Added support for more models in ReplicateAPI. Completed automation script for Project Gutenberg. Updated README.

.gitignore

@@ -8,6 +8,9 @@
 **/empty_alt_text_sample.txt
 **/book_outputs
 **/downloaded_books
+**/results
+**/alts.txt
+**/images.txt
 
 **/keys.py
 **/vertex-key.json

README.md

@@ -1,6 +1,10 @@
 # Alt-Text
 
-A PyPi package used for finding, generating, and setting alt-text for images in HTML and EPUB files.
+A PyPi package used for finding, generating, and setting alt-text for images in HTML files.
+
+Developed as a Computer Science Senior Design Project at [Stevens Institute of Technology](https://www.stevens.edu/) in collaboration with the [Free Ebook Foundation](https://ebookfoundation.org/).
+
+[Learn more about the developers](#the-deveolpers).
 
 ## Getting Started
 
@@ -26,14 +30,18 @@ As of the moment, the image analyzation tools that Alt-Text uses are not fully b
 
 Description Engines are used to generate descriptions of an image. If you are to use one of these, you will need to fulfill that specific Engine's dependencies before use.
 
-##### ReplicateMiniGPT4API
+##### ReplicateAPI
 
-ReplicateMiniGPT4API Engine uses the [Replicate API](https://replicate.com/), hence you will need to get an API key via [Logging in with Github](https://replicate.com/signin) on the Replicate website.
+ReplicateAPI Engine uses the [Replicate API](https://replicate.com/), hence you will need to get an API key via [Logging in with Github](https://replicate.com/signin) on the Replicate website.
 
 ##### GoogleVertexAPI
 
 GoogleVertexAPI Engine uses the [Vertex AI API](https://cloud.google.com/vertex-ai), hence you will need to get access from the [Google API Marketplace](https://console.cloud.google.com/marketplace/product/google/aiplatform.googleapis.com). Additionally, Alt-Text uses Service Account Keys to get authenticated with Google Cloud, hence you will need to [Create a Service Account Key](https://cloud.google.com/iam/docs/keys-create-delete#creating) with permission for the Vertex AI API and have its according JSON.
 
+##### BlipLocal
+
+The BlipLocal Engine uses a modified version of the [cobanov/image-captioning repository](https://github.com/cobanov/image-captioning), which allows for the use of Blip locally via a CLI. To get started, you must download [this fork](https://github.com/xxmistacruzxx/image-captioning) of the repository and download/install the [BLIP-Large](https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth) checkpoint as described in the README.
+
 #### OCR Engines
 
 Optical Character Recognition Engines are used to find text within images. If you are to use one of these, you will need to fulfill that specific Engine's dependencies before use.
@@ -42,9 +50,126 @@ Optical Character Recognition Engines are used to find text within images. If yo
 
 The Tesseract Engine uses [Tesseract](https://github.com/tesseract-ocr/tesseract), hence you will need to install the [Tesseract OCR](https://tesseract-ocr.github.io/tessdoc/Installation.html).
 
+#### Language Engines
+
+Language Engines are used to generate a alt-text given an image description (from the [Description Engine](#Description-Engines)), characters found in an image (from the [OCR Engine](#OCR-Engines)), and context within the Ebook. If you are to use one of these, you will need to fulfill that specific Engine's dependencies before use.
+
+##### OpenAI API
+
+The OpenAI API Engine gives access to [Open AI's GPT Models via their API](https://platform.openai.com/docs/models). To use this, you will need an [API Key](https://openai.com/blog/openai-api) with access to the appropriate tier (more info on their [pricing page](https://openai.com/pricing)).
+
+##### PrivateGPT
+
+The PrivateGPT Engine gives allows for easy integration with an instance of [PrivateGPT](https://github.com/zylon-ai/private-gpt). To use this, you'll need a running instance of a [PrivateGPT API Server](https://docs.privategpt.dev/overview/welcome/introduction).
+
 ## Quickstart & Usage
 
-To be added...
+### Setup
+
+#### Standard Setup
+
+The standard setup assumes that you have access to a [Description Engine](#Description-Engines) and [Language Engine](#Language-Engines) (the [OCR Engine](#OCR-Engines) being optional).
+
+```python
+from alttext.alttext import AltTextHTML
+
+alt = AltTextHTML(
+    ReplicateAPI("REPLICATE_KEY"),
+    # Tesseract(),
+    OpenAIAPI("OPENAI_KEY", "gpt-3.5-turbo"),
+)
+```
+
+#### Legacy Setup
+
+This setup assumes that you have access to a [Description Engine]() (the [OCR Engine]() and [Language Engine]() being optional).
+
+```python
+from alttext.alttext import AltTextHTML
+
+alt = AltTextHTML(
+    ReplicateAPI("REPLICATE_KEY"),
+    # Tesseract(),
+    # OpenAIAPI("OPENAI_KEY", "gpt-3.5-turbo"),
+    options = {"version": 1}
+)
+```
+
+#### Options
+
+Below are the default options for the `AltTextHTML` class. You can change these by passing a `dict` into the `options` parameter during instantiation. When passing options, you only need the options you'd like to change from the default values in the `dict`.
+
+```python
+DEFOPTIONS = {
+    "withContext": True,
+    "withHash": True,
+    "multiThreaded": True,
+    "version": 2,
+}
+```
+
+### Basic Usage
+
+#### Loading an Ebook
+
+```python
+# from a file
+alt.parseFile("/path/to/ebook.html")
+
+# or from a string
+alt.parse("<HTML>...</HTML>")
+```
+
+#### Getting Images
+
+```python
+# getting all images
+imgs : list[bs4.element.Tag] = alt.getAllImgs()
+
+# getting all images with no alt attribute or where alt = ""
+imgs_noalt : list[bs4.element.Tag] = alt.getNoAltImgs()
+
+# get a specific image by src
+img : bs4.element.Tag = alt.getImg("path_as_in_html/image.png")
+```
+
+#### Generating Alt-Text
+
+```python
+# generate alt-text for a single image by src
+alt_text : str = alt.genAltText("path_as_in_html/image.png")
+
+# generate an association from an image tag
+# example_association = {
+#   "src" : "path_as_in_html/image.png"
+#   "alt" : "generated alt text"
+#   "hash" : 1234
+# }
+association : dict = alt.genAssociation(img : bs4.element.Tag)
+
+# generate a list of associations given a list of image tags
+associations : list[dict] = alt.genAltAssociations(imgs : list[bs4.element.Tag])
+```
+
+#### Setting Alt-Text
+
+```python
+# setting alt-text for a single image by src
+new_img_tag : bs4.element.Tag = alt.setAlt("path_as_in_html/image.png", "new alt")
+
+# setting alt-text for multiple images given a list of associations
+new_img_tags : list[bs4.element.Tag] = alt.setAlts(associations : list[dict])
+```
+
+#### Exporting Current HTML Status
+
+```python
+# getting current html as string
+html : str = alt.export()
+
+# exporting to a file
+path : str = alt.exportToFile("path/to/new_html.html")
+```
 
 ## Our Mission
 
@@ -52,9 +177,9 @@ The Alt-Text project is developed for the [Free Ebook Foundation](https://ebookf
 
 As Ebooks become a more prominant way to consume written materials, it only becomes more important for them to be accessible to all people. Alternative text (aka alt-text) in Ebooks are used as a way for people to understand images in Ebooks if they are unable to use images as intended (e.g. a visual impaired person using a screen reader to read an Ebook).
 
-While this feature exists, it is still not fully utilized and many Ebooks lack alt-text in some, or even all their images. To illustrate this, the [Gutenberg Project](https://gutenberg.org/), the creator of the Ebook and now a distributor of Public Domain Ebooks, have over 70,000 Ebooks in their collection and of those, there are about 470,000 images without alt-text.
+While this feature exists, it is still not fully utilized and many Ebooks lack alt-text in some, or even all their images. To illustrate this, the [Gutenberg Project](https://gutenberg.org/), the creator of the Ebook and now a distributor of Public Domain Ebooks, have over 70,000 Ebooks in their collection and of those, there are about 470,000 images without alt-text (not including images with insufficient alt-text).
 
-The Alt-Text project's goal is to use the power of AI, Automation, and the Internet to craft a solution capable of automatically generating descriptions for images lacking alt-text in Ebooks, closing the accessibility gap and improving collections, such as the [Gutenberg Project](https://gutenberg.org/).
+The Alt-Text project's goal is to use the power of various AI technologies, such as machine vision and large language models, to craft a solution capable of assisting in the creation of alt-text for Ebooks, closing the accessibility gap and improving collections, such as the [Gutenberg Project](https://gutenberg.org/).
 
 ### Contact Information
 
@@ -90,7 +215,7 @@ The emails and relevant information of those involved in the Alt-Text project ca
 
 ## APIs, Tools, & Libraries Used
 
-Alt-Text is developed using an assortment of modern Python tools...
+Alt-Text is developed using an assortment of tools...
 
 ### Development Tools
 
@@ -100,13 +225,18 @@ Alt-Text is developed using...
 - [EbookLib](https://pypi.org/project/EbookLib/)
 - [Replicate](https://pypi.org/project/replicate/)
 - [Google-Cloud-AIPlatform](https://pypi.org/project/google-cloud-aiplatform/)
+- [PyTorch](https://pypi.org/project/torch/)
 - [PyTesseract](https://pypi.org/project/pytesseract/)
+- [OpenAI Python API](https://pypi.org/project/openai/)
 
 ### APIs and Supplementary Tools
 
 - [Replicate API](https://replicate.com/)
 - [Vertex AI API](https://cloud.google.com/vertex-ai)
+- [cobanov/image-captioning](https://github.com/cobanov/image-captioning)
 - [Tesseract](https://github.com/tesseract-ocr/tesseract)
+- [OpenAI API](https://openai.com/blog/openai-api)
+- [PrivateGPT](https://github.com/zylon-ai/private-gpt)
 
 ### Packaging/Distribution Tools
 

src/alttext/descengine/replicateapi.py

@@ -5,18 +5,22 @@ import os
 from .descengine import DescEngine
 
 REPLICATE_MODELS = {
+    "blip-2": "andreasjansson/blip-2:f677695e5e89f8b236e52ecd1d3f01beb44c34606419bcc19345e046d8f786f9",
     "blip": "salesforce/blip:2e1dddc8621f72155f24cf2e0adbde548458d3cab9f00c0139eea840d0ac4746",
-    "clip_prefix_caption": "rmokady/clip_prefix_caption:9a34a6339872a03f45236f114321fb51fc7aa8269d38ae0ce5334969981e4cd8",
+    "llava-13b": "yorickvp/llava-13b:b5f6212d032508382d61ff00469ddda3e32fd8a0e75dc39d8a4191bb742157fb",
-    "clip-caption-reward": "j-min/clip-caption-reward:de37751f75135f7ebbe62548e27d6740d5155dfefdf6447db35c9865253d7e06",
     "img2prompt": "methexis-inc/img2prompt:50adaf2d3ad20a6f911a8a9e3ccf777b263b8596fbd2c8fc26e8888f8a0edbb5",
+    "clip_prefix_caption": "rmokady/clip_prefix_caption:9a34a6339872a03f45236f114321fb51fc7aa8269d38ae0ce5334969981e4cd8",
+    "clip-interrogator": "pharmapsychotic/clip-interrogator:8151e1c9f47e696fa316146a2e35812ccf79cfc9eba05b11c7f450155102af70",
+    "clip-caption-reward": "j-min/clip-caption-reward:de37751f75135f7ebbe62548e27d6740d5155dfefdf6447db35c9865253d7e06",
     "minigpt4": "daanelson/minigpt-4:b96a2f33cc8e4b0aa23eacfce731b9c41a7d9466d9ed4e167375587b54db9423",
     "image-captioning-with-visual-attention": "nohamoamary/image-captioning-with-visual-attention:9bb60a6baa58801aa7cd4c4fafc95fcf1531bf59b84962aff5a718f4d1f58986",
 }
 
+
 class ReplicateAPI(DescEngine):
-    def __init__(self, key: str, model: str = "blip") -> None:
+    def __init__(self, key: str, modelName: str = "blip") -> None:
         self.__setKey(key)
-        self.__setModel(model)
+        self.__setModel(modelName)
         return None
 
     def __getModel(self) -> str:
@@ -42,10 +46,18 @@ class ReplicateAPI(DescEngine):
         base64_utf8_str = base64.b64encode(imgData).decode("utf-8")
         model = self.__getModel()
         ext = src.split(".")[-1]
-        prompt = "Create alternative-text for this image."
-        if context != None:
-            prompt = f"Create alternative-text for this image given the following context...\n{context}"
-
         dataurl = f"data:image/{ext};base64,{base64_utf8_str}"
-        output = replicate.run(model, input={"image": dataurl, "prompt": prompt})
+
-        return output
+        input = {"image": dataurl}
+        if self.model == REPLICATE_MODELS["blip-2"]:
+            input["caption"] = True
+            input["question"] = ""
+        if self.model == REPLICATE_MODELS["llava-13b"]:
+            input["prompt"] = "What is this a picture of?"
+        if self.model == REPLICATE_MODELS["minigpt4"]:
+            input["prompt"] = "What is this a picture of?"
+
+        output = replicate.run(model, input=input)
+        if self.model == REPLICATE_MODELS["llava-13b"]:
+            return "".join(output)
+        return output

tests/automate.py

@@ -10,9 +10,13 @@ import keys
 
 sys.path.append("../")
 from src.alttext.alttext import AltTextHTML
+from src.alttext.descengine.descengine import DescEngine
 from src.alttext.descengine.replicateapi import ReplicateAPI
+from src.alttext.descengine.bliplocal import BlipLocal
+from src.alttext.descengine.googlevertexapi import GoogleVertexAPI
 from src.alttext.ocrengine.tesseract import Tesseract
 from src.alttext.langengine.openaiapi import OpenAIAPI
+from src.alttext.langengine.privategpt import PrivateGPT
 
 
 class AltTextGenerator(AltTextHTML):
@@ -29,10 +33,15 @@ class AltTextGenerator(AltTextHTML):
 
         # Description generation timing
         # print("starting desc")
-        genDesc_start_time = time.time()
+        genDesc = None
-        desc = self.genDesc(imgdata, src, context)
+        with open("./results/llava-13b.csv", mode="r") as csvfile:
-        genDesc_end_time = time.time()
+            reader = csv.DictReader(csvfile)
-        genDesc_total_time = genDesc_end_time - genDesc_start_time
+            for row in reader:
+                if row["book"] == book_id and row["image"] == src:
+                    genDesc = row["genDesc"]
+                    break
+        if genDesc == None:
+            raise Exception("Description not found in llava-13b.csv")
 
         # OCR processing timing
         # print("starting ocr")
@@ -44,7 +53,11 @@ class AltTextGenerator(AltTextHTML):
         # Refinement processing timing
         # print("starting refinement")
         refine_start_time = time.time()
-        refined_desc = self.langEngine.refineAlt(desc, chars, context, None)
+        if context[0] is not None:
+            context[0] = context[0][:1000]
+        if context[1] is not None:
+            context[1] = context[1][:1000]
+        refined_desc = self.langEngine.refineAlt(genDesc, chars[:1000], context, None)
         refine_end_time = time.time()
         refine_total_time = refine_end_time - refine_start_time
 
@@ -60,10 +73,7 @@ class AltTextGenerator(AltTextHTML):
             "status": status,  # Set false if failed, set true is worked
             "beforeContext": context[0],
             "afterContext": context[1],
-            "genDesc": desc,
+            "genDesc": genDesc,
-            "genDesc-Start": genDesc_start_time,
-            "genDesc-End": genDesc_end_time,
-            "genDesc-Time": genDesc_total_time,
             "genOCR": chars,
             "genOCR-Start": ocr_start_time,
             "genOCR-End": ocr_end_time,
@@ -95,11 +105,14 @@ def benchmarkBooks(booksDir: str, srcsDir: str):
     generator = AltTextGenerator(
         ReplicateAPI(keys.ReplicateEricKey()),
         Tesseract(),
-        OpenAIAPI(keys.OpenAIKey(), "gpt-3.5-turbo"),
+        # OpenAIAPI(keys.OpenAIKey(), "gpt-4-0125-preview"),
+        PrivateGPT("http://127.0.0.1:8001"),
     )
 
     records = []
-    for bookId in os.listdir(booksDir):
+    for bookId in os.listdir(srcsDir):
+        bookId = bookId.split("_")[1].split(".")[0]
+        time.sleep(1)
         try:
             bookPath = os.path.join(booksDir, bookId)
 
@@ -120,13 +133,77 @@ def benchmarkBooks(booksDir: str, srcsDir: str):
                     record = generator.genAltTextV2(src, bookId, src, bookPath)
                     records.append(record)
                 except Exception as e:
-                    print(f"Error processing image {src} in book {bookId}: {e}")
+                    print(f"ERROR processing image {bookId} | {src}: {e}")
         except Exception as e:
-            print(f"Error processing book {bookId}: {e}")
+            print(f"ERROR processing book {bookId}: {e}")
 
-    generateCSV("test_benchmark.csv", records)
+    generateCSV("private-gpt.csv", records)
+
+
+def benchmarkDescEngine(
+    descEngine: DescEngine, booksDir: str, srcsDir: str, outputFilename: str
+):
+    generator = AltTextHTML(descEngine)
+
+    records = []
+    for bookId in os.listdir(srcsDir):
+        bookId = bookId.split("_")[1].split(".")[0]
+        try:
+            print("STARTING BOOK ID: ", bookId)
+            bookPath = os.path.join(booksDir, bookId)
+
+            htmlpath = None
+            for object in os.listdir(bookPath):
+                if object.endswith(".html"):
+                    htmlpath = os.path.join(bookPath, object)
+                    break
+            generator.parseFile(htmlpath)
+
+            srcs = []
+            with open(f"{srcsDir}/ebook_{bookId}.txt", "r") as file:
+                for line in file:
+                    srcs.append(line.split(f"{bookId}/")[1].strip())
+
+            for src in srcs:
+                time.sleep(8)
+                try:
+                    print("STARTING IMAGE: ", src)
+                    context = generator.getContext(generator.getImg(src))
+                    genDesc_start_time = time.time()
+                    desc = generator.genDesc(generator.getImgData(src), src, context)
+                    print(f"TEST: {desc}")
+                    genDesc_end_time = time.time()
+                    genDesc_total_time = genDesc_end_time - genDesc_start_time
+                    record = {
+                        "book": bookId,
+                        "image": src,
+                        "path": bookPath,
+                        # "beforeContext": context[0],
+                        # "afterContext": context[1],
+                        "genDesc": desc.replace('"', "'"),
+                        "genDesc-Start": genDesc_start_time,
+                        "genDesc-End": genDesc_end_time,
+                        "genDesc-Time": genDesc_total_time,
+                    }
+                    records.append(record)
+                except Exception as e:
+                    print(f"ERROR processing image {bookId} | {src}: {e}")
+        except Exception as e:
+            print(f"ERROR processing book {bookId}: {e}")
+
+    generateCSV(outputFilename, records)
 
 
 if __name__ == "__main__":
     print("RUNNING AUTOMATE.PY")
     benchmarkBooks("./downloaded_books", "./book_outputs")
+    # benchmarkDescEngine(
+    # ReplicateAPI(
+    #     keys.ReplicateEricKey(), modelName="image-captioning-with-visual-attention"
+    # ),
+    # BlipLocal("C:/Users/dacru/Desktop/ALT/image-captioning"),
+    #     GoogleVertexAPI(keys.VertexProject(), keys.VertexRegion(), keys.VertexGAC()),
+    #     "./downloaded_books",
+    #     "./book_outputs2",
+    #     "vertexai.csv",
+    # )

tests/collect.py

@@ -0,0 +1,83 @@
+import random
+import requests
+import bs4
+import time
+import os
+
+
+def extractImage(imgs: list[bs4.element.Tag]) -> list[bs4.element.Tag]:
+    if len(imgs) == 0:
+        return None
+    index = random.randint(0, len(imgs) - 1)
+    img = imgs[index]
+    if img.has_attr("alt") and img.attrs["alt"].strip() != "":
+        return img
+    return extractImage(imgs[:index] + imgs[index + 1 :])
+
+
+def collect(
+    num: int, image_output: str = "images.txt", alt_output: str = "alts.txt"
+) -> int:
+    """
+    Collect images with alt-text from random ebooks
+
+    Args:
+        num (int): Number of images to collect.
+        image_output (str, optional): Path to output image URLs. Defaults to "images.txt".
+        alt_output (str, optional): Path to output alt-text. Defaults to "alts.txt".
+    """
+    count = 0
+    while count < num:
+        time.sleep(0.5)
+        bookid = random.randint(1, 70000)
+        bookurl = f"https://gutenberg.org/cache/epub/{bookid}/pg{bookid}-images.html"
+
+        response = requests.get(bookurl)
+        if response.status_code != 200:
+            print(f"Failed to fetch book {bookid}.")
+            continue
+
+        soup = bs4.BeautifulSoup(response.text, "html.parser")
+        div = soup.find("div", id="pg-machine-header")
+        if not div:
+            print(f"No 'pg-machine-header' found in book {bookid}.")
+            continue
+
+        languageP = div.find_all(recursive=False)[3]
+        if languageP.text.strip() != "Language: English":
+            print(f"Book {bookid} is not in English.")
+            continue
+
+        imgs: list[bs4.element.Tag] = soup.find_all("img")
+        img = extractImage(imgs)
+        if img is None:
+            print(
+                f"Out of {len(imgs)} images, no images with alt-text found in book {bookid}."
+            )
+            continue
+
+        with open(image_output, "a") as imagefile:
+            imagefile.write(f"{bookid} cache/epub/{bookid}/{img['src']}\n")
+        with open(alt_output, "a") as altfile:
+            altfile.write(f"{img['alt'].encode('ascii', 'ignore').decode()}\n")
+
+        count += 1
+
+    return True
+
+
+def split(input_file, book_output, image_output):
+    with open(input_file, "r") as file:
+        for line in file:
+            book_number = line.split()[0]  # Extracting book number
+            image = line.split()[1]  # Extracting image
+
+            with open(book_output, "a") as output_file:
+                output_file.write(f"{book_number}\n")
+            with open(image_output, "a") as output_file:
+                output_file.write(f"{image}\n")
+
+
+if __name__ == "__main__":
+    # collect(150)
+    split("images.txt", "books.txt", "images2.txt")

tests/downloadbooks.py

@@ -10,7 +10,7 @@ download_folder = "downloaded_books/download_files"
 extraction_folder = "downloaded_books"
 
 
-def download_and_unzip_books(folder_path, download_folder, extraction_folder):
+def downloadAndUnzipBooks(folder_path, download_folder, extraction_folder):
     base_url = "https://www.gutenberg.org/cache/epub/{book_id}/pg{book_id}-h.zip"
 
     # Ensure the download and extraction folders exist
@@ -68,4 +68,5 @@ def download_and_unzip_books(folder_path, download_folder, extraction_folder):
                 print(f"No book ID found in {filename}")
 
 
-download_and_unzip_books(folder_path, download_folder, extraction_folder)
+if __name__ == "__main__":
+    downloadAndUnzipBooks(folder_path, download_folder, extraction_folder)

tests/getbooks.py

@@ -4,31 +4,23 @@
 
 import os
 
-input_file = "./empty_alt_text_sample.txt"  # The file path of whatever initial .txt you are working with
+input_file = "./images.txt"
 output_folder = "./book_outputs"
 
 
-def createIndividualBookFiles(input_file, output_folder):
+def splitSampleByBook(input_file, output_folder):
-    # Ensure the output folder exists
     if not os.path.exists(output_folder):
         os.makedirs(output_folder)
 
-    # Keep track of the last book number processed
-    last_book_number = None
-
     with open(input_file, "r") as file:
         for line in file:
             book_number = line.split()[0]  # Extracting book number
-            # Check if this line is for a new book
+            output_file_name = f"ebook_{book_number}.txt"
-            if book_number != last_book_number:
+            output_path = os.path.join(output_folder, output_file_name)
-                output_file_name = f"ebook_{book_number}.txt"
-                output_path = os.path.join(output_folder, output_file_name)
-                # print(f"Creating/Updating file for book {book_number}")
-                last_book_number = book_number
 
-            # Append to the file (creates a new file if it doesn't exist)
             with open(output_path, "a") as output_file:
                 output_file.write(line)
 
 
-createIndividualBookFiles(input_file, output_folder)
+if __name__ == "__main__":
+    splitSampleByBook(input_file, output_folder)

tests/test.py

@@ -10,6 +10,7 @@ from src.alttext.langengine.openaiapi import OpenAIAPI
 import keys
 
 # HTML BOOK FILEPATHS
+HTML_ADVENTURES = "../books/pg76-h/pg76-images.html"
 HTML_BIRD = "../books/pg30221-h/pg30221-images.html"
 HTML_HUNTING = "../books/pg37122-h/pg37122-images.html"
 HTML_MECHANIC = "../books/pg71856-h/pg71856-images.html"
@@ -33,11 +34,20 @@ def testHTML():
         OpenAIAPI(keys.OpenAIKey(), "gpt-3.5-turbo"),
     )
 
-    alt.parseFile(HTML_HUNTING)
+    # imgs = alt.getAllImgs()
-    imgs = alt.getAllImgs()
+
-    src = imgs[7].attrs["src"]
+    alt.parseFile(HTML_ADVENTURES)
-    print(src)
+    img = alt.getImg("images/c01-21.jpg")
-    print(alt.genAltText(src))
+    src = img.attrs["src"]
+    imgData = alt.getImgData(src)
+    chars = alt.genChars(imgData, src)
+    desc = alt.genDesc(imgData, src, alt.getContext(img))
+    altText = alt.genAltText(src)
+    print(chars)
+    print("=====================================")
+    print(desc)
+    print("=====================================")
+    print(altText)
 
     # desc = alt.genDesc(alt.getImgData(src), src)
     # print(desc)