import gradio as gr
from PIL import Image
import numpy as np
from pickle import load
from tensorflow.keras.applications.xception import Xception
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.sequence import pad_sequences
from matplotlib import pyplot as plt

def extract_features(filename, model):
    try:
        image = Image.open(filename)
    except:
        print("ERROR: Couldn't open image! Make sure the image path and extension is correct")
    image = image.resize((299,299))
    image = np.array(image)
    # for images that has 4 channels, we 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:
            break
        in_text += ' ' + word
        if word == 'end':
            break
    return in_text.split()[1:-1]

max_length = 32
tokenizer = load(open("tokenizer.p","rb"))
model = load_model('models/model_9.h5')
xception_model = Xception(include_top=False, pooling="avg")

def caption_generator(img_path):
    photo = extract_features(img_path, xception_model)
    img = Image.open(img_path)
    description = generate_desc(model, tokenizer, photo, max_length)
    description = ' '.join(description)
    return description

inputs = gr.inputs.File(label="Select an Image")
outputs = gr.outputs.Textbox(label="Description")

gr.Interface(fn=caption_generator , inputs=inputs, outputs=outputs, capture_session=True).launch()