Update app.py

app.py

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+import gradio as gr
+import pickle
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.applications import EfficientNetB7
+from tensorflow.keras.applications.efficientnet import preprocess_input
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.models import Model
+
+
+# load vgg16 model
+pre_trained_model = EfficientNetB7(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+# Freeze the base model
+pre_trained_model.trainable = False
+
+pre_trained_model = tf.keras.Sequential([
+    pre_trained_model,
+    tf.keras.layers.GlobalAveragePooling2D()
+])
+# restructure the model
+pre_trained_model = Model(inputs=pre_trained_model.inputs, outputs=pre_trained_model.layers[-1].output)
+
+model = tf.keras.models.load_model("Image_Captioner_model.h5")
+
+tokenizer = Tokenizer()
+with open("Image_Captioner_tokenizer.pkl", "rb") as f:
+    tokenizer = pickle.load(f)
+
+def idx_to_word(integer, tokenizer):
+    for word, index in tokenizer.word_index.items():
+        if index == integer:
+            return word
+    return None
+
+# generate caption for an image
+def predict_caption(model, image, tokenizer, max_length):
+    # add start tag for generation process
+    in_text = 'startseq'
+    # iterate over the max length of sequence
+    for i in range(max_length):
+        # encode input sequence
+        sequence = tokenizer.texts_to_sequences([in_text])[0]
+        # pad the sequence
+        sequence = pad_sequences([sequence], max_length)
+        # predict next word
+        yhat = model.predict([image, sequence], verbose=0)
+        # get index with high probability
+        yhat = np.argmax(yhat)
+        # convert index to word
+        word = idx_to_word(yhat, tokenizer)
+        # stop if word not found
+        if word is None:
+            break
+        # append word as input for generating next word
+        in_text += " " + word
+        # stop if we reach end tag
+        if word == 'endseq':
+            break
+    cut_text = ' '.join(in_text.split()[1:-1])
+    return cut_text
+
+def google_image_testing(inp):
+    # Convert input into jpg file
+    input_image = Image.fromarray(inp)
+    input_image.save("input_image.jpg")
+    # Load input Image
+    image_path = 'input_image.jpg'
+    image = load_img(image_path, target_size=(224, 224))
+    # convert image pixels to numpy array
+    image = img_to_array(image)
+    # reshape data for model
+    image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
+    # preprocess image for vgg
+    image = preprocess_input(image)
+    # extract features
+    img_feature = pre_trained_model.predict(image, verbose=0)
+    # predict the caption
+    predicted = predict_caption(model, img_feature, tokenizer, max_length=35)
+    return predicted
+
+demo = gr.Interface(fn=google_image_testing, inputs='image',outputs='text',title='Image Captioner')
+demo.launch(debug=True,share=True)