Add application file

app.py

@@ -0,0 +1,66 @@
+import gradio as gr
+import pickle as pkl
+
+from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input
+from tensorflow.keras.models import Model
+import tensorflow as tf
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+
+# Feature Extracting model
+vgg_model = VGG16()
+vgg_model.trainable = False
+
+img_model = Model(inputs=vgg_model.input,
+                  outputs=vgg_model.layers[-2].output)
+
+# Caption genartion model
+model = tf.keras.models.load_model('caption_genaration_model.h5')
+
+# load Tokenizer
+with open('tokenizer.pkl','rb') as f:
+    tokenizer = pkl.load(f)
+
+# convert index to word from prediction
+def index_to_word(word_idx):
+    return tokenizer.index_word[word_idx]
+
+# Resize layer
+resize_img = tf.keras.layers.Resizing(height=224, width=224)
+
+# Preprocces input Image
+def img_preprocces(img): 
+    img = tf.expand_dims(img,axis=0)
+    resized_image = resize_img(img)
+    img = preprocess_input(resized_image)
+    feature = vgg_model.predict(img,verbose=False)
+    return feature
+
+def genarate_caption(img):
+    seq_in = 'startseq'
+    feature_img = img_preprocces(img)
+
+    for _ in range(30):
+        # Tokenization & Padding
+        seq_in_sequence = tokenizer.texts_to_sequences([seq_in])[0]
+        seq_in_padded = pad_sequences([seq_in_sequence], padding='post',maxlen=30)
+
+        # Predict next word
+        y_hat = model.predict([feature_img,seq_in_padded],verbose=False)
+        word_index = y_hat.argmax(axis=1)
+        predicted_word = index_to_word(word_index[0])
+        if predicted_word == 'endseq':
+            break
+        seq_in = seq_in + ' ' + predicted_word 
+        
+    
+    return seq_in[9:]
+
+app = gr.Interface(
+    fn=genarate_caption,
+    inputs=['image'],
+    outputs=['text']
+)
+
+app.launch()