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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", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]

caption_api.py

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+from flask import Flask,request
+import google.generativeai as palm
+import re
+import pickle
+import numpy as np
+import requests
+from PIL import Image
+from io import BytesIO
+from tensorflow.keras.applications.vgg16 import VGG16, 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
+from tensorflow.keras.utils import to_categorical, plot_model
+from tensorflow.keras.layers import Input, Dense, LSTM, Embedding, Dropout, add
+from tensorflow.keras.models import load_model
+
+
+#tokenizer=pickle.load(open('tokenizer.pkl','rb'))
+#vgg_model = load_model('vgg_model.h5')
+model = load_model('best_model.h5')
+max_len=35
+
+
+with open('captions.txt','r') as f:
+    next(f)
+    caption_file=f.read()
+
+captions={}
+for line in caption_file.split('\n'):
+    values=line.split(",")
+    if(len(line)<2):
+        continue
+    #get image_id
+    image_id=values[0]
+    image_id=image_id.split('.')[0]
+    #get caption
+    caption=values[1:]
+    caption=" ".join(caption)
+    #mapping caption
+    if image_id not in captions:
+        captions[image_id]=[]
+    captions[image_id].append(caption)
+
+def clean(captions):
+    for key,caption_ in captions.items():
+        for i in range(len(caption_)):
+            caption=caption_[i]
+            #process caption
+            caption=caption.lower()
+            caption = re.sub('[^a-zA-Z]', ' ', caption)
+            caption = re.sub('\s+', ' ', caption)
+            caption=" ".join([word for word in caption.split() if len(word)>1])
+            caption="startseq "+caption+" endseq"
+            caption_[i]=caption
+
+clean(captions)
+
+all_captions=[]
+for key,caption_ in captions.items():
+        for i in range(len(caption_)):
+            all_captions.append(caption_[i])
+
+tokenizer=Tokenizer()
+tokenizer.fit_on_texts(all_captions)
+
+# load vgg16 model
+vgg_model = VGG16()
+# restructure the model
+vgg_model = Model(inputs=vgg_model.inputs, outputs=vgg_model.layers[-2].output)
+
+def index_to_word(indx,tokenizer):
+  for word,index in tokenizer.word_index.items():
+    if index == indx:
+      return word
+  return None
+
+def predict_captions(model,image,tokenizer,max_len):
+  in_text='startseq'
+  for i in range(max_len):
+    seq=tokenizer.texts_to_sequences([in_text])[0]
+    seq=pad_sequences([seq],max_len)[0]
+    if len(image.shape) == 3:
+            image = np.expand_dims(image, axis=0)
+    y_pred=model.predict([image, np.expand_dims(seq, axis=0)],verbose=0)
+    y_pred=np.argmax(y_pred)
+
+    word=index_to_word(y_pred,tokenizer)
+    if word == None:
+      break
+    in_text += " " + word
+    if word == 'endseq':
+      break
+  return in_text
+
+def caption_generator(url):
+    #load image
+    response = requests.get(url)
+    image= Image.open(BytesIO(response.content))
+    image = image.resize((224,224))
+    #convert image into numpy array
+    image=img_to_array(image)
+    #reshape image
+    image=image.reshape((1,image.shape[0],image.shape[1],image.shape[2]))
+    #preprrocess image for vgg16
+    image=preprocess_input(image)
+    #extract features
+    feature=vgg_model.predict(image,verbose=0)
+    y_pred = predict_captions(model, feature, tokenizer, max_len)
+    #plt.imshow(image_pic)
+    return y_pred
+
+app=Flask(__name__)
+
+@app.route('/')
+def home():
+    return "HELLO WORLD"
+
+@app.route('/predict',methods=['POST'])
+def predict():
+    url=request.get_json()
+    print(url)
+    result=caption_generator(url['url'])
+    palm.configure(api_key='AIzaSyDDXOjF1BBgJM6g1tMV-6tcI7xh9-ctvQU')
+    #models = [m for m in palm.list_models() if 'generateText' in m.supported_generation_methods]
+    #model = models[0].name
+    model="models/text-bison-001"
+    prompt = "Generate a creative & attractive instagram caption of 10-30 words words for" + str(result)
+    completion = palm.generate_text(
+        model=model,
+        prompt=prompt,
+        temperature=0,
+        # The maximum length of the response
+        max_output_tokens=100,
+    )
+    return completion.result
+    #return {'caption':str(result)}
+
+if __name__ == '__main__':
+    app.run(debug=True)

requirements.txt

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+Flask
+numpy
+Pillow
+protobuf
+Requests
+tensorflow
+tensorflow_intel