Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import tensorflow as tf
+import numpy as np
+
+# Load the pre-trained model
+caption_model = get_caption_model()
+
+# Load the index lookup dictionary
+with open('index_lookup.pkl', 'rb') as f:
+    index_lookup = pickle.load(f)
+
+# Set the maximum decoded sentence length
+max_decoded_sentence_length = 40
+
+def generate_caption(img):
+    # Preprocess the image
+    img = tf.expand_dims(img, 0)
+    img_embed = caption_model.cnn_model(img)
+    
+    # Pass the image features to the Transformer encoder
+    encoded_img = caption_model.encoder(img_embed, training=False)
+    
+    # Generate the caption using the Transformer decoder
+    decoded_caption = "<start> "
+    for i in range(max_decoded_sentence_length):
+        tokenized_caption = vectorization([decoded_caption])[:, :-1]
+        mask = tf.math.not_equal(tokenized_caption, 0)
+        predictions = caption_model.decoder(
+            tokenized_caption, encoded_img, training=False, mask=mask
+        )
+        sampled_token_index = np.argmax(predictions[0, i, :])
+        sampled_token = index_lookup[sampled_token_index]
+        if sampled_token == "<end>":
+            break
+        decoded_caption += " " + sampled_token
+    
+    decoded_caption = decoded_caption.replace("<start> ", "")
+    decoded_caption = decoded_caption.replace(" <end>", "").strip()
+    return decoded_caption
+
+st.title("Image Captioning")
+
+# Upload an image
+uploaded_file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+    
+    # Generate the caption
+    img = tf.keras.preprocessing.image.img_to_array(image)
+    img = tf.image.resize(img, (299, 299))
+    caption = generate_caption(img)
+    
+    # Display the generated caption
+    st.write("Generated Caption:", caption)