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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

saved_model/saved_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:09f1568e1d7a9251f9b73130626c1590f6dcdc9eda85fef8d5ff91bd31ce82e2
+size 8186264

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]))