import pandas as pd
import streamlit as st
from infer import USPPPMModel, USPPPMDataset
import torch

@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)")