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.gitignore ADDED
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+ # Byte-compiled / optimized / DLL files
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+ __pycache__/
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+ *.py[cod]
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+ *$py.class
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+
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+ # C extensions
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+ *.so
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+
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+ # Distribution / packaging
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+ .Python
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+ build/
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+ develop-eggs/
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+ dist/
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+ downloads/
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+ eggs/
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+ .eggs/
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+ lib/
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+ lib64/
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+ parts/
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+ sdist/
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+ var/
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+ wheels/
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+ pip-wheel-metadata/
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+ share/python-wheels/
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+ *.egg-info/
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+ .installed.cfg
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+ *.egg
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+ MANIFEST
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+
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+ # PyInstaller
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+ # Django stuff:
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+ # Flask stuff:
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+ ipython_config.py
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+
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+ # pyenv
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+ .python-version
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+
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+ # pipenv
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+ # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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+ # PEP 582; used by e.g. github.com/David-OConnor/pyflow
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+ __pypackages__/
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+ # Celery stuff
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+ # SageMath parsed files
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+ # Environments
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+ # Spyder project settings
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+ # Rope project settings
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+ # mkdocs documentation
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+ # mypy
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+ # Pyre type checker
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+ .pyre/
.vscode/settings.json ADDED
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+ {
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+ "python.pythonPath": "C:\\Users\\BHATTJ\\Anaconda3\\envs\\nlp\\python.exe"
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+ }
Untitled.ipynb ADDED
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+ {
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+ "cells": [],
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+ "metadata": {},
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+ "nbformat": 4,
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+ "nbformat_minor": 4
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+ }
requirement.txt ADDED
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+ streamlit==0.80.0
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+ tensorflow==2.5.0rc1
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+ opencv-python==4.5.1.48
saved_model/mdl_wts.hdf5 ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:dd65e0741f782e2d379ecb76740aa665ad480d5742f09f1febdd681c2ef4df67
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+ size 25203432
scr.py ADDED
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+ import pandas as pd
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+ import numpy as np
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+
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+ from sklearn.datasets import load_iris
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+ iris = load_iris()
streamlit_host.py ADDED
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+ import cv2
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+ import numpy as np
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+ import streamlit as st
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+ import tensorflow as tf
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+ from tensorflow.keras.preprocessing import image
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+ from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2,preprocess_input as mobilenet_v2_preprocess_input
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+
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+ model = tf.keras.models.load_model("saved_model/mdl_wts.hdf5")
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+ ### load file
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+ uploaded_file = st.file_uploader("Choose a image file", type="jpg")
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+
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+ map_dict = {0: 'dog',
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+ 1: 'horse',
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+ 2: 'elephant',
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+ 3: 'butterfly',
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+ 4: 'chicken',
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+ 5: 'cat',
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+ 6: 'cow',
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+ 7: 'sheep',
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+ 8: 'spider',
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+ 9: 'squirrel'}
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+
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+
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+ if uploaded_file is not None:
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+ # Convert the file to an opencv image.
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+ file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
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+ opencv_image = cv2.imdecode(file_bytes, 1)
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+ opencv_image = cv2.cvtColor(opencv_image, cv2.COLOR_BGR2RGB)
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+ resized = cv2.resize(opencv_image,(224,224))
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+ # Now do something with the image! For example, let's display it:
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+ st.image(opencv_image, channels="RGB")
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+
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+ resized = mobilenet_v2_preprocess_input(resized)
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+ img_reshape = resized[np.newaxis,...]
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+
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+ Genrate_pred = st.button("Generate Prediction")
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+ if Genrate_pred:
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+ prediction = model.predict(img_reshape).argmax()
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+ st.title("Predicted Label for the image is {}".format(map_dict [prediction]))