Delete app_v0.py

app_v0.py

@@ -1,113 +0,0 @@
-# """
-# Author: Amir Hossein Kargaran
-# Date: August, 2023
-
-# Description: This code applies LIME (Local Interpretable Model-Agnostic Explanations) on fasttext language identification.
-
-# MIT License
-
-# Some part of the code is adopted from here: https://gist.github.com/ageitgey/60a8b556a9047a4ca91d6034376e5980
-# """
-
-import gradio as gr
-from io import BytesIO
-import base64
-from fasttext.FastText import _FastText
-import re
-import lime.lime_text
-import numpy as np
-from pathlib import Path
-from huggingface_hub import hf_hub_download
-
-# Load the FastText language identification model from Hugging Face Hub
-model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin")
-
-# Create the FastText classifier
-classifier = _FastText(model_path)
-
-def remove_label_prefix(item):
-    """
-    Remove label prefix from an item
-    """
-    return item.replace('__label__', '')
-
-def remove_label_prefix_list(input_list):
-    """
-    Remove label prefix from list or list of list
-    """
-    if isinstance(input_list[0], list):
-        # If the first element is a list, it's a list of lists
-        return [[remove_label_prefix(item) for item in inner_list] for inner_list in input_list]
-    else:
-        # Otherwise, it's a simple list
-        return [remove_label_prefix(item) for item in input_list]
-
-
-# Get the sorted class names from the classifier
-class_names = remove_label_prefix_list(classifier.labels)
-class_names = np.sort(class_names)
-num_class = len(class_names)
-
-
-def tokenize_string(string):
-    """
-    Splits the string into words similar to FastText's method.
-    """
-    return string.split()
-
-explainer = lime.lime_text.LimeTextExplainer(
-    split_expression=tokenize_string,
-    bow=False,
-    class_names=class_names
-)
-
-def fasttext_prediction_in_sklearn_format(classifier, texts):
-    """
-    Converts FastText predictions into Scikit-Learn format predictions.
-    """
-    res = []
-    labels, probabilities = classifier.predict(texts, num_class)
-    
-    # Remove label prefix
-    labels = remove_label_prefix_list(labels)
-    
-    for label, probs, text in zip(labels, probabilities, texts):
-        order = np.argsort(np.array(label))
-        res.append(probs[order])
-
-    return np.array(res)
-
-def generate_explanation_html(input_sentence):
-    """
-    Generates an explanation HTML file using LIME for the input sentence.
-    """
-    preprocessed_sentence = input_sentence  # No need to preprocess anymore
-    exp = explainer.explain_instance(
-        preprocessed_sentence,
-        classifier_fn=lambda x: fasttext_prediction_in_sklearn_format(classifier, x),
-        top_labels=2,
-        num_features=20,
-    )
-
-    output_html_filename = "explanation.html"
-    exp.save_to_file(output_html_filename)
-
-    return output_html_filename
-
-def download_html_file(html_filename):
-    """
-    Downloads the content of the given HTML file.
-    """
-    with open(html_filename, "rb") as file:
-        html_content = file.read()
-    return html_content
-
-input_sentence = gr.inputs.Textbox(label="Input Sentence")  # Change the label if needed
-output_explanation = gr.outputs.File(label="Download Explanation HTML")
-
-gr.Interface(
-    fn=generate_explanation_html,
-    inputs=input_sentence,
-    outputs=output_explanation,
-    allow_flagging='never'
-).launch()