import datetime
import os
import pathlib
import requests
import zipfile
import pandas as pd
import pydeck as pdk
import geopandas as gpd
import streamlit as st
import leafmap.colormaps as cm
from leafmap.common import hex_to_rgb
import time
from infer import USPPPMModel, USPPPMDataset
import torch
import pandas as pd

@st.cache_resource
def load_model():
    model = USPPPMModel('microsoft/deberta-v3-small')
    model.load_state_dict(torch.load('model_weights.pth', map_location=torch.device('cpu')))
    model.eval()
    ds = USPPPMDataset(model.tokenizer, 133)
    return model, ds

def infer(anchor, target, title):
    model, ds = load_model()
    d = {
        'anchor': anchor,
        'target': target,
        'title': title,
        'label': 0
    }
    
    x = ds[d][0]
    with torch.no_grad():
        y = model(x)
        
    return y.cpu().numpy()[0][0]

@st.cache_data
def get_context():
    df = pd.read_csv('./fold-0-train.csv')
    l = list(set(list(df['title'].values)))
    return l
    
    


st.set_page_config(
    page_title="PatentMatch",
    page_icon="🧊",
    layout="centered",
    initial_sidebar_state="expanded",
)



# fix sidebar
st.markdown("""
    <style>
        .css-vk3wp9 {
            background-color: rgb(255 255 255);
            }
        .css-18l0hbk {
            padding: 0.34rem 1.2rem !important;
            margin: 0.125rem 2rem;
            }
        .css-nziaof {
            padding: 0.34rem 1.2rem !important;
            margin: 0.125rem 2rem;
            background-color: rgb(181 197 227 / 18%) !important;
            }
    </style>
    """, unsafe_allow_html=True
)
hide_st_style = """
            <style>
            #MainMenu {visibility: hidden;}
            footer {visibility: hidden;}
            header {visibility: hidden;}
            </style>
            """
st.markdown(hide_st_style, unsafe_allow_html=True)



def app():

    st.title("PatentMatch: Patent Semantic Similarity Matcher")
    #st.markdown("[![View in W&B](https://img.shields.io/badge/View%20in-W%26B-blue)](https://wandb.ai/<username>/<project_name>?workspace=user-<username>)")
    
    st.markdown(
        """This project is focused on developing a Transformer based NLP model to match phrases 
        in U.S. patents based on their semantic similarity within a specific 
        technical domain context. The trained model achieved Pearson correlation coefficient score of 0.745.
        [[Source Code]](https://github.com/dataraptor/PatentMatch)
    """
    )

    st.markdown('---')
    # st.selectbox("Select from example", 
    #                          [
    #                             "Example 1",
    #                             "Example 2",
    #                              ])
    
    
    row1_col1, row1_col2, row1_col3 = st.columns(
        [0.5, 0.4, 0.4]
    )
    # with row1_col1:
    #     frequency = st.selectbox("Section", 
    #                              [
    #                                 "A: Human Necessities",
    #                                 "B: Operations and Transport",
    #                                 "C: Chemistry and Metallurgy",
    #                                 "D: Textiles",
    #                                 "E: Fixed Constructions",
    #                                 "F: Mechanical Engineering",
    #                                 "G: Physics",
    #                                 "H: Electricity",
    #                                 "Y: Emerging Cross-Sectional Technologies",
    #                                  ])
    # with row1_col2:
    #     class_box = st.selectbox("Class", 
    #                              [
    #                                 "21",
    #                                 "14",
    #                                 "23",
    #                                  ])
    
    with row1_col1:
        l = get_context()
        context = st.selectbox("Context", l, l.index('basic electric elements'))
        
       
    with row1_col2:
        anchor = st.text_input("Anchor", "deflect light")
    with row1_col3:
        target = st.text_input("Target", "bending moment")
    
    
    
    
    if st.button("Predict Scores", type="primary"):
        with st.spinner("Predicting scores..."):
            score = infer(anchor, target, context)
            ss = st.success("Scores predicted successfully!")
        
        score += 2.0
        fmt = "{:<.3f}".format(score)
        st.subheader(f"Similarity Score: {fmt}")
    
    
    
    
app()


# Display a footer with links and credits
st.markdown("---")
st.markdown("Built by [Shamim Ahamed](https://www.shamimahamed.com/). Data provided by [Kaggle](https://www.kaggle.com/competitions/us-patent-phrase-to-phrase-matching)")
#st.markdown("Data provided by [The Feedback Prize - ELLIPSE Corpus Scoring Challenge on Kaggle](https://www.kaggle.com/c/feedbackprize-ellipse-corpus-scoring-challenge)")