Update app.py

app.py

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+import numpy as np
+import pandas as pd
+import shap
+import streamlit as st
+import streamlit.components.v1 as components
+import torch
+from datasets import load_dataset
+from transformers import (AutoModelForCausalLM, AutoModelForQuestionAnswering,
+                          AutoModelForSeq2SeqLM,
+                          AutoModelForSequenceClassification, AutoTokenizer,
+                          pipeline)
+
+st.set_page_config(page_title="HF-SHAP")
+st.title("HF-SHAP: A front end for SHAP")
+st.caption("By Allen Roush")
+st.caption("github: https://github.com/Hellisotherpeople")
+st.caption("Linkedin: https://www.linkedin.com/in/allen-roush-27721011b/")
+st.title("SHAP (SHapley Additive exPlanations)")
+st.image("https://shap.readthedocs.io/en/latest/_images/shap_header.png", width = 700)
+st.caption("By Lundberg, Scott M and Lee, Su-In")
+st.caption("Full Citation: https://raw.githubusercontent.com/slundberg/shap/master/docs/references/shap_nips.bib")
+st.caption("See on github:: https://github.com/slundberg/shap")
+
+
+form = st.sidebar.form("Main Settings")
+
+form.header("Main Settings")
+
+
+
+task_done = form.selectbox("Which NLP task do you want to solve?", ["Text Generation", "Sentiment Analysis", "Translation", "Summarization"])
+
+
+
+
+
+custom_doc = form.checkbox("Use a document from an existing dataset?", value = False)
+if custom_doc:
+    dataset_name = form.text_area("Enter the name of the huggingface Dataset to do analysis of:", value = "Hellisotherpeople/DebateSum")
+    dataset_name_2 = form.text_area("Enter the name of the config for the dataset if it has one", value = "")
+    split_name = form.text_area("Enter the name of the split of the dataset that you want to use", value = "train")
+    number_of_records = form.number_input("Enter the number of documents that you want to analyze from the dataset", value = 200)
+    column_name = form.text_area("Enter the name of the column that we are doing analysis on (the X value)", value = "Full-Document")
+    index_to_analyze_start = form.number_input("Enter the index start of the document that you want to analyze of the dataset", value = 1)
+    index_to_analyze_end = form.number_input("Enter the index end of the document that you want to analyze of the dataset", value = 2)
+    form.caption("Multiple documents may not work on certain tasks")
+else:
+    doc = st.text_area("Enter a custom document", value = "This is an example custom document")
+
+
+
+if task_done == "Text Generation":
+    model_name = form.text_area("Enter the name of the pre-trained model from transformers that we are using for Text Generation", value = "gpt2")
+    form.caption("This will download a new model, so it may take awhile or even break if the model is too large")
+    decoder = form.checkbox("Is this a decoder model?", value = True)
+    form.caption("This should be true for models like GPT-2, and false for models like BERT")
+    max_length = form.number_input("What's the max length of the text?", value = 50)
+    min_length = form.number_input("What's the min length of the text?", value = 20, max_value = max_length)
+    penalize_repetion = form.number_input("How strongly do we want to penalize repetition in the text generation?", value = 2)
+    sample = form.checkbox("Shall we use top-k and top-p decoding?", value = True)
+    form.caption("Setting this to false makes it greedy")
+    if sample:
+        top_k = form.number_input("What value of K should we use for Top-K sampling? Set to zero to disable", value = 50)
+        form.caption("In Top-K sampling, the K most likely next words are filtered and the probability mass is redistributed among only those K next words. ")
+        top_p = form.number_input("What value of P should we use for Top-p sampling? Set to zero to disable", value = 0.95, max_value = 1.0, min_value = 0.0)
+        form.caption("Top-p sampling chooses from the smallest possible set of words whose cumulative probability exceeds the probability p. The probability mass is then redistributed among this set of words.")
+        temperature = form.number_input("How spicy/interesting do we want our models output to be", value = 1.05, min_value = 0.0)
+        form.caption("Setting this higher decreases the likelihood of high probability words and increases the likelihood of low probability (and presumably more interesting) words")
+        form.caption("For more details on what these settings mean, see here: https://huggingface.co/blog/how-to-generate")
+
+
+elif task_done == "Sentiment Analysis":
+    model_name = form.text_area("Enter the name of the pre-trained model from transformers that we are using for Sentiment Analysis", value = "nateraw/bert-base-uncased-emotion")
+    rescale_logits = form.checkbox("Do we rescale the probabilities in terms of log odds?", value = False)
+elif task_done == "Translation":
+    model_name = form.text_area("Enter the name of the pre-trained model from transformers that we are using for Translation", value = "Helsinki-NLP/opus-mt-en-es")
+elif task_done == "Summarization":
+    model_name = form.text_area("Enter the name of the pre-trained model from transformers that we are using for Translation", value = "sshleifer/distilbart-xsum-12-6")
+else:
+    model_name = form.text_area("Enter the name of the pre-trained model from transformers that we are using for Question Answering", value = "deepset/roberta-base-squad2")
+
+form.header("Model Explanation Display Settings")
+output_width = form.number_input("Enter the number of pixels for width of model explanation html display", value = 800)
+output_height = form.number_input("Enter the number of pixels for height of model explanation html display", value = 2000)
+form.form_submit_button("Submit")
+
+@st.cache
+def load_and_process_data(path, name, streaming, split_name, number_of_records):
+    dataset = load_dataset(path = path, name = name, streaming=streaming)
+    #return list(dataset)
+    dataset_head = dataset[split_name].take(number_of_records)
+    df = pd.DataFrame.from_dict(dataset_head)
+    return df[column_name]
+
+
+
+@st.cache(allow_output_mutation=True)
+def load_model(model_name):
+    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+    if task_done == "Text Generation":
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        model.config.is_decoder=decoder
+        if sample == True:
+            model.config.task_specific_params["text-generation"] = {"do_sample": sample, "max_length": max_length, "min_length": min_length, "temperature": temperature, "top_k": top_k, "top_p" : top_p, "no_repeat_ngram_size": penalize_repetion}
+        else:
+            model.config.task_specific_params["text-generation"] = {"do_sample": sample, "max_length": max_length, "min_length": min_length, "no_repeat_ngram_size": penalize_repetion}
+    
+    elif task_done == "Sentiment Analysis":
+        model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    elif task_done == "Translation":
+        model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    elif task_done == "Summarization":
+        model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    elif task_done == "Question Answering":
+        #TODO: This one is going to be harder... 
+        # https://shap.readthedocs.io/en/latest/example_notebooks/text_examples/question_answering/Explaining%20a%20Question%20Answering%20Transformers%20Model.html
+        model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+
+    return tokenizer, model
+
+tokenizer, model = load_model(model_name)
+
+
+
+
+
+
+if custom_doc:
+    df = load_and_process_data(dataset_name, dataset_name_2, True, split_name, number_of_records)
+    doc = list(df[index_to_analyze_start:index_to_analyze_end])
+    st.write(doc)
+
+if task_done == "Sentiment Analysis":
+    pred = pipeline("text-classification", model=model, tokenizer=tokenizer, return_all_scores=True)
+    explainer = shap.Explainer(pred, rescale_to_logits = rescale_logits)
+else:
+    explainer = shap.Explainer(model, tokenizer)
+
+if custom_doc:
+    shap_values = explainer(doc)
+else:
+    shap_values = explainer([doc])
+
+
+###REMEMBER YOU FIXED THE CODE IN SHAP, YOU NEED TO FORK IT
+
+the_plot = shap.plots.text(shap_values, display = False)
+components.html(the_plot, height = output_width, width = output_height)
+st.caption("Scroll to see the full output!")