first upload image caption generator

Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "-b", "0.0.0.0:7860", "main:app", "--host"]

main.py

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+import os
+import io
+from flask import Flask, request, jsonify
+import base64
+import numpy as np
+from pickle import load
+from PIL import Image
+from keras.applications.xception import Xception #to get pre-trained model Xception
+from keras.models import load_model
+from keras.preprocessing.sequence import pad_sequences
+
+os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
+
+app = Flask(__name__)
+
+# MAX_LENGTH = 34
+MAX_LENGTH = 38
+
+def extract_features(image_data, model):
+    try:
+        image = Image.open(io.BytesIO(image_data))
+    except Exception as e:
+        print("ERROR: Can't open image! Ensure that image data is correct and in the expected format")
+        print(str(e))
+        return None
+
+    image = image.resize((299,299))
+    image = np.array(image)
+
+    # for 4 channels images, we need to convert them into 3 channels
+    if image.shape[2] == 4:
+        image = image[..., :3]
+
+    image = np.expand_dims(image, axis=0)
+    image = image/127.5
+    image = image - 1.0
+    feature = model.predict(image)
+
+    return feature
+
+
+def word_for_id(integer, tokenizer):
+    for word, index in tokenizer.word_index.items():
+        if index == integer:
+            return word
+    return None
+
+
+def generate_desc(model, tokenizer, photo, max_length):
+    in_text = 'start'
+    for i in range(max_length):
+        sequence = tokenizer.texts_to_sequences([in_text])[0]
+        sequence = pad_sequences([sequence], maxlen=max_length)
+        pred = model.predict([photo,sequence], verbose=0)
+        pred = np.argmax(pred)
+        word = word_for_id(pred, tokenizer)
+        if word is None or word == 'end':
+            break
+        in_text += ' ' + word
+    return in_text.replace('start ', '')
+
+
+# API endpoint to receive image and generate caption
+@app.route('/api', methods=['POST'])
+def generate_caption():
+    try:
+        base64_image_data = request.form['image']
+        # return jsonify({'caption': base64_image_data}), 200
+        # Replace spaces with "+" characters to handle cases where "+" characters are missing
+        # base64_image_data = base64_image_data.replace(" ", "+")
+        
+        # Decode the Base64 string into binary image data
+        image_data = base64.b64decode(base64_image_data)
+
+        tokenizer = load(open("tokenizer.p","rb"))
+        # model = load_model('model_9.h5')
+        model = load_model('models/model_9.keras')
+
+        xception_model = Xception(include_top=False, pooling="avg")
+        photo = extract_features(image_data, xception_model)
+
+        if photo is None:
+            return jsonify({'error': 'Failed to extract features from the image'}), 400
+        
+        caption = generate_desc(model, tokenizer, photo, MAX_LENGTH)
+
+        # Return the generated caption
+        return jsonify({'caption': caption}), 200
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0')

requirements.txt

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+uvicorn
+Flask==2.2.5
+Pillow==9.4.0
+numpy==1.25.2
+keras==3.0
+tensorflow==2.16.1