL SUPER MGDO 5W-40 CTN 4X4L:IN
+31-12-2020,30.0,101000000010000,MOBILUBE GX 80W-90 CTN 6X2.5L:IN
+31-12-2020,208.0,101000000007290,MOBIL SUPER MGDO 5W-40 DRUM 208L:IN
+31-12-2020,36.0,101000000019696,MOBIL DELVAC TM G CI-4PLUS 15W-40 CTN 6X3L:IN
+31-12-2020,94.5,101000000013094,"MOBIL SHC 630,5GA"
+31-12-2020,1040.0,101000000020132,MOBIL DELVAC TM G CI-4PLUS 15W-40 DRUM 208L:IN
+31-12-2020,180.0,101000000019180,MOBIL DELVAC TM G CH-4 15W-40 PAIL 15L:IN
+31-12-2020,16.0,101000000012337,MOBILGREASE XHP 222 PAIL 16KG:SG
+31-12-2020,96.0,101000000004891,MOBIL SUPER HP 10W-30 CTN 4X4L:IN
+31-12-2020,1.0,101000000022771,175 / 65R15 ASSURANCE TRIPLEMAX 84T TL
+31-12-2020,2250.0,101000000017961,MOBIL DELVAC TM G CF-4 15W-40 PAIL 7.5L:IN
+31-12-2020,546.0,101000000006445,MOBIL SUPER MGDO 15W-40 CTN 4X3.5L:IN
+31-12-2020,52.0,101000000024014,185/60R15  Assurancer TripleMax 84H TL
+31-12-2020,20.0,101000000017132,MOBIL DELVAC TM G CF-4 15W-40 CTN 20X1L:IN
+31-12-2020,20.0,101000000010612,MOBILUBE HD 85W-140 CTN 20X1L:IN
+31-12-2020,82.0,101000000003319,MOBIL SUPER 3000 FORMULA I 5W-40 CTN 12X1L:IN
+31-12-2020,756.0,101000000017549,MOBIL DELVAC TM G CF-4 15W-40 CTN 6X3L:IN
+31-12-2020,2436.0,101000000046656,"MOBILITH SHC 221, 174KG/383.6LB DR"
+31-12-2020,24.0,101000000035381,MOBIL DELVAC LONG LIFE GREASE CTN 24X0.5KG - cap promo
+31-12-2020,252.0,101000000044266,MOBIL SPECIAL 20W-50 CTN 6X3L:IN-SL_old
+31-12-2020,4.0,101000000031812,195/60R16 89H ASSURANCE TRIPLEMAX
+31-12-2020,15.0,101000000048094,CO-1G Green Coolant 1:3 For all cars
+31-12-2020,24.0,101000000035646,MOBIL DELVAC LL GREASE CTN 12X1KG:IN
+31-12-2020,208.0,101000000028407,MOBIL SUPER 3000 TMGO 5W-30 DRUM 208L:IN
+31-12-2020,100.0,101000000046925,MOBILGEAR EP 460 PAIL 20L:IN
+31-12-2020,120.0,101000000038034,MOBILUBE HD 85W-140 PAIL 12L:IN
+31-12-2020,828.0,101000000046225,MOBIL SUPER MOTO 20W-40 CTN 20X0.9L:IN
+31-12-2020,38.4,101000000034876,MOBIL SUPER MOTO SCOOTER 10W-30 CTN 12X0.8L:IN - cap promo
+31-12-2020,352.0,101000000045383,"MOBIL POLYREX EM, 16KG PAIL"
+31-12-2020,12.0,101000000034710,MOBIL SUPER MOTO 10W-30 CTN 12X1L:IN - cap promo
+31-12-2020,98.0,101000000036159,MOBIL DELVAC LONG LIFE GREASE PAIL 7KG - cap promo
+31-12-2020,130.0,101000000037398,MOBIL DELVAC LONG LIFE GREASE CTN 2X5KG - cap promo
+31-12-2020,2.0,101000000027610,175 / 70R14 DURAPLUS 84H / T TL
+31-12-2020,1.0,101000000027409,195 / 55R16 ASSURANCE TRIPLEMAX 87V / H TL
+31-12-2020,24.0,101000000036366,MOBIL DELVAC LONG LIFE GREASE CTN 6X2KG - cap promo
+31-12-2020,172.5,101000000043516,MOBIL DEL TRACTOR SONA 20W-40 PL 7.5L:IN
+31-12-2020,36.0,101000000035874,MOBIL DELVAC LONG LIFE GREASE CTN 4X3KG - cap promo
+31-12-2020,48.0,101000000033625,MOBIL SUPER MOTO 10W-40 CTN 12X1L:IN - cap promo
+31-12-2020,168.0,101000000046999,ECSTAR F9000 FULLY SYN 0W20 CTN4X3.5L:IN
+31-12-2020,20.0,101000000046554,MOBIL SUPER MOTO 20W-40 CTN 20X1L:IN
+31-12-2020,2.0,101000000026452,205 / 55R16 EAGLE NCT5 91V RHD (4RIB)
+31-12-2020,50.0,101000000042528,MOBIL SUPER MOTO 20W-40 PAIL 50L:IN
+31-12-2020,1.0,101000000046772,165/70R14 81T ASSURANCE TRIPLEMAX
+31-12-2020,345.6,101000000034474,MOBIL SUPER MOTO 10W-30 CTN 12X0.9L:IN - cap promo
+31-12-2020,1.0,101000000048926,LED3 H7 40W Light
+31-12-2020,300.0,101000000043708,MOBIL SUP MOTO SYN TEC15W50 CTN6X2.5L:IN
+31-12-2020,126.0,101000000037723,MOBILUBE GX 80W-90 PAIL 7L:IN
+31-12-2019,2.0,101000000039144,4.00-10 CONTI TUFF OT
+31-12-2019,1372.0,101000000029156,MOBIL SUPER 3000 FORMULA I 5W-40 CTN 4X3.5L:IN
+31-12-2019,3.0,101000000039699,90/90-17 CONTI MOTO TL
+31-12-2019,8.0,101000000038954,90/100-10 CONTI GRIPP TL
+31-12-2019,3.0,101000000040882,2.75-18 CONTI RIB TT
+31-12-2019,28.0,101000000041514,2.75/3.00X18 Metro Conti M/Cy.Tube
+31-12-2019,5.0,101000000040259,2.75-18 CONTI RIB OT
+31-12-2019,97.2,101000000034054,MOBIL SUPER MOTO 20W-40 CTN 12X0.9L:IN - cap promo
+31-12-2019,70.0,101000000041365,3.50-8 NAVIGATOR TT
+31-12-2019,154.0,101000000030504,MOBIL SUPER 3000 X1 FORMULA FE 5W-30 CTN4X3.5L:IN
+31-12-2019,594.0,101000000044022,MOBIL SPECIAL 20W-50 CTN 6X3L:IN-SL_old
+31-12-2019,70.0,101000000041800,3.50X8 Metro Conti Scooter Tube
+31-12-2019,1.0,101000000040053,150/60R-17 CONTI BAZOOKA TL
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+31-12-2019,10.0,101000000045728,215/65R16 98H WRANGLER AT SILENTTR
+31-12-2019,80.0,101000000037327,MOBIL DELVAC LONG LIFE GREASE CTN 2X5KG - cap promo
+31-12-2019,72.0,101000000042279,MOBIL DELVAC SUPER 1400 15W-40 CT6X3L:IN
+31-12-2019,1.0,101000000039857,110/70-17 CONTI GO!  TL
+31-12-2019,25.0,101000000041025,2.75-18 CONTI TOOFANI-2 TT
+31-12-2019,36.0,101000000035603,MOBIL DELVAC LL GREASE CTN 12X1KG:IN
+31-12-2019,36.0,101000000035310,MOBIL DELVAC LONG LIFE GREASE CTN 24X0.5KG - cap promo
+31-12-2019,6.0,101000000041663,2.75/3.00X17 Metro Conti M/Cy.Tube
+31-12-2019,270.0,101000000045114,MOBIL DEL MX PLUS 15W-40 PL 15L:IN - DP
+31-12-2019,60.0,101000000029380,MOBIL DELVAC MX PLUS 15W-40 CTN 20X1L:IN
+31-12-2019,388.8,101000000034330,MOBIL SUPER MOTO 10W-30 CTN 12X0.9L:IN - cap promo
+31-12-2019,36.0,101000000036328,MOBIL DELVAC LONG LIFE GREASE CTN 6X2KG - cap promo
+31-12-2019,5.0,101000000042066,185/55R16 87H ASSURANCE TRIPLEMAX

Demand_forcasting/forcasting.py

@@ -1,7 +1,5 @@
 from flask import Flask, render_template, send_file, request, redirect, Response
 import os
-
-
 import pandas as pd
 import warnings
 import json
@@ -9,10 +7,6 @@ import requests
 from urllib.request import urlopen
 warnings.filterwarnings("ignore")
 
-
-
-
-
 app = Flask(__name__)
 
 
@@ -317,13 +311,14 @@ def month(Num,df):
     
     #df1=pd.read_csv(r'./upload/' + name)
     #df1=df1[df1['obdate']!='01/01/0001']
-    userdata.columns = ['itemname','sum','journaldate','itemid']
+    #userdata.columns = ['itemname','sum','journaldate','itemid']
+    userdata.columns = ['journaldate','sum','itemid','itemname']
     # import pandas as pd
     # userdata = pd.read_csv(r'C:\Users\Bizga\Desktop\forcast\5yearsitems.csv')
     # itemid = userdata[['itemname', 'itemid']]
     #userdata['journaldate'] = pd.to_datetime(userdata['journaldate'])
     userdata["journaldate"] = userdata["journaldate"].astype(str)
-    userdata[["year", "month", "day"]] = userdata["journaldate"].str.split("-", expand = True)
+    userdata[["day","month","year"]] = userdata["journaldate"].str.split("-", expand = True)
     userdata['Month-Year']=userdata['year'].astype(str)+'-'+userdata['month'].astype(str)
     item_unique_name = userdata['itemname'].unique()
     
@@ -495,6 +490,33 @@ def month(Num,df):
         result['LowerLimit']=result['Predict'].mean()-result['Predict'].std()*3
         result["LowerLimit"][result["LowerLimit"] < 0] = 0
         print(result)
+        # frames = [fulldata, result]
+      
+        # final = pd.concat(frames)
+        # print('********************************************************')
+        # final['itemname'] = item
+        # final['itemid'] =item_id 
+        # final.columns = ['Date','Predict','ItemName','ItemId']
+        # final['upper_limit']=final["Predict"]+final['Predict']*0.2
+        # # final['lower_limit']=final['Predict']-final['Predict']*0.2
+        # # print(final)
+        # # final.to_json('forcast.json', orient="records")
+    
+        # with open('forcast.json', 'r') as json_file:
+        #     json_load = json.load(json_file)
+        #     #url = "https://demo.bizgaze.app/apis/v4/bizgaze/integrations/demandforecast/saveforecast/List"
+        #     url='https://qa.bizgaze.app/apis/v4/bizgaze/integrations/demandforecast/saveforecast/List'
+    
+        # payload = json.dumps(json_load)#.replace("]", "").replace("[", "")
+        # print(payload)
+        # headers = {
+        #     #'Authorization': 'stat 263162e61f084d3392f162eb7ec39b2c',#demo
+        #     'Authorization': 'stat 873f2e6f70b3483e983972f96fbf5ea4',#test
+        #     'Content-Type': 'application/json'
+        # }
+        # response = requests.request("POST", url, headers=headers, data=payload)
+        # print("##############################################################")
+        # print(response.text)
         
         filePath='path.csv'
 
@@ -574,6 +596,7 @@ def sales_forcast():
     #print(Dataset)
     import pandas as pd
     df=pd.DataFrame(Dataset)
+    #df=pd.read_csv('ItemWiseQuantity_STC.csv')
     print(df)
     # a = Dataset
     #x = a['wise']
@@ -601,7 +624,7 @@ def sales_forcast():
     # import json
     # a={"status":"success"}
     # payload123 = json.dumps(a)
-    return output
+    return "done"
 
 if __name__ == "__main__":
     app.run(host='0.0.0.0', port=8082)

Demand_forcasting/forcasting2.py

@@ -0,0 +1,561 @@
+from flask import Flask, render_template, send_file, request, redirect, Response
+import os
+import pandas as pd
+import warnings
+import json
+import requests
+from urllib.request import urlopen
+warnings.filterwarnings("ignore")
+
+app = Flask(__name__)
+
+@app.route("/", methods=["GET"])
+def home():
+    return 'forcasting app running'
+
+######################################################################################################################
+list_output=[]
+
+def day(Num,get_url,get_url_token,post_url,post_url_token):
+
+    import requests
+
+    url = get_url
+
+    payload = {}
+    headers = {
+    'Authorization': get_url_token
+    }
+
+    response = requests.request("GET", url, headers=headers, data=payload)
+    #a=response.text
+    # print(response.text)
+
+    import pandas as pd
+    
+    df2 = pd.read_json(response.text, orient ='index')
+    df2=df2.reset_index()
+    df2.columns = ['key','value']
+    #print(df2)
+    a=df2['value'][0]
+    
+    j=json.loads(a)
+    userdata = pd.DataFrame(j)
+    #df1
+    itemid=userdata[['itemname','itemid']]
+    itemid.columns = ['ItemName', 'ItemId']
+    
+    #df1=pd.read_csv(r'./upload/' + name)
+    #df1=df1[df1['obdate']!='01/01/0001']
+    userdata.columns = ['journaldate','sum','itemid','itemname']
+    
+    
+    # import pandas as pd
+    # userdata = pd.read_csv(r'C:\Users\Bizga\Desktop\forcast\5yearsitems.csv')
+    # itemid = userdata[['itemname', 'itemid']]
+    #userdata['journaldate'] = pd.to_datetime(userdata['journaldate'])
+    userdata["journaldate"] = userdata["journaldate"].astype(str)
+    #userdata[["year", "month", "day"]] = userdata["journaldate"].str.split("/", expand = True)
+    userdata[[ "day","month","year", ]] = userdata["journaldate"].str.split("-", expand = True)
+    #userdata['Month-Year']=userdata['year'].astype(str)+'-'+userdata['month'].astype(str)
+    item_unique_name = userdata['itemname'].unique()
+    
+    #df=pd.read_csv("C:\\Users\\Bizgaze\\2021_2022.csv")
+    # Group the DataFrame by the 'item' column
+    grouped = userdata.groupby('itemname')
+    
+    # Print the unique items in the 'item' column
+    #print(grouped.groups.keys())
+    
+    # Iterate over the unique items and print the group data
+    for item, userdata in grouped:
+        print("itemname: ", item)
+    
+        item_id = userdata.iloc[-1]['itemid']
+       
+        print(item_id)
+    
+        userdata= userdata.groupby('journaldate').sum()
+        userdata= userdata.reset_index()
+        #print(userdata)
+        fulldata=userdata[['journaldate','sum']]
+        fulldata.columns = ["Dates","SALES"]
+    #************************************************************************************************************************
+    
+        ## Use Techniques Differencing
+        import pandas as pd
+        from pandas import DataFrame
+
+        
+        # userdata=pd.read_csv(r"C:\Users\Bizgaze\ipynb files\TS forcasting\working\139470.csv")
+        userdata=userdata[['journaldate','sum','itemid']]
+        userdata.columns = ['Date', 'sales','sku']
+        from statsmodels.tsa.stattools import adfuller
+        
+        DATE=[]
+        SALES=[]
+        
+        
+        def adf_test(series,userdata):
+            result=adfuller(series)
+            print('ADF Statistics: {}'.format(result[0]))
+            print('p- value: {}'.format(result[1]))
+            if result[1] <= 0.05:
+                print("strong evidence against the null hypothesis, reject the null hypothesis. Data has no unit root and is stationary")
+            else:
+            #print(userdata)
+                print(stationary_test(userdata))
+                print("weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary ")
+             
+        #*********************************************    stationary or non-stationary **********************************************************   
+        def stationary_test(userdata):
+          data=pd.DataFrame(userdata)
+          for i in range(1,13):
+            print(i)
+            sales_data=DataFrame()
+            data['sales']=data['sales'].shift(i)
+            data.dropna(inplace=True)
+            #print( userdata['sales'])
+            try:
+              X=adf_test(data["sales"],userdata="nothing")
+              if "non-stationary" in str(X):
+                print("non-stationary")
+              else:
+                print("stationary")
+                #print(userdata[["Date","sales"]])
+                #df_sale=pd.DataFrame(userdata)
+                DATE.append(data["Date"])
+                SALES.append(data["sales"])
+                #df4 = pd.concat([data, sales_data], axis=1)
+                return "done"
+                break
+            except ValueError:
+              pass
+        
+        try:
+            adf_test(userdata["sales"],userdata)
+        except ValueError:
+            pass
+        sales=pd.DataFrame(SALES).T
+        dates=pd.DataFrame(DATE).T
+        try:
+            df4 = pd.concat([dates["Date"],sales["sales"]], axis=1)
+            df4=df4.dropna()
+            print(df4)
+        except KeyError:
+            df4=userdata[['Date','sales']]
+            df4=df4.dropna()
+            print(df4)
+            pass
+        
+    #####################################################################################################################  
+        userdata=df4
+        a = userdata.iloc[-1]['Date']
+
+        #userdata['Date'] = pd.to_datetime(userdata['Date'])
+        userdata["Date"] = userdata["Date"].astype(str)
+        print('after testing')
+        print(userdata)
+        userdata[["year", "month", "day"]] = userdata["Date"].str.split("-", expand = True)
+        #userdata[["year", "month"]] = userdata["Month"].str.split("-", expand=True)
+        #userdata = userdata[["year","month",'sum']]
+        userdata["year"] = userdata["year"].astype(int)
+        userdata["month"] = userdata["month"].astype(int)
+        userdata["day"] = userdata["day"].astype(int)
+    #####################################################################################################################
+    
+    
+        list_dates=[]
+        import datetime
+    
+        days=int(Num)+1
+        import pandas as pd
+        base_date=pd.to_datetime(a)
+        for x in range(1,days):
+              dates=(base_date + datetime.timedelta(days=x))
+              dates=str(dates).replace(" 00:00:00","")
+              #print(dates)
+              list_dates.append(dates)
+        fut_date = pd.DataFrame(list_dates)
+        fut_date.columns = ["Dates"]
+        
+        future_dates=pd.DataFrame(list_dates)
+    
+        future_dates.columns=["Dates"]
+        future_dates[["year", "month", "day"]] = future_dates["Dates"].str.split("-", expand=True)
+        future_dates.drop(['Dates'], axis=1, inplace=True)
+        future_dates["year"] = future_dates["year"].astype(int)
+        future_dates["month"] = future_dates["month"].astype(int)
+        future_dates["day"] = future_dates["day"].astype(int)
+        #print(future_dates)
+    
+    
+    
+        ###############################################################################
+        userdata['sales']=userdata["sales"].astype(float)
+        dependent = userdata[['year','month','day']]
+        independent = userdata['sales']
+    
+        import numpy as np
+        import pandas as pd
+    
+        import xgboost
+        from sklearn.model_selection import train_test_split
+        from sklearn.model_selection import GridSearchCV
+        from sklearn.metrics import roc_auc_score
+    
+        import matplotlib.pyplot as plt
+    
+        #model = xgboost.XGBRegressor()
+        from sklearn.ensemble import RandomForestRegressor
+        model = RandomForestRegressor(random_state=1,n_jobs=-1)
+        #model.fit(dependent, independent)
+        model.fit(dependent, independent)
+        #future=pd.read_csv('future_dates.csv')
+    
+    
+        future_prediction = model.predict(future_dates)
+        #print(future_prediction)
+        df=pd.DataFrame(future_prediction)
+        df.columns = ["SALES"]
+        frames = [fut_date, df]
+      
+      
+        result = pd.concat(frames,axis=1)
+        result['itemname'] = item
+        result['itemid'] =item_id 
+        result.columns = ['Date','Predict','ItemName','ItemId']
+        #result['Predict']=result["Predict"].astype(int)
+        result['UpperLimit']=result["Predict"].mean()+result['Predict'].std()*3
+        result['LowerLimit']=result['Predict'].mean()-result['Predict'].std()*3
+        print(result)
+        result.to_json('forcast.json', orient="records")
+    
+    
+        with open('forcast.json', 'r') as json_file:
+            json_load = json.load(json_file)
+            #url = "https://demo.bizgaze.app/apis/v4/bizgaze/integrations/demandforecast/saveforecast/List"
+            url=post_url
+    
+            payload = json.dumps(json_load)#.replace("]", "").replace("[", "")
+            print(payload)
+        #print(payload)
+            headers = {
+            #'Authorization': 'stat 263162e61f084d3392f162eb7ec39b2c',#demo
+            'Authorization': post_url_token,#test
+            'Content-Type': 'application/json'
+            }
+            response = requests.request("POST", url, headers=headers, data=payload)
+        # print("##############################################################")
+            print(response.text)
+
+    
+    return 'done'
+
+
+
+#############################################################################################################################################################
+
+
+def month(Num,get_url,get_url_token,post_url,post_url_token):
+    #url='https://qa.bizgaze.app/apis/v4/bizgaze/integrations/demandforecast/getitemdata'
+    # url= get_url
+    # response = urlopen(url)
+    # data_json = json.loads(response.read())
+    # headers = {
+    #         'Authorization':get_url_token,
+    #         #'Authorization':'stat 873f2e6f70b3483e983972f96fbf5ea4',#qa
+    #         'Content-Type': 'application/json'
+    # }
+    # response = requests.request("GET", url, headers=headers, data=data_json)
+    # #print("##############################################################")
+    # a=response.text
+    # # print(response.text)
+    import requests
+
+    url = get_url
+
+    payload = {}
+    headers = {
+    'Authorization': get_url_token
+    }
+
+    response = requests.request("GET", url, headers=headers, data=payload)
+
+    #print(response.text)
+
+    import pandas as pd
+    
+    df2 = pd.read_json(response.text, orient ='index')
+    df2=df2.reset_index()
+    df2.columns = ['key','value']
+    #print(df2)
+    a=df2['value'][0]
+    
+    j=json.loads(a)
+    userdata = pd.DataFrame(j)
+
+    #filePath='path.csv'
+    # if os.path.exists(filePath):
+    #     print('file exist')
+    #     os.remove('path.csv')
+    # else:
+    #     print("file doesn't exists")
+    #     pass
+    #userdata=df
+    itemid=userdata[['itemname','itemid']]
+    itemid.columns = ['ItemName', 'ItemId']
+    
+    #df1=pd.read_csv(r'./upload/' + name)
+    #df1=df1[df1['obdate']!='01/01/0001']
+    userdata.columns = ['journaldate','sum','itemid','itemname']
+    # import pandas as pd
+    # userdata = pd.read_csv(r'C:\Users\Bizga\Desktop\forcast\5yearsitems.csv')
+    # itemid = userdata[['itemname', 'itemid']]
+    #userdata['journaldate'] = pd.to_datetime(userdata['journaldate'])
+    userdata["journaldate"] = userdata["journaldate"].astype(str)
+    #userdata[["year", "month", "day"]] = userdata["journaldate"].str.split("-", expand = True)
+    userdata[[ "day","month","year", ]] = userdata["journaldate"].str.split("-", expand = True)
+    #userdata[["year", "day", "month"]] = userdata["journaldate"].str.split("/", expand=True)
+    userdata['Month-Year']=userdata['year'].astype(str)+'-'+userdata['month'].astype(str)
+    item_unique_name = userdata['itemname'].unique()
+    
+    #df=pd.read_csv("C:\\Users\\Bizgaze\\2021_2022.csv")
+    # Group the DataFrame by the 'item' column
+    grouped = userdata.groupby('itemname')
+    
+    # Print the unique items in the 'item' column
+    #print(grouped.groups.keys())
+    
+    # Iterate over the unique items and print the group data
+    for item, userdata in grouped:
+        print("itemname: ", item)
+    
+        item_id = userdata.iloc[-1]['itemid']
+       
+        print(item_id)
+    
+        userdata= userdata.groupby('Month-Year').sum()
+        userdata= userdata.reset_index()
+      
+        fulldata=userdata[['Month-Year','sum']]
+        fulldata.columns = ["Dates","SALES"]
+    #************************************************************************************************************************
+    
+        ## Use Techniques Differencing
+        import pandas as pd
+        from pandas import DataFrame
+        
+        # userdata=pd.read_csv(r"C:\Users\Bizgaze\ipynb files\TS forcasting\working\139470.csv")
+        userdata=userdata[['Month-Year','sum','itemid']]
+        userdata.columns = ['Month', 'sales','sku']
+        from statsmodels.tsa.stattools import adfuller
+        
+        DATE=[]
+        SALES=[]
+        
+        
+        def adf_test(series,userdata):
+            result=adfuller(series)
+            print('ADF Statistics: {}'.format(result[0]))
+            print('p- value: {}'.format(result[1]))
+            if result[1] <= 0.05:
+                print("strong evidence against the null hypothesis, reject the null hypothesis. Data has no unit root and is stationary")
+            else:
+            #print(userdata)
+                print(stationary_test(userdata))
+                print("weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary ")
+            
+                
+        #*********************************************    stationary or non-stationary **********************************************************   
+        def stationary_test(userdata):
+          data=pd.DataFrame(userdata)
+          for i in range(1,13):
+            print(i)
+            sales_data=DataFrame()
+            data['sales']=data['sales'].shift(i)
+            data.dropna(inplace=True)
+            #print( userdata['sales'])
+            try:
+              X=adf_test(data["sales"],userdata="nothing")
+              if "non-stationary" in str(X):
+                print("non-stationary")
+              else:
+                print("stationary")
+                #print(userdata[["Month","sales"]])
+                #df_sale=pd.DataFrame(userdata)
+                DATE.append(data["Month"])
+                SALES.append(data["sales"])
+                #df4 = pd.concat([data, sales_data], axis=1)
+                return "done"
+                break
+            except ValueError:
+              pass
+        
+        try:
+            adf_test(userdata["sales"],userdata)
+        except ValueError:
+            pass
+        sales=pd.DataFrame(SALES).T
+        dates=pd.DataFrame(DATE).T
+        try:
+            df4 = pd.concat([dates["Month"],sales["sales"]], axis=1)
+            df4=df4.dropna()
+            print(df4)
+        except KeyError:
+            df4=userdata[['Month','sales']]
+            df4=df4.dropna()
+            print(df4)
+            pass
+        
+    #####################################################################################################################  
+        userdata=df4
+        a = userdata.iloc[-1]['Month']
+        userdata[["year", "month"]] = userdata["Month"].str.split("-", expand=True)
+        #userdata = userdata[["year","month",'sum']]
+        userdata["year"] = userdata["year"].astype(int)
+        userdata["month"] = userdata["month"].astype(int)
+    #####################################################################################################################
+    
+        #a = userdata.iloc[-1]['Month-Year']
+        from datetime import datetime
+        from dateutil.relativedelta import relativedelta
+        import pandas as pd
+        months_value = int(Num)+1
+        base_month = pd.to_datetime(a)
+        list_months = []
+        
+        def months(MD):
+            date_after_month = ((base_month + relativedelta(months=MD)).strftime('%Y-%m'))
+            # print
+            list_months.append(date_after_month)
+        
+        for i in range(1, months_value):
+            months(i)
+        
+        future_dates = pd.DataFrame(list_months)
+        
+        future_dates.columns = ["Dates"]
+    
+        fut_date = pd.DataFrame(list_months)
+        fut_date.columns = ["Dates"]
+        
+        future_dates[["year", "month"]] = future_dates["Dates"].str.split("-", expand=True)
+        future_dates.drop(['Dates'], axis=1, inplace=True)
+        future_dates["year"] = future_dates["year"].astype(int)
+        future_dates["month"] = future_dates["month"].astype(int)
+        
+    
+    
+    
+        ###############################################################################
+        userdata['sales']=userdata["sales"].astype(float)
+        dependent = userdata[['year','month']]
+        independent = userdata['sales']
+    
+        import numpy as np
+        import pandas as pd
+    
+        import xgboost
+        from sklearn.model_selection import train_test_split
+        from sklearn.model_selection import GridSearchCV
+        from sklearn.metrics import roc_auc_score
+    
+        import matplotlib.pyplot as plt
+    
+        #model = xgboost.XGBRegressor()
+        from sklearn.ensemble import RandomForestRegressor
+        model = RandomForestRegressor(random_state=1,n_jobs=-1)
+        model.fit(dependent, independent)
+        #future=pd.read_csv('future_dates.csv')
+    
+    
+        future_prediction = model.predict(future_dates)
+        #print(future_prediction)
+        df=pd.DataFrame(future_prediction)
+        df.columns = ["SALES"]
+        frames = [fut_date, df]
+      
+      
+        result = pd.concat(frames,axis=1)
+        result['itemname'] = item
+        result['itemid'] =item_id 
+        result.columns = ['Date','Predict','ItemName','ItemId']
+        #result['Predict']=result["Predict"].astype(int)
+        result['UpperLimit']=result["Predict"].mean()+result['Predict'].std()*3
+        result['LowerLimit']=result['Predict'].mean()-result['Predict'].std()*3
+        result["LowerLimit"][result["LowerLimit"] < 0] = 0
+        print(result)
+        result.to_json('forcast.json', orient="records")
+    
+        with open('forcast.json', 'r') as json_file:
+            json_load = json.load(json_file)
+            #url = "https://demo.bizgaze.app/apis/v4/bizgaze/integrations/demandforecast/saveforecast/List"
+            url=post_url
+    
+            payload = json.dumps(json_load)#.replace("]", "").replace("[", "")
+            print(payload)
+        #print(payload)
+            headers = {
+            #'Authorization': 'stat 263162e61f084d3392f162eb7ec39b2c',#demo
+            'Authorization': post_url_token,#test
+            'Content-Type': 'application/json'
+            }
+            response = requests.request("POST", url, headers=headers, data=payload)
+        # print("##############################################################")
+            print(response.text)
+
+    
+    #output={"response":"success","result":json_data}
+    #print(output)
+    return 'done'
+
+
+
+###############################################################################################################################################################
+
+#####################################################################################################################
+
+@app.route("/sales_forcast", methods=["GET", "POST"])
+def sales_forcast():
+    #wise= request.args.get('wise').replace('{','').replace('}','')
+    #Num= request.args.get('value').replace('{','').replace('}','')
+    #print(wise)
+    #print(Num)
+    Dataset = request.get_json()
+    a = Dataset
+    wise = a['wise']
+    # print(x)
+    Num = a['future_dates']
+    get_url = a['get_url']
+    get_url_token = a['get_url_token']
+    post_url = a['post_url']
+    post_url_token = a['post_url_token']
+
+    #print(Dataset)
+    # import pandas as pd
+    # df=pd.DataFrame(Dataset)
+    # print(df)
+    # a = Dataset
+    #x = a['wise']
+    # cmd = "python C:\\Users\\Bizga\\Desktop\\forcast\\XGdaywise.py"
+    # os.system(cmd)
+   
+    #split=wise
+    # wise='month'
+    # Num=5
+    if wise=='days':
+        print('daywise groupby')
+        output=day(Num,get_url,get_url_token,post_url,post_url_token)
+        # cmd = "python C:\\Users\\Bizga\\Desktop\\forcast\\XGdaywise.py"+" "+ Num
+        # os.system(cmd)
+    else:
+        print('monthwise groupby')
+        output=month(Num,get_url,get_url_token,post_url,post_url_token)
+        # payload = json.dumps(output)
+        # payload_list="["+payload+"]"
+
+    return output
+
+if __name__ == "__main__":
+    app.run(host='0.0.0.0', port=8082)

Events/requriments.txt

@@ -0,0 +1,35 @@
+certifi==2022.12.7
+charset-normalizer==3.1.0
+click==8.1.3
+contourpy==1.0.7
+cycler==0.11.0
+Flask==2.2.3
+fonttools==4.39.3
+idna==3.4
+importlib-metadata==6.3.0
+importlib-resources==5.12.0
+itsdangerous==2.1.2
+Jinja2==3.1.2
+joblib==1.2.0
+kiwisolver==1.4.4
+MarkupSafe==2.1.2
+matplotlib==3.7.1
+numpy==1.24.2
+packaging==23.0
+pandas==2.0.0
+patsy==0.5.3
+Pillow==9.5.0
+pyparsing==3.0.9
+python-dateutil==2.8.2
+pytz==2023.3
+requests==2.28.2
+scikit-learn==1.2.2
+scipy==1.10.1
+six==1.16.0
+statsmodels==0.13.5
+threadpoolctl==3.1.0
+tzdata==2023.3
+urllib3==1.26.15
+Werkzeug==2.2.3
+xgboost==1.7.5
+zipp==3.15.0

Events/src/Dockerfile

@@ -0,0 +1,18 @@
+# Use an official Python runtime as a parent image
+FROM python:3.8.10
+
+# Set the working directory in the container
+WORKDIR /opt/events
+
+# Copy the current directory contents into the container at /opt/attendance
+COPY . .
+
+# Install any needed packages specified in requirements.txt
+RUN apt update && apt install -y libgl1-mesa-glx && apt --fix-broken install && \
+    pip install --no-cache-dir -r requirements.txt
+
+# Make port 8081 available to the world outside this container
+EXPOSE 8081
+
+# Run app.py when the container launches
+CMD ["python", "./myproject.py"]

Events/src/myproject2.py

@@ -0,0 +1,419 @@
+import pickle
+import numpy as np
+import face_recognition
+import os
+from flask import Flask, render_template, request, redirect, send_file
+#import shutil
+import cv2
+
+app = Flask(__name__)
+
+#datasetPath = "/opt/bizgaze/events.bizgaze.app/wwwroot/_files/1/Gallery/"
+#peoplePath = "/opt/bizgaze/events.bizgaze.app/wwwroot/_files/People/"
+
+
+
+
+@app.route('/', methods=['GET'])
+def home():
+    return render_template('index.html')
+
+
+
+@app.route('/predict', methods=["GET", "POST"])
+def predict():
+    Dataset = request.get_json()
+    a = Dataset
+    peoplePath = a['People']
+ 
+    #print(peoplePath1)
+    datasetPath = a['Gallery']
+  
+    #print(datasetPath)
+   
+    print('starting')
+    def saveEncodings(encs, names, fname="encodings.pickle"):
+        print('encoding')
+        """
+        Save encodings in a pickle file to be used in future.
+
+        Parameters
+        ----------
+        encs : List of np arrays
+            List of face encodings.
+        names : List of strings
+            List of names for each face encoding.
+        fname : String, optional
+            Name/Location for pickle file. The default is "encodings.pickle".
+
+        Returns
+        -------
+        None.
+
+        """
+
+        data = []
+        d = [{"name": nm, "encoding": enc} for (nm, enc) in zip(names, encs)]
+        data.extend(d)
+
+        encodingsFile = fname
+
+        # dump the facial encodings data to disk
+        print("[INFO] serializing encodings...")
+        f = open(encodingsFile, "wb")
+        f.write(pickle.dumps(data))
+        f.close()
+
+        # Function to read encodings
+
+    def readEncodingsPickle(fname):
+        """
+        Read Pickle file.
+
+        Parameters
+        ----------
+        fname : String
+            Name of pickle file.(Full location)
+
+        Returns
+        -------
+        encodings : list of np arrays
+            list of all saved encodings
+        names : List of Strings
+            List of all saved names
+
+        """
+
+        data = pickle.loads(open(fname, "rb").read())
+        data = np.array(data)
+        encodings = [d["encoding"] for d in data]
+        names = [d["name"] for d in data]
+        return encodings, names
+
+    # Function to create encodings and get face locations
+    def createEncodings(image):
+        """
+        Create face encodings for a given image and also return face locations in the given image.
+
+        Parameters
+        ----------
+        image : cv2 mat
+            Image you want to detect faces from.
+
+        Returns
+        -------
+        known_encodings : list of np array
+            List of face encodings in a given image
+        face_locations : list of tuples
+            list of tuples for face locations in a given image
+
+        """
+
+        # Find face locations for all faces in an image
+        face_locations = face_recognition.face_locations(image)
+
+        # Create encodings for all faces in an image
+        known_encodings = face_recognition.face_encodings(image, known_face_locations=face_locations)
+        return known_encodings, face_locations
+
+    # Function to compare encodings
+    def compareFaceEncodings(unknown_encoding, known_encodings, known_names):
+        """
+        Compares face encodings to check if 2 faces are same or not.
+
+        Parameters
+        ----------
+        unknown_encoding : np array
+            Face encoding of unknown people.
+        known_encodings : np array
+            Face encodings of known people.
+        known_names : list of strings
+            Names of known people
+
+        Returns
+        -------
+        acceptBool : Bool
+            face matched or not
+        duplicateName : String
+            Name of matched face
+        distance : Float
+            Distance between 2 faces
+
+        """
+        duplicateName = ""
+        distance = 0.0
+        matches = face_recognition.compare_faces(known_encodings, unknown_encoding, tolerance=0.47)
+        face_distances = face_recognition.face_distance(known_encodings, unknown_encoding)
+        best_match_index = np.argmin(face_distances)
+        distance = face_distances[best_match_index]
+        if matches[best_match_index]:
+            acceptBool = True
+            duplicateName = known_names[best_match_index]
+        else:
+            acceptBool = False
+            duplicateName = ""
+        return acceptBool, duplicateName, distance
+
+    p = []
+
+    # Save Image to new directory
+    def saveImageToDirectory(image, name, imageName):
+        """
+        Saves images to directory.
+
+        Parameters
+        ----------
+        image : cv2 mat
+            Image you want to save.
+        name : String
+            Directory where you want the image to be saved.
+        imageName : String
+            Name of image.
+
+        Returns
+        -------
+        None.
+
+        """
+        path = "./output/" + name
+        path1 = "./output/" + name
+        if os.path.exists(path):
+            pass
+        else:
+            if not os.path.exists(path):
+                os.makedirs(path)
+            # os.mkdir(path,exist_ok=True)
+        cv2.imwrite(path + "/" + imageName, image)
+        x = []
+        c = (path1 + "/" + imageName)
+        x.append(c)
+        p.append(x)
+
+    # Function for creating encodings for known people
+    def processKnownPeopleImages(path=peoplePath, saveLocation="./known_encodings.pickle"):
+        """
+        Process images of known people and create face encodings to compare in future.
+        Eaach image should have just 1 face in it.
+
+        Parameters
+        ----------
+        path : STRING, optional
+            Path for known people dataset. The default is "C:/inetpub/vhosts/port82/wwwroot/_files/People".
+            It should be noted that each image in this dataset should contain only 1 face.
+        saveLocation : STRING, optional
+            Path for storing encodings for known people dataset. The default is "./known_encodings.pickle in current directory".
+
+        Returns
+        -------
+        None.
+
+        """
+
+        known_encodings = []
+        known_names = []
+        for img in os.listdir(path):
+            imgPath = path + img
+
+            # Read image
+            image = cv2.imread(imgPath)
+            name = img.rsplit('.')[0]
+            # Resize
+            image = cv2.resize(image, (0, 0), fx=0.9, fy=0.9, interpolation=cv2.INTER_LINEAR)
+
+            # Get locations and encodings
+            encs, locs = createEncodings(image)
+            try:
+                known_encodings.append(encs[0])
+            except IndexError:
+                pass
+            known_names.append(name)
+
+            for loc in locs:
+                top, right, bottom, left = loc
+
+            # Show Image
+            #cv2.rectangle(image, (left, top), (right, bottom), color=(255, 0, 0), thickness=2)
+            # cv2.imshow("Image", image)
+           # cv2.waitKey(1)
+            #cv2.destroyAllWindows()
+        saveEncodings(known_encodings, known_names, saveLocation)
+
+    # Function for processing dataset images
+    def processDatasetImages(saveLocation="./Gallery_encodings.pickle"):
+        """
+        Process image in dataset from where you want to separate images.
+        It separates the images into directories of known people, groups and any unknown people images.
+        Parameters
+        ----------
+        path : STRING, optional
+            Path for known people dataset. The default is "D:/port1004/port1004/wwwroot/_files/People".
+            It should be noted that each image in this dataset should contain only 1 face.
+        saveLocation : STRING, optional
+            Path for storing encodings for known people dataset. The default is "./known_encodings.pickle in current directory".
+
+        Returns
+        -------
+        None.
+
+        """
+        # Read pickle file for known people to compare faces from
+        people_encodings, names = readEncodingsPickle("./known_encodings.pickle")
+
+        for root, dirs, files in os.walk(datasetPath, topdown=False):
+
+            for name in files:
+                s = os.path.join(root, name)
+                #print(p)
+          #  imgPath = path + img
+
+            # Read image
+                image = cv2.imread(s)
+                orig = image.copy()
+
+            # Resize
+                image = cv2.resize(image, (0, 0), fx=0.9, fy=0.9, interpolation=cv2.INTER_LINEAR)
+
+            # Get locations and encodings
+                encs, locs = createEncodings(image)
+
+            # Save image to a group image folder if more than one face is in image
+            # if len(locs) > 1:
+            #     saveImageToDirectory(orig, "Group", img)
+
+            # Processing image for each face
+                i = 0
+                knownFlag = 0
+                for loc in locs:
+                    top, right, bottom, left = loc
+                    unknown_encoding = encs[i]
+                    i += 1
+                    acceptBool, duplicateName, distance = compareFaceEncodings(unknown_encoding, people_encodings, names)
+                    if acceptBool:
+                        saveImageToDirectory(orig, duplicateName,name)
+                        knownFlag = 1
+                if knownFlag == 1:
+                    print("Match Found")
+                else:
+                    saveImageToDirectory(orig, "0",name)
+
+            # Show Image
+            #     cv2.rectangle(image, (left, top), (right, bottom), color=(255, 0, 0), thickness=2)
+            # # cv2.imshow("Image", image)
+            #     cv2.waitKey(1)
+            #     cv2.destroyAllWindows()
+
+
+    def main():
+        """
+        Main Function.
+
+        Returns
+        -------
+        None.
+
+        """
+
+        processKnownPeopleImages(peoplePath)
+        processDatasetImages(datasetPath)
+        # shutil.make_archive('./Images', 'zip','./output')
+        # p='./Images.zip'
+        # return send_file(p,as_attachment=True)
+
+
+        # import pandas as pd
+        # q = pd.DataFrame(p)
+        # x = q
+        # # x.drop(x.columns[0], axis=1, inplace=True)
+        # df = x.groupby([0], as_index=False).count()
+        # z = df[0].str.split('/', expand=True)
+
+
+        # for i, group in z.groupby([2]):
+        #     group.drop(group.columns[2], axis=1, inplace=True)
+            #group.to_csv(f'./output1/{i}.csv', index=False, sep='/', header=False)
+
+
+##############################csv creation code ##############################
+        import pandas as pd
+        q = pd.DataFrame(p)
+        m = q
+        #print(m)
+        #   x.drop(x.columns[Unnam], axis=1, inplace=True)
+        df = m.groupby([0], as_index=False).count()
+        first_column_name = df.columns[0]
+
+        # Rename the first column
+        df.rename(columns={first_column_name: 'col'}, inplace=True)
+        #print(df)
+        z = df['col'].str.split('/', expand=True)
+        z['ImagePath'] = z[3]
+
+        result = z.drop([0,1,3], axis=1)
+        result.rename({result.columns[-1]: 'test'}, axis=1, inplace=True)
+        # print(result)
+        result.to_csv('results1.csv')
+        import pandas as pd
+        import os
+        c = []
+        for root, dirs, files in os.walk(datasetPath, topdown=False):
+            for name in files:
+                # print(name)
+                L = os.path.join(root, name)
+                c.append(L)
+        df = pd.DataFrame(c)
+        #print('seconfdf')
+      
+        first_column_name = df.columns[0]
+
+        # Rename the first column
+        df.rename(columns={first_column_name: 'col'}, inplace=True)
+        print(df)
+        df1 = df['col'].str.split("\\", expand=True)
+        df1.rename({df1.columns[-2]: 'abc'}, axis=1, inplace=True)
+        #print('this is df1')
+        #print(df1)
+        df1.rename({df1.columns[-1]: 'test'}, axis=1, inplace=True)
+        merge = pd.merge(df1, result, on='test', how='left')
+        merge.to_csv('merge.csv')
+        mergesplit = merge.loc[:,'test'].str.split(".", expand=True)
+        mergesplit.rename({mergesplit.columns[-2]: 'ImageName'}, axis=1, inplace=True)
+        mergesplit =  mergesplit.loc[:,'ImageName' ]
+
+        merge.rename({merge.columns[-1]: 'Matched'}, axis=1, inplace=True)
+        merge['EventName'] = merge['abc']
+        merge['Imagepath']= datasetPath+merge['EventName']+'/'+ + merge['test']
+
+        frames = [merge, mergesplit]
+
+        r = pd.concat(frames, axis=1, join='inner')
+        r=r.iloc[:,3:]
+        #print(r)
+        r.to_csv('path.csv', index=False)
+        #r.to_json(r'./matched.json', orient="records")
+        column_list = ['Matched','Imagepath', 'ImageName', 'EventName']
+        r[column_list].to_json('matched.json', orient="records")
+
+#############################################################################################
+
+        print("Completed")
+
+    if __name__ == "__main__":
+        main()
+
+    #    return render_template('index.html')
+    p = './matched.json'
+    return send_file(p,as_attachment=True)
+
+   # return 'ALL IMAGES MATCHED'
+
+
+@app.route('/json')
+def json():
+    p = './matched.json'
+    return send_file(p,as_attachment=True)
+
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0",port=8081)
+
+

Events/src/requirements.txt

@@ -0,0 +1,35 @@
+certifi==2022.12.7
+charset-normalizer==3.1.0
+click==8.1.3
+contourpy==1.0.7
+cycler==0.11.0
+Flask==2.2.3
+fonttools==4.39.3
+idna==3.4
+importlib-metadata==6.3.0
+importlib-resources==5.12.0
+itsdangerous==2.1.2
+Jinja2==3.1.2
+joblib==1.2.0
+kiwisolver==1.4.4
+MarkupSafe==2.1.2
+matplotlib==3.7.1
+numpy==1.24.2
+packaging==23.0
+pandas==2.0.0
+patsy==0.5.3
+Pillow==9.5.0
+pyparsing==3.0.9
+python-dateutil==2.8.2
+pytz==2023.3
+requests==2.28.2
+scikit-learn==1.2.2
+scipy==1.10.1
+six==1.16.0
+statsmodels==0.13.5
+threadpoolctl==3.1.0
+tzdata==2023.3
+urllib3==1.26.15
+Werkzeug==2.2.3
+xgboost==1.7.5
+zipp==3.15.0

Resume_parser/resume.parser.py

@@ -407,6 +407,6 @@ def upload_resume():
         return results[0]
 
 if __name__ == "__main__":
-    app.run(host='0.0.0.0', port=5003, debug=True)
+    app.run(host='0.0.0.0', port=1113, debug=True)