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
+import pickle
+
+# Load the pre-trained model
+@st.cache(allow_output_mutation=True)
+def load_model():
+    model = tf.keras.models.load_model('model.h5')
+    return model
+
+# Load the tokenizer
+@st.cache(allow_output_mutation=True)
+def load_tokenizer():
+    with open('tokenizer.pkl', 'rb') as handle:
+        tokenizer = pickle.load(handle)
+    return tokenizer
+
+model = load_model()
+tokenizer = load_tokenizer()
+
+# Function to preprocess the image
+def preprocess_image(image):
+    image = image.resize((299, 299))  # Resize to the input size of the model
+    image = np.array(image) / 255.0  # Normalize
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    return image
+
+# Function to generate caption
+def generate_caption(image):
+    image = preprocess_image(image)
+    predictions = model.predict(image)
+    predicted_caption = tokenizer.sequences_to_texts(predictions.argmax(axis=-1))
+    return predicted_caption[0]
+
+# Streamlit app
+st.title("Image Captioning App")
+st.write("Upload an image to generate a caption")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+    st.write("")
+    st.write("Generating caption...")
+    caption = generate_caption(image)
+    st.write(caption)