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.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

.vscode/settings.json

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+{
+    "python.pythonPath": "C:\\Users\\BHATTJ\\Anaconda3\\envs\\nlp\\python.exe"
+}

Untitled.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

requirement.txt

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+streamlit==0.80.0
+tensorflow==2.5.0rc1
+opencv-python==4.5.1.48

saved_model/mdl_wts.hdf5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dd65e0741f782e2d379ecb76740aa665ad480d5742f09f1febdd681c2ef4df67
+size 25203432

scr.py

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+import pandas as pd
+import numpy as np
+
+from sklearn.datasets import load_iris
+iris = load_iris()

streamlit_host.py

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+import cv2
+import numpy as np
+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2,preprocess_input as mobilenet_v2_preprocess_input
+
+model = tf.keras.models.load_model("saved_model/mdl_wts.hdf5")
+### load file
+uploaded_file = st.file_uploader("Choose a image file", type="jpg")
+
+map_dict = {0: 'dog',
+            1: 'horse',
+            2: 'elephant',
+            3: 'butterfly',
+            4: 'chicken',
+            5: 'cat',
+            6: 'cow',
+            7: 'sheep',
+            8: 'spider',
+            9: 'squirrel'}
+
+
+if uploaded_file is not None:
+    # Convert the file to an opencv image.
+    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    opencv_image = cv2.imdecode(file_bytes, 1)
+    opencv_image = cv2.cvtColor(opencv_image, cv2.COLOR_BGR2RGB)
+    resized = cv2.resize(opencv_image,(224,224))
+    # Now do something with the image! For example, let's display it:
+    st.image(opencv_image, channels="RGB")
+
+    resized = mobilenet_v2_preprocess_input(resized)
+    img_reshape = resized[np.newaxis,...]
+
+    Genrate_pred = st.button("Generate Prediction")    
+    if Genrate_pred:
+        prediction = model.predict(img_reshape).argmax()
+        st.title("Predicted Label for the image is {}".format(map_dict [prediction]))