import streamlit as st
import pickle
from html_information import html


def load_pickle_file(file_name):
    with open(file_name, 'rb') as f:
        return pickle.load(f)


def streamlit_carousel(header_name: str, rec_item_url: list,
                       rec_item_name: list, rec_dict: dict) -> None:
    st.header(header_name)
    st.write(rec_dict)
    mid_section = ""
    for index, value in enumerate(rec_item_url):
        mid_section += """<div class="item"><div id="image-container"><img src='""" + str(value) + """' /></div><p>""" + str(rec_item_name[index]) + """</p></div>"""

    mid_html = html + mid_section + """</div></div></body>"""
    st.markdown(mid_html, unsafe_allow_html=True)


def get_mapped_values(uid_list, uid_map_dict):
    res = []
    for val in uid_list:
        res.append(uid_map_dict[val])
    return res


uid_name_map = load_pickle_file('generalize_uid_name_map.pkl')
uid_media_map = load_pickle_file('generalize_uid_media_map.pkl')
img_rec = load_pickle_file('img.pkl')
text_rec = load_pickle_file('text.pkl')
both_rec = load_pickle_file('both.pkl')


text_dict = {
    "path": "gs://cml-datalake-dev/fynd_latest.json",
    "row": ["uid", "slug", "brand_name", "category_name", "attributes_gender", "attributes_name", "attributes_color", "medias"],
    "multiple_media": True,
    "max_workers": 15,
    "clip_model": "ViT-B/32",
    "token_word": 77,
    "text_row": ["brand_name", "category_name", "attributes_gender", "attributes_name", "attributes_color"],
    "req_row": ["uid", "slug"],
    "similarity_fields": ["category_name", "attributes_gender"],
    "text_weightage": [0.2, 0.2, 0.2, 0.2, 0.2],
    "text_image_weightage": [0.1, 0.9],
    "number_recommendations": 50,
    "collection_name": "similar_product_generalize_img_emd",
    "mongo_url": "",
    "mongo_db": "fynd",
    "both_embeddings": False,
    "text_embeddings": True
}


img_dict = {
    "path": "gs://cml-datalake-dev/fynd_latest.json",
    "row": ["uid", "slug", "brand_name", "category_name", "attributes_gender", "attributes_name", "attributes_color", "medias"],
    "multiple_media": True,
    "max_workers": 15,
    "clip_model": "ViT-B/32",
    "token_word": 77,
    "text_row": ["brand_name", "category_name", "attributes_gender", "attributes_name", "attributes_color"],
    "req_row": ["uid", "slug"],
    "similarity_fields": ["category_name", "attributes_gender"],
    "text_weightage": [0.2, 0.2, 0.2, 0.2, 0.2],
    "text_image_weightage": [0.1, 0.9],
    "number_recommendations": 50,
    "collection_name": "similar_product_generalize_img_emd",
    "mongo_url": "",
    "mongo_db": "fynd",
    "both_embeddings": False,
    "text_embeddings": False
}

both_dict = {
    "path": "gs://cml-datalake-dev/fynd_latest.json",
    "row": ["uid", "slug", "brand_name", "category_name", "attributes_gender", "attributes_name", "attributes_color", "medias"],
    "multiple_media": True,
    "max_workers": 15,
    "clip_model": "ViT-B/32",
    "token_word": 77,
    "text_row": ["brand_name", "category_name", "attributes_gender", "attributes_name", "attributes_color"],
    "req_row": ["uid", "slug"],
    "similarity_fields": ["category_name", "attributes_gender"],
    "text_weightage": [0.2, 0.2, 0.2, 0.2, 0.2],
    "text_image_weightage": [0.1, 0.9],
    "number_recommendations": 50,
    "collection_name": "similar_product_generalize_img_emd",
    "mongo_url": "",
    "mongo_db": "fynd",
    "both_embeddings": True,
    "text_embeddings": False
}


st.set_page_config(page_title="My App", page_icon=":guardsman:", layout="wide", initial_sidebar_state="auto")
st.header("Similar Recommendations")
uid_list = list(uid_name_map)
uid_name_list = get_mapped_values(uid_list, uid_name_map)
st.subheader("Choose a Product")
index = st.selectbox("Product List", range(len(uid_name_list)), format_func=lambda x: uid_name_list[x])
query_id = uid_list[index]
print(query_id)
print()
query_url = uid_media_map[query_id]
st.image(query_url, width=200)
for val in text_rec:
    if val["product_id"] == query_id:
        text_rec_list = val["recommendations"]
        print(text_rec_list)
        if text_rec_list:
            text_rec_url = []
            text_rec_name = []
            for val in text_rec_list:
                text_rec_url.append(uid_media_map[val["product_id"]])
                text_rec_name.append(uid_name_map[val["product_id"]])
            streamlit_carousel("Text Recommendations", text_rec_url, text_rec_name, text_dict)
        else:
            st.write("No text recommendations found")
for val in img_rec:
    if val["product_id"] == query_id:
        img_rec_list = val["recommendations"]
        if img_rec_list:
            img_rec_url = []
            img_rec_name = []
            for val in img_rec_list:
                img_rec_url.append(uid_media_map[val["product_id"]])
                img_rec_name.append(uid_name_map[val["product_id"]])
            streamlit_carousel("Image Recommendations", img_rec_url, img_rec_name, img_dict)
        else:
            st.write("No both recommendations found")
for val in both_rec:
    if val["product_id"] == query_id:
        both_rec_list = val["recommendations"]
        if both_rec_list:
            both_rec_url = []
            both_rec_name = []
            for val in both_rec_list:
                both_rec_url.append(uid_media_map[val["product_id"]])
                both_rec_name.append(uid_name_map[val["product_id"]])
            streamlit_carousel("Both Recommendations 0.1 Text 0.9 Image Weightage", both_rec_url, both_rec_name, both_dict)
        else:
            st.write("No both recommendations found")