add image output

app.py

@@ -11,13 +11,15 @@
 
 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 PIL import Image
 from huggingface_hub import hf_hub_download
+from selenium import webdriver
+from selenium.common.exceptions import WebDriverException
+import os
 
 # Load the FastText language identification model from Hugging Face Hub
 model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin")
@@ -26,33 +28,19 @@ model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification"
 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(
@@ -62,52 +50,65 @@ explainer = lime.lime_text.LimeTextExplainer(
 )
 
 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
+    preprocessed_sentence = input_sentence
     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
+def take_screenshot(local_html_path):
+    options = webdriver.ChromeOptions()
+    options.add_argument('--headless')
+    options.add_argument('--no-sandbox')
+    options.add_argument('--disable-dev-shm-usage')
+
+    try:
+        local_html_path = os.path.abspath(local_html_path)
+        wd = webdriver.Chrome(options=options)
+        wd.set_window_size(1366, 728)
+        wd.get('file://' + local_html_path)
+        wd.implicitly_wait(10)
+        screenshot = wd.get_screenshot_as_png()
+    except WebDriverException as e:
+        return Image.new('RGB', (1, 1))
+    finally:
+        if wd:
+            wd.quit()
+
+    return Image.open(BytesIO(screenshot))
+
+def merge(input_sentence):
+    input_sentence = input_sentence.replace('\n', ' ')
+    output_html_filename = generate_explanation_html(input_sentence)
+    im = take_screenshot(output_html_filename)
+    
+    return im, output_html_filename
+
+input_sentence = gr.inputs.Textbox(label="Input Sentence")
 
-input_sentence = gr.inputs.Textbox(label="Input Sentence")  # Change the label if needed
-output_explanation = gr.outputs.File(label="Download Explanation HTML")
+output_explanation = gr.outputs.File(label="Explanation HTML")
 
-gr.Interface(
-    fn=generate_explanation_html,
+iface = gr.Interface(
+    fn=merge,
     inputs=input_sentence,
-    outputs=output_explanation,
+    outputs=[gr.Image(type="pil", height=364, width=683, label = "Explanation Image"), output_explanation],
+    title="LIME LID",
+    description="This code applies LIME (Local Interpretable Model-Agnostic Explanations) on fasttext language identification.",
     allow_flagging='never'
-).launch()
+)
+
+iface.launch()

app_legacy.py

@@ -0,0 +1,113 @@
+# """
+# 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()

packages.txt

@@ -0,0 +1 @@
+chromium-chromedriver

requirements.txt

@@ -2,4 +2,5 @@ fasttext>=0.9.2
 lime>=0.2.0,<0.3.0
 huggingface-hub>=0.14.1
 numpy>=1.24.3
-gradio>=3.40.1
+gradio>=3.40.1
+selenium >=4.0.0, < 5.0.0