from fashion_clip.fashion_clip import FashionCLIP
import pickle
import subprocess
import streamlit as st
import numpy as np
from PIL import Image
import os
from streamlit_image_select import image_select
os.environ["CUDA_VISIBLE_DEVICES"] =""
import torch
torch.cuda.is_available = lambda : False
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
st.sidebar.write("# Shoping muse")

#query = st.sidebar.text_input("Enter some text", "A red dress")
#prompt = st.chat_input("Say something")
st.write("Shoping MUSE")

def horizontal_scroll_images(images):
    with st.beta_container():
        for img_path in images:
            st.image(img_path, use_column_width=True)
            
def horizontal_scroll_images(images,image_width=300):
    cols = st.columns(len(images))
    for col, img_path in zip(cols, images):
        
        col.image(img_path, use_column_width=True)
        
#def horizontal_scroll_images(images, image_width=300):
#    cols = st.columns(len(images))
#    for col, img_path in zip(cols, images):
#        col.image(img_path, width=image_width)             


new_size = (800, 600)  # Set your desired width and height

@st.cache_resource
def load_embedding_file():
    with open("embeddings_and_paths.pkl", "rb") as filino:
        data = pickle.load(filino)

        images = data["images_path"]
        embeddings = data["embeddings"]
    return images, embeddings

fclip = FashionCLIP('fashion-clip')

if not os.path.exists("clothing-dataset"):
    subprocess.run("git clone https://github.com/alexeygrigorev/clothing-dataset", shell=True)

#st.write("## Simple FashionCLIP search engine")
#query = st.text_input("Enter a description of the clothing item you want to find", "a red dress")
#query = prompt
images, image_embeddings = load_embedding_file()
image_cnt=8
def append_message(sender, message):
    chat_history.append((sender, message))

def chatbot_interface():
    st.sidebar.title("Chatbot Interface")

    user_input = st.sidebar.text_input("You:", key="user_input")

    if st.sidebar.button("Send"):
        append_message("You", user_input)
        # Replace the following line with your chatbot logic to generate a response
        append_message("Chatbot", f"Bot response to: {user_input}")


    query=user_input
    text_embedding = fclip.encode_text([query], 32)[0]
    arr=text_embedding.dot(image_embeddings.T)
    id_of_matched_object1=(-arr).argsort()[:image_cnt]
    id_of_matched_object = np.argmax(arr)

    image = Image.open(images[id_of_matched_object])
    #st.image(image)
    image=[]
    for k in id_of_matched_object1:
    	image.append(Image.open(images[k]).resize(new_size))
    img = image_select(
    label="Results",
    images=image,
    captions=[str(query) + "result  "] * (image_cnt),
	)

    st.sidebar.markdown("---")

    # Display the chat history
    st.sidebar.title("Chat History")

    for sender, message in chat_history:
        st.sidebar.text(f"{sender}: {message}")

# Initialize the chat history
chat_history = []

# Main content area
st.title("Muse Chatbot")

# Display the chatbot interface inside a box in the sidebar
st.sidebar.markdown("## Chatbot Box")




#text_embedding = fclip.encode_text([query], 32)[0]
#arr=text_embedding.dot(image_embeddings.T)
#id_of_matched_object1=(-arr).argsort()[:image_cnt]
#id_of_matched_object = np.argmax(arr)

#image = Image.open(images[id_of_matched_object])
#st.image(image)
#image=[]



#for k in id_of_matched_object1:
#	image.append(Image.open(images[k]).resize(new_size))


#img = image_select(
#    label="Results",
#    images=image,
#    captions=[str(query) + "result  "] * (image_cnt),
#)

	
#st.title("Horizontal Scroll of Images")

# Specify the width of the images
#image_width = 300

#horizontal_scroll_images(image)
#print(image)
#st.image(image , use_column_width=True, caption=["some generic text"] * (image_cnt))


chatbot_interface()