import streamlit as st
import torch
from PIL import Image
from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
#pickle.load(open('energy_model.pkl', 'rb'))
#vocab = np.load('w2i.p', allow_pickle=True)
print("="*150)
print("MODEL LOADED")
st.title("img_captioning_app")
#st.text("Build with Streamlit and OpenCV")
if "photo" not in st.session_state:
	st.session_state["photo"]="not done"

c2, c3 = st.columns([2,1])
def change_photo_state():
	st.session_state["photo"]="done"
print("="*150)
print("RESNET MODEL LOADED")

@st.cache
def load_image(img):
	im = Image.open(img)
	return im
activities = ["Detection","About"]
choice = st.sidebar.selectbox("Select Activty",activities)
uploaded_photo = c2.file_uploader("Upload Image",type=['jpg','png','jpeg'], on_change=change_photo_state)
camera_photo = c2.camera_input("Take a photo", on_change=change_photo_state)
if choice == 'Detection':
    st.subheader("Face Detection") 
    if st.session_state["photo"]=="done":
        if uploaded_photo:
            our_image= load_image(uploaded_photo)
        elif camera_photo:
            our_image= load_image(camera_photo)
        elif uploaded_photo==None and camera_photo==None:
            our_image= load_image('image.jpg')
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        model.to(device)
        max_length = 16
        num_beams = 4
        gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
        def predict_step(our_image):
            if our_image.mode != "RGB":
                our_image = our_image.convert(mode="RGB")
            pixel_values = feature_extractor(images=our_image, return_tensors="pt").pixel_values
            pixel_values = pixel_values.to(device)
            output_ids = model.generate(pixel_values, **gen_kwargs)
            preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
            preds = [pred.strip() for pred in preds]
            return preds
        predict_step(our_image)