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Dog_Breed_Classifier.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:697fceb5ecb77baff9ede71a0585ef7cb24dc430949fa8e7774c337d13839b63
+size 82934708

app.py

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+import streamlit as st
+import pandas as pd
+import tensorflow as tf
+import tensorflow_hub as hub
+import numpy as np
+
+st.title('Dog Breed Classifier App ')
+st.header('Made By Lakhan Singh :sunglasses:',divider='rainbow')
+
+label = {'afghan_hound': 0,
+ 'african_hunting_dog': 1,
+ 'airedale': 2,
+ 'basenji': 3,
+ 'basset': 4,
+ 'beagle': 5,
+ 'bedlington_terrier': 6,
+ 'bernese_mountain_dog': 7,
+ 'black-and-tan_coonhound': 8,
+ 'blenheim_spaniel': 9,
+ 'bloodhound': 10,
+ 'bluetick': 11,
+ 'border_collie': 12,
+ 'border_terrier': 13,
+ 'borzoi': 14,
+ 'boston_bull': 15,
+ 'bouvier_des_flandres': 16,
+ 'brabancon_griffon': 17,
+ 'bull_mastiff': 18,
+ 'cairn': 19,
+ 'cardigan': 20,
+ 'chesapeake_bay_retriever': 21,
+ 'chow': 22,
+ 'clumber': 23,
+ 'cocker_spaniel': 24,
+ 'collie': 25,
+ 'curly-coated_retriever': 26,
+ 'dhole': 27,
+ 'dingo': 28,
+ 'doberman': 29,
+ 'english_foxhound': 30,
+ 'english_setter': 31,
+ 'entlebucher': 32,
+ 'flat-coated_retriever': 33,
+ 'german_shepherd': 34,
+ 'german_short-haired_pointer': 35,
+ 'golden_retriever': 36,
+ 'gordon_setter': 37,
+ 'great_dane': 38,
+ 'great_pyrenees': 39,
+ 'groenendael': 40,
+ 'ibizan_hound': 41,
+ 'irish_setter': 42,
+ 'irish_terrier': 43,
+ 'irish_water_spaniel': 44,
+ 'irish_wolfhound': 45,
+ 'japanese_spaniel': 46,
+ 'keeshond': 47,
+ 'kerry_blue_terrier': 48,
+ 'komondor': 49,
+ 'kuvasz': 50,
+ 'labrador_retriever': 51,
+ 'leonberg': 52,
+ 'lhasa': 53,
+ 'malamute': 54,
+ 'malinois': 55,
+ 'maltese_dog': 56,
+ 'mexican_hairless': 57,
+ 'miniature_pinscher': 58,
+ 'miniature_schnauzer': 59,
+ 'newfoundland': 60,
+ 'norfolk_terrier': 61,
+ 'norwegian_elkhound': 62,
+ 'norwich_terrier': 63,
+ 'old_english_sheepdog': 64,
+ 'otterhound': 65,
+ 'papillon': 66,
+ 'pekinese': 67,
+ 'pembroke': 68,
+ 'pomeranian': 69,
+ 'pug': 70,
+ 'redbone': 71,
+ 'rhodesian_ridgeback': 72,
+ 'rottweiler': 73,
+ 'saint_bernard': 74,
+ 'saluki': 75,
+ 'samoyed': 76,
+ 'schipperke': 77,
+ 'scotch_terrier': 78,
+ 'scottish_deerhound': 79,
+ 'sealyham_terrier': 80,
+ 'shetland_sheepdog': 81,
+ 'standard_poodle': 82,
+ 'standard_schnauzer': 83,
+ 'sussex_spaniel': 84,
+ 'tibetan_mastiff': 85,
+ 'tibetan_terrier': 86,
+ 'toy_terrier': 87,
+ 'vizsla': 88,
+ 'weimaraner': 89,
+ 'whippet': 90,
+ 'wire-haired_fox_terrier': 91,
+ 'yorkshire_terrier': 92}
+
+model_summary = '''Model: "sequential"
+================================================================
+ Layer (type)                Output Shape              Param    
+=================================================================
+ random_flip (RandomFlip)    (None, 400, 400, 3)       0         
+                                                                 
+ random_rotation (RandomRot  (None, 400, 400, 3)       0         
+ ation)                                                          
+                                                                 
+ keras_layer (KerasLayer)    (None, 1280)              20331360  
+                                                                 
+ dropout (Dropout)           (None, 1280)              0         
+                                                                 
+ dense (Dense)               (None, 93)                119133    
+                                                                 
+=================================================================
+Total params: 20450493 (78.01 MB)
+Trainable params: 119133 (465.36 KB)
+Non-trainable params: 20331360 (77.56 MB)
+================================================================='''
+
+
+label_dt = pd.DataFrame(label,index=label.values())
+
+if st.checkbox('Do you want to check all breeds of dog that is used in this model'):
+    st.write('These are here')
+    st.write(label_dt.head(1))
+
+if st.checkbox('Check here model summary :sunglasses:'):
+    st.text(model_summary)
+
+img = st.file_uploader('## Upload a dog image to classified it breed : ',type=['png', 'jpg','jpeg'])
+
+@st.cache_resource
+
+def load_model():
+    model = tf.keras.models.load_model('Dog_Breed_Classifier.h5',custom_objects={'KerasLayer': hub.KerasLayer})
+    return model
+
+model = load_model()
+
+st.text('Model Loaded Suceessfully ...')
+
+def predict(model, img):
+    img = tf.keras.utils.load_img(img,target_size=(400,400))
+    img_array = tf.keras.utils.img_to_array(img)
+    img_array = img_array/255
+    st.image(img_array)
+
+    img_array = np.expand_dims(img_array,axis=0)
+    predictions = model.predict(img_array)
+
+    print('Prediction Value of the image is : ', np.argmax(predictions))
+
+    predicted_class = [i for i ,j in label.items() if j == np.argmax(predictions)]
+    confidence = round(100*(np.max(predictions[0])), 2)
+
+    st.subheader(f" Predicted Class: {predicted_class[0]}")
+    st.subheader(f" Confidence: {confidence}%")
+
+if st.button('show image with prediction'):
+    result = predict(model , img)
+
+if st.button("Clear All Cache"):
+    st.cache_data.clear()
+
+
+
+
+

requirements.txt

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+pandas == 1.5.3
+scipy == 1.11.3
+numpy == 1.23.5
+tensorflow == 2.13.0
+tensorflow-hub == 0.15.0
+python == 3.11.6
+streamlit == 1.27.2