app.py

import torch
import transformers
from transformers import AutoModelForCausalLM
import pandas as pd
import gradio as gr

# Build model & get some layers
tokenizer = transformers.AutoTokenizer.from_pretrained('gpt2')
m = AutoModelForCausalLM.from_pretrained("lora-x/backpack-gpt2", trust_remote_code=True)
m.eval()

lm_head = m.get_lm_head() # (V, d)
word_embeddings = m.backpack.get_word_embeddings() # (V, d)
sense_network = m.backpack.get_sense_network() # (V, nv, d)
num_senses = m.backpack.get_num_senses()
sense_names = [i for i in range(num_senses)]

"""
Single token sense lookup
"""
def visualize_word(word, count=10, remove_space=False):

    if not remove_space:
        word = ' ' + word
    print(f"Looking up word '{word}'...")

    token_ids = tokenizer(word)['input_ids']
    tokens = [tokenizer.decode(token_id) for token_id in token_ids]
    tokens = ", ".join(tokens) # display tokenization for user
    print(f"Tokenized as: {tokens}")
    # look up sense vectors only for the first token
    # contents = vecs[token_ids[0]] # torch.Size([16, 768])
    sense_input_embeds = word_embeddings(torch.tensor([token_ids[0]]).long().unsqueeze(0)) # (bs=1, s=1, d), sense_network expects bs dim
    senses = sense_network(sense_input_embeds) # -> (bs=1, nv, s=1, d) 
    senses = torch.squeeze(senses) # (nv, s=1, d)

    # for pos and neg respectively, create a list (for each sense) of list (top k) of tuples (word, logit)
    pos_word_lists = [] 
    neg_word_lists = []
    sense_names = [] # column header
    for i in range(senses.shape[0]):
        logits = lm_head(senses[i,:])
        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
        sense_names.append('sense {}'.format(i))

        pos_sorted_words = [tokenizer.decode(sorted_indices[j]) for j in range(count)]
        pos_sorted_logits = [sorted_logits[j].item() for j in range(count)]
        pos_word_lists.append(list(zip(pos_sorted_words, pos_sorted_logits)))

        neg_sorted_words = [tokenizer.decode(sorted_indices[-j-1]) for j in range(count)]
        neg_sorted_logits = [sorted_logits[-j-1].item() for j in range(count)]
        neg_word_lists.append(list(zip(neg_sorted_words, neg_sorted_logits)))

    def create_dataframe(word_lists, sense_names, count):
        data = dict(zip(sense_names, word_lists))
        df = pd.DataFrame(index=[i for i in range(count)],
                        columns=list(data.keys()))
        for prop, word_list in data.items():
            for i, word_pair in enumerate(word_list):
                cell_value = "space ({:.2f})".format(word_pair[1])
                cell_value = "{} ({:.2f})".format(word_pair[0], word_pair[1])
                df.at[i, prop] = cell_value
        return df
    
    pos_df = create_dataframe(pos_word_lists, sense_names, count)
    neg_df = create_dataframe(neg_word_lists, sense_names, count)

    return pos_df, neg_df, tokens

"""
Returns:
    - tokens: the tokenization of the input sentence, also used as options to choose from for get_token_contextual_weights
    - top_k_words_df: a dataframe of the top k words predicted by the model
    - length: of the input sentence, stored as a gr.State variable so other methods can find the 
        contextualization weights for the *last* token that's needed
    - contextualization_weights: gr.State variable, stores the contextualization weights for the input sentence
"""
def predict_next_word (sentence, top_k = 5, contextualization_weights = None):  
    
    if sentence == "":
        return None, None, None, None

    # For better tokenization, by default, adds a space at the beginning of the sentence if it doesn't already have one
    # and remove trailing space
    sentence = sentence.strip()
    if sentence[0] != ' ':
        sentence = ' ' + sentence
    print(f"Sentence: '{sentence}'")

    # Make input, keeping track of original length
    token_ids = tokenizer(sentence)['input_ids']
    tokens = [[tokenizer.decode(token_id) for token_id in token_ids]] # a list of a single list because used as dataframe
    length = len(token_ids)
    inp = torch.zeros((1,512)).long()
    inp[0,:length] = torch.tensor(token_ids).long()

    # Get output at correct index
    if contextualization_weights is None:
        print("contextualization_weights IS None, freshly computing contextualization_weights")
        output = m(inp)
        logits, contextualization_weights = output.logits[0,length-1,:], output.contextualization
        # Store contextualization weights and return it as a gr.State var for use by get_token_contextual_weights
    else:
        print("contextualization_weights is NOT None, using passed in contextualization_weights")
        output = m.run_with_custom_contextualization(inp, contextualization_weights)
        logits = output.logits[0,length-1,:]
    probs = logits.softmax(dim=-1) # probs over next word
    probs, indices = torch.sort(probs, descending=True)
    top_k_words = [(tokenizer.decode(indices[i]), round(probs[i].item(), 3)) for i in range(top_k)]
    top_k_words_df = pd.DataFrame(top_k_words, columns=['word', 'probability'], index=range(1, top_k+1))

    top_k_words_df = top_k_words_df.T

    print(top_k_words_df)

    return tokens, top_k_words_df, length, contextualization_weights


"""
Returns a dataframe of senses with weights for the selected token.

Args:
    contextualization_weights: a gr.State variable that stores the contextualization weights for the input sentence.
    length: length of the input sentence, used to get the contextualization weights for the last token
    token: the selected token
    token_index: the index of the selected token in the input sentence
    pos_count: how many top positive words to display for each sense
    neg_count: how many top negative words to display for each sense
"""
def get_token_contextual_weights (contextualization_weights, length, token, token_index, pos_count = 5, neg_count = 3):
    print(">>>>>in get_token_contextual_weights")
    print(f"Selected {token_index}th token: {token}")
    
    # get contextualization weights for the selected token
    # Only care about the weights for the last word, since that's what contributes to the output
    token_contextualization_weights = contextualization_weights[0, :, length-1, token_index] 
    token_contextualization_weights_list = [round(x, 3) for x in token_contextualization_weights.tolist()]

    # get sense vectors of the selected token
    token_ids = tokenizer(token)['input_ids'] # keep as a list bc sense_network expects s dim
    sense_input_embeds = word_embeddings(torch.tensor(token_ids).long().unsqueeze(0)) # (bs=1, s=1, d), sense_network expects bs dim
    senses = sense_network(sense_input_embeds) # -> (bs=1, nv, s=1, d) 
    senses = torch.squeeze(senses) # (nv, s=1, d)

    # build dataframe
    pos_dfs, neg_dfs = [], []

    for i in range(num_senses):
        logits = lm_head(senses[i,:]) # (vocab,) [768, 50257] -> [50257]
        sorted_logits, sorted_indices = torch.sort(logits, descending=True)

        pos_sorted_words = [tokenizer.decode(sorted_indices[j]) for j in range(pos_count)]
        pos_df = pd.DataFrame(pos_sorted_words, columns=["Sense {}".format(i)])
        pos_dfs.append(pos_df)

        neg_sorted_words = [tokenizer.decode(sorted_indices[-j-1]) for j in range(neg_count)]
        neg_df = pd.DataFrame(neg_sorted_words, columns=["Top Negative"])
        neg_dfs.append(neg_df)

    sense0words, sense1words, sense2words, sense3words, sense4words, sense5words, \
        sense6words, sense7words, sense8words, sense9words, sense10words, sense11words, \
            sense12words, sense13words, sense14words, sense15words = pos_dfs
    
    sense0negwords, sense1negwords, sense2negwords, sense3negwords, sense4negwords, sense5negwords, \
        sense6negwords, sense7negwords, sense8negwords, sense9negwords, sense10negwords, sense11negwords, \
            sense12negwords, sense13negwords, sense14negwords, sense15negwords = neg_dfs
    
    sense0slider, sense1slider, sense2slider, sense3slider, sense4slider, sense5slider, \
        sense6slider, sense7slider, sense8slider, sense9slider, sense10slider, sense11slider, \
            sense12slider, sense13slider, sense14slider, sense15slider = token_contextualization_weights_list
    
    return token, token_index, \
        sense0words, sense1words, sense2words, sense3words, sense4words, sense5words, sense6words, sense7words, \
        sense8words, sense9words, sense10words, sense11words, sense12words, sense13words, sense14words, sense15words, \
        sense0negwords, sense1negwords, sense2negwords, sense3negwords, sense4negwords, sense5negwords, sense6negwords, sense7negwords, \
        sense8negwords, sense9negwords, sense10negwords, sense11negwords, sense12negwords, sense13negwords, sense14negwords, sense15negwords, \
        sense0slider, sense1slider, sense2slider, sense3slider, sense4slider, sense5slider, sense6slider, sense7slider, \
        sense8slider, sense9slider, sense10slider, sense11slider, sense12slider, sense13slider, sense14slider, sense15slider

"""
Wrapper for when the user selects a new token in the tokens dataframe. 
Converts `evt` (the selected token) to `token` and `token_index` which are used by get_token_contextual_weights.
"""
def new_token_contextual_weights (contextualization_weights, length, evt: gr.SelectData, pos_count = 5, neg_count = 3):
    print(">>>>>in new_token_contextual_weights")
    token_index = evt.index[1] # selected token is the token_index-th token in the sentence
    token = evt.value
    if not token:
        return None, None, \
            None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, \
            None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, \
            None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None
    return get_token_contextual_weights (contextualization_weights, length, token, token_index, pos_count, neg_count)

def change_sense0_weight(contextualization_weights, length, token_index, new_weight):
    contextualization_weights[0, 0, length-1, token_index] = new_weight
    return contextualization_weights
def change_sense1_weight(contextualization_weights, length, token_index, new_weight):
    contextualization_weights[0, 1, length-1, token_index] = new_weight
    return contextualization_weights
def change_sense2_weight(contextualization_weights, length, token_index, new_weight):
    contextualization_weights[0, 2, length-1, token_index] = new_weight
    return contextualization_weights
def change_sense3_weight(contextualization_weights, length, token_index, new_weight):
    contextualization_weights[0, 3, length-1, token_index] = new_weight
    return contextualization_weights
def change_sense4_weight(contextualization_weights, length, token_index, new_weight):
    contextualization_weights[0, 4, length-1, token_index] = new_weight
    return contextualization_weights
def change_sense5_weight(contextualization_weights, length, token_index, new_weight):
    contextualization_weights[0, 5, length-1, token_index] = new_weight
    return contextualization_weights
def change_sense6_weight(contextualization_weights, length, token_index, new_weight):
    contextualization_weights[0, 6, length-1, token_index] = new_weight
    return contextualization_weights
def change_sense7_weight(contextualization_weights, length, token_index, new_weight):
    contextualization_weights[0, 7, length-1, token_index] = new_weight
    return contextualization_weights
def change_sense8_weight(contextualization_weights, length, token_index, new_weight):
    contextualization_weights[0, 8, length-1, token_index] = new_weight
    return contextualization_weights
def change_sense9_weight(contextualization_weights, length, token_index, new_weight):
    contextualization_weights[0, 9, length-1, token_index] = new_weight
    return contextualization_weights
def change_sense10_weight(contextualization_weights, length, token_index, new_weight):
    contextualization_weights[0, 10, length-1, token_index] = new_weight
    return contextualization_weights
def change_sense11_weight(contextualization_weights, length, token_index, new_weight):
    contextualization_weights[0, 11, length-1, token_index] = new_weight
    return contextualization_weights
def change_sense12_weight(contextualization_weights, length, token_index, new_weight):
    contextualization_weights[0, 12, length-1, token_index] = new_weight
    return contextualization_weights
def change_sense13_weight(contextualization_weights, length, token_index, new_weight):
    contextualization_weights[0, 13, length-1, token_index] = new_weight
    return contextualization_weights
def change_sense14_weight(contextualization_weights, length, token_index, new_weight):
    contextualization_weights[0, 14, length-1, token_index] = new_weight
    return contextualization_weights
def change_sense15_weight(contextualization_weights, length, token_index, new_weight):
    contextualization_weights[0, 15, length-1, token_index] = new_weight
    return contextualization_weights

"""
Clears all gr.State variables used to store info across methods when the input sentence changes.
"""
def clear_states(contextualization_weights, token_index, length):
    contextualization_weights = None
    token_index = None
    length = 0
    return contextualization_weights, token_index, length

def reset_weights(contextualization_weights):
    print("Resetting weights...")
    contextualization_weights = None
    return contextualization_weights

with gr.Blocks( theme = gr.themes.Base(), 
                css = """#sense0slider, #sense1slider, #sense2slider, #sense3slider, #sense4slider, #sense5slider, #sense6slider, #sense7slider,
                        #sense8slider, #sense9slider, #sense1slider0, #sense11slider, #sense12slider, #sense13slider, #sense14slider, #sense15slider 
                        { height: 200px; width: 200px;  transform: rotate(270deg); }""" 
                ) as demo:
    
    gr.Markdown("""
    ## Backpack Sense Visualization
    """)

    with gr.Tab("Language Modeling"):
        contextualization_weights = gr.State(None) # store session data for sharing between functions
        token_index = gr.State(None)
        length = gr.State(0)
        top_k = gr.State(10)
        with gr.Row():
            with gr.Column(scale=8):
                input_sentence = gr.Textbox(label="Input Sentence", placeholder='Enter a sentence and click "Predict next word". Then, you can go to the Tokens section, click on a token, and see its contextualization weights.')
            with gr.Column(scale=1):
                predict = gr.Button(value="Predict next word", variant="primary")
                reset_weights_button = gr.Button("Reset weights")
        gr.Markdown("""#### Top-k predicted next word""")
        top_k_words = gr.Dataframe(interactive=False)
        gr.Markdown("""### **Token Breakdown:** click on a token below to see its senses and contextualization weights""")
        tokens = gr.DataFrame()
        with gr.Row():
            with gr.Column(scale=1):
                selected_token = gr.Textbox(label="Current Selected Token", interactive=False)
            with gr.Column(scale=8):
                gr.Markdown("""####
                Once a token is chosen, you can **use the sliders below to change the weight of any sense or multiple senses** for that token, \
                    and then click "Predict next word" to see updated next-word predictions. Erase all changes with "Reset weights".
                """)
        # sense sliders and top sense words dataframes
        with gr.Row():
            with gr.Column(scale=0, min_width=120):
                sense0slider= gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Sense 0", elem_id="sense0slider", interactive=True)
            with gr.Column(scale=0, min_width=120):
                sense1slider= gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Sense 1", elem_id="sense1slider", interactive=True)
            with gr.Column(scale=0, min_width=120):
                sense2slider= gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Sense 2", elem_id="sense2slider", interactive=True)
            with gr.Column(scale=0, min_width=120):
                sense3slider= gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Sense 3", elem_id="sense3slider", interactive=True)
            with gr.Column(scale=0, min_width=120):
                sense4slider= gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Sense 4", elem_id="sense4slider", interactive=True)
            with gr.Column(scale=0, min_width=120):
                sense5slider= gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Sense 5", elem_id="sense5slider", interactive=True)
            with gr.Column(scale=0, min_width=120):
                sense6slider= gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Sense 6", elem_id="sense6slider", interactive=True)
            with gr.Column(scale=0, min_width=120):
                sense7slider= gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Sense 7", elem_id="sense7slider", interactive=True)
        with gr.Row():
            with gr.Column(scale=0, min_width=120):
                sense0words = gr.DataFrame(headers = ["Sense 0"])
            with gr.Column(scale=0, min_width=120):
                sense1words = gr.DataFrame(headers = ["Sense 1"])
            with gr.Column(scale=0, min_width=120):
                sense2words = gr.DataFrame(headers = ["Sense 2"])
            with gr.Column(scale=0, min_width=120):
                sense3words = gr.DataFrame(headers = ["Sense 3"])
            with gr.Column(scale=0, min_width=120):
                sense4words = gr.DataFrame(headers = ["Sense 4"])
            with gr.Column(scale=0, min_width=120):
                sense5words = gr.DataFrame(headers = ["Sense 5"])
            with gr.Column(scale=0, min_width=120):
                sense6words = gr.DataFrame(headers = ["Sense 6"])
            with gr.Column(scale=0, min_width=120):
                sense7words = gr.DataFrame(headers = ["Sense 7"])
        with gr.Row():
            with gr.Column(scale=0, min_width=120):
                sense0negwords = gr.DataFrame(headers = ["Top Negative"])
            with gr.Column(scale=0, min_width=120):
                sense1negwords = gr.DataFrame(headers = ["Top Negative"])
            with gr.Column(scale=0, min_width=120):
                sense2negwords = gr.DataFrame(headers = ["Top Negative"])
            with gr.Column(scale=0, min_width=120):
                sense3negwords = gr.DataFrame(headers = ["Top Negative"])
            with gr.Column(scale=0, min_width=120):
                sense4negwords = gr.DataFrame(headers = ["Top Negative"])
            with gr.Column(scale=0, min_width=120):
                sense5negwords = gr.DataFrame(headers = ["Top Negative"])
            with gr.Column(scale=0, min_width=120):
                sense6negwords = gr.DataFrame(headers = ["Top Negative"])
            with gr.Column(scale=0, min_width=120):
                sense7negwords = gr.DataFrame(headers = ["Top Negative"])
        with gr.Row():
            with gr.Column(scale=0, min_width=120):
                sense8slider= gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Sense 8", elem_id="sense8slider", interactive=True)
            with gr.Column(scale=0, min_width=120):
                sense9slider= gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Sense 9", elem_id="sense9slider", interactive=True)
            with gr.Column(scale=0, min_width=120):
                sense10slider= gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Sense 10", elem_id="sense1slider0", interactive=True)
            with gr.Column(scale=0, min_width=120):
                sense11slider= gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Sense 11", elem_id="sense11slider", interactive=True)
            with gr.Column(scale=0, min_width=120):
                sense12slider= gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Sense 12", elem_id="sense12slider", interactive=True)
            with gr.Column(scale=0, min_width=120):
                sense13slider= gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Sense 13", elem_id="sense13slider", interactive=True)
            with gr.Column(scale=0, min_width=120):
                sense14slider= gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Sense 14", elem_id="sense14slider", interactive=True)
            with gr.Column(scale=0, min_width=120):
                sense15slider= gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Sense 15", elem_id="sense15slider", interactive=True)
        with gr.Row():
            with gr.Column(scale=0, min_width=120):
                sense8words = gr.DataFrame(headers = ["Sense 8"])
            with gr.Column(scale=0, min_width=120):
                sense9words = gr.DataFrame(headers = ["Sense 9"])
            with gr.Column(scale=0, min_width=120):
                sense10words = gr.DataFrame(headers = ["Sense 10"])
            with gr.Column(scale=0, min_width=120):
                sense11words = gr.DataFrame(headers = ["Sense 11"])
            with gr.Column(scale=0, min_width=120):
                sense12words = gr.DataFrame(headers = ["Sense 12"])
            with gr.Column(scale=0, min_width=120):
                sense13words = gr.DataFrame(headers = ["Sense 13"])
            with gr.Column(scale=0, min_width=120):
                sense14words = gr.DataFrame(headers = ["Sense 14"])
            with gr.Column(scale=0, min_width=120):
                sense15words = gr.DataFrame(headers = ["Sense 15"])
        with gr.Row():
            with gr.Column(scale=0, min_width=120):
                sense8negwords = gr.DataFrame(headers = ["Top Negative"])
            with gr.Column(scale=0, min_width=120):
                sense9negwords = gr.DataFrame(headers = ["Top Negative"])
            with gr.Column(scale=0, min_width=120):
                sense10negwords = gr.DataFrame(headers = ["Top Negative"])
            with gr.Column(scale=0, min_width=120):
                sense11negwords = gr.DataFrame(headers = ["Top Negative"])
            with gr.Column(scale=0, min_width=120):
                sense12negwords = gr.DataFrame(headers = ["Top Negative"])
            with gr.Column(scale=0, min_width=120):
                sense13negwords = gr.DataFrame(headers = ["Top Negative"])
            with gr.Column(scale=0, min_width=120):
                sense14negwords = gr.DataFrame(headers = ["Top Negative"])
            with gr.Column(scale=0, min_width=120):
                sense15negwords = gr.DataFrame(headers = ["Top Negative"])
        gr.Markdown("""Note: **"Top Negative"** shows words that have the most negative dot products with the sense vector, which can exhibit more coherent meaning than those with the most positive dot products.  
                        To see more representative words of each sense, scroll to the top and use the **"Individual Word Sense Look Up"** tab.""")
        # gr.Examples(
        #     examples=[["Messi plays for", top_k, None]],
        #     inputs=[input_sentence, top_k, contextualization_weights],
        #     outputs=[tokens, top_k_words, length, contextualization_weights],
        #     fn=predict_next_word,
        # )

        sense0slider.change(fn=change_sense0_weight,
                            inputs=[contextualization_weights, length, token_index, sense0slider],
                            outputs=[contextualization_weights])
        sense1slider.change(fn=change_sense1_weight,
                            inputs=[contextualization_weights, length, token_index, sense1slider],
                            outputs=[contextualization_weights])
        sense2slider.change(fn=change_sense2_weight,
                            inputs=[contextualization_weights, length, token_index, sense2slider],
                            outputs=[contextualization_weights])
        sense3slider.change(fn=change_sense3_weight,
                            inputs=[contextualization_weights, length, token_index, sense3slider],
                            outputs=[contextualization_weights])
        sense4slider.change(fn=change_sense4_weight,
                            inputs=[contextualization_weights, length, token_index, sense4slider],
                            outputs=[contextualization_weights])
        sense5slider.change(fn=change_sense5_weight,
                            inputs=[contextualization_weights, length, token_index, sense5slider],
                            outputs=[contextualization_weights])
        sense6slider.change(fn=change_sense6_weight,
                            inputs=[contextualization_weights, length, token_index, sense6slider],
                            outputs=[contextualization_weights])
        sense7slider.change(fn=change_sense7_weight,
                            inputs=[contextualization_weights, length, token_index, sense7slider],
                            outputs=[contextualization_weights])
        sense8slider.change(fn=change_sense8_weight,
                            inputs=[contextualization_weights, length, token_index, sense8slider],
                            outputs=[contextualization_weights])
        sense9slider.change(fn=change_sense9_weight,
                            inputs=[contextualization_weights, length, token_index, sense9slider],
                            outputs=[contextualization_weights])
        sense10slider.change(fn=change_sense10_weight,
                            inputs=[contextualization_weights, length, token_index, sense10slider],
                            outputs=[contextualization_weights])
        sense11slider.change(fn=change_sense11_weight,
                            inputs=[contextualization_weights, length, token_index, sense11slider],
                            outputs=[contextualization_weights])
        sense12slider.change(fn=change_sense12_weight,
                            inputs=[contextualization_weights, length, token_index, sense12slider],
                            outputs=[contextualization_weights])
        sense13slider.change(fn=change_sense13_weight,
                            inputs=[contextualization_weights, length, token_index, sense13slider],
                            outputs=[contextualization_weights])
        sense14slider.change(fn=change_sense14_weight,
                            inputs=[contextualization_weights, length, token_index, sense14slider],
                            outputs=[contextualization_weights])
        sense15slider.change(fn=change_sense15_weight,
                            inputs=[contextualization_weights, length, token_index, sense15slider],
                            outputs=[contextualization_weights])
    
        
        predict.click(
            fn=predict_next_word,
            inputs = [input_sentence, top_k, contextualization_weights], 
            outputs= [tokens, top_k_words, length, contextualization_weights],
        )

        tokens.select(fn=new_token_contextual_weights, 
                    inputs=[contextualization_weights, length], 
                    outputs= [selected_token, token_index, 
                                
                                sense0words, sense1words, sense2words, sense3words, sense4words, sense5words, sense6words, sense7words, 
                                sense8words, sense9words, sense10words, sense11words, sense12words, sense13words, sense14words, sense15words,

                                sense0negwords, sense1negwords, sense2negwords, sense3negwords, sense4negwords, sense5negwords, sense6negwords, sense7negwords, 
                                sense8negwords, sense9negwords, sense10negwords, sense11negwords, sense12negwords, sense13negwords, sense14negwords, sense15negwords, 

                                sense0slider, sense1slider, sense2slider, sense3slider, sense4slider, sense5slider, sense6slider, sense7slider, 
                                sense8slider, sense9slider, sense10slider, sense11slider, sense12slider, sense13slider, sense14slider, sense15slider]
                                )

        reset_weights_button.click(
                fn=reset_weights,
                inputs=[contextualization_weights],
                outputs=[contextualization_weights]
            ).success(
                fn=predict_next_word,
                inputs = [input_sentence, top_k, contextualization_weights],
                outputs= [tokens, top_k_words, length, contextualization_weights],
            ).success(
                fn=get_token_contextual_weights,
                inputs=[contextualization_weights, length, selected_token, token_index],
                outputs= [selected_token, token_index, 
                        
                        sense0words, sense1words, sense2words, sense3words, sense4words, sense5words, sense6words, sense7words, 
                        sense8words, sense9words, sense10words, sense11words, sense12words, sense13words, sense14words, sense15words,
                        
                        sense0negwords, sense1negwords, sense2negwords, sense3negwords, sense4negwords, sense5negwords, sense6negwords, sense7negwords, 
                        sense8negwords, sense9negwords, sense10negwords, sense11negwords, sense12negwords, sense13negwords, sense14negwords, sense15negwords, 

                        sense0slider, sense1slider, sense2slider, sense3slider, sense4slider, sense5slider, sense6slider, sense7slider, 
                        sense8slider, sense9slider, sense10slider, sense11slider, sense12slider, sense13slider, sense14slider, sense15slider]
                        )
        
        input_sentence.change(
            fn=clear_states,
            inputs=[contextualization_weights, token_index, length],
            outputs=[contextualization_weights, token_index, length]
        )

    with gr.Tab("Individual Word Sense Look Up"):
        gr.Markdown("""> Note on tokenization: Backpack uses the GPT-2 tokenizer, which includes the space before a word as part \
            of the token, so by default, a space character `' '` is added to the beginning of the word \
                you look up. You can disable this by checking `Remove space before word`, but know this might \
                    cause strange behaviors like breaking `afraid` into `af` and `raid`, or `slight` into `s` and `light`.
        """)
        with gr.Row():
            word = gr.Textbox(label="Word", placeholder="e.g. science")
            token_breakdown = gr.Textbox(label="Token Breakdown (senses are for the first token only)")
            remove_space = gr.Checkbox(label="Remove space before word", default=False)
            count = gr.Slider(minimum=1, maximum=20, value=10, label="Top K", step=1)
        look_up_button = gr.Button("Look up")
        pos_outputs = gr.Dataframe(label="Highest Scoring Senses")
        neg_outputs = gr.Dataframe(label="Lowest Scoring Senses")
        gr.Examples(
        examples=["science", "afraid", "book", "slight"],
        inputs=[word],
        outputs=[pos_outputs, neg_outputs, token_breakdown],
        fn=visualize_word,
        cache_examples=True,
        )

        look_up_button.click(
            fn=visualize_word, 
            inputs= [word, count, remove_space],
            outputs= [pos_outputs, neg_outputs, token_breakdown],
        )

demo.launch()


# Code for generating slider functions & event listners

# for i in range(16):
#     print(
#         f"""def change_sense{i}_weight(contextualization_weights, length, token_index, new_weight):
#     print(f"Changing weight for the {i}th sense of the {{token_index}}th token.")
#     print("new_weight to be assigned = ", new_weight)
#     contextualization_weights[0, {i}, length-1, token_index] = new_weight
#     print("contextualization_weights: ", contextualization_weights[0, :, length-1, token_index])
#     return contextualization_weights"""
#     )

# for i in range(16):
#     print(
#         f"""  sense{i}slider.change(fn=change_sense{i}_weight,
#                     inputs=[contextualization_weights, length, token_index, sense{i}slider],
#                     outputs=[contextualization_weights])"""
#     )