Ads generated from website images

ai/image.py

@@ -2,6 +2,10 @@ import os
 from typing import Any, Dict, List
 
 import openai
+from dotenv import load_dotenv
+
+load_dotenv()
+
 
 openai.api_key = os.environ["OPENAI_KEY_PERSONAL"]
 

ai/llm.py

@@ -2,6 +2,9 @@ import os
 from typing import Any, Dict, List, Optional
 
 import openai
+from dotenv import load_dotenv
+
+load_dotenv()
 
 
 openai.api_key = os.environ["OPENAI_KEY_PERSONAL"]

ai_tasks/headlines_for_images.py

@@ -0,0 +1,41 @@
+import json
+from typing import Dict, List
+
+from ai import llm
+from utils.io import print_system
+
+
+PROMPT = """
+Your goal is to find a set of the best images that can be used as ads for a website.
+The ads have to be in certain dimensions. The images can be edited or cut.
+So you must consider which images would be better suited to fit those dimensions.
+You should consider the content of the website. The content of the images has been labeled.
+You will create a headline for each ad.
+
+Summary of the website:
+{summary}
+
+Image urls, with labels and dimensions:
+{images}
+
+Dimensions for the ads: {dimensions}
+
+Use the folliowing format. Each dimension requirement must be represented.
+
+Why the image was chosen:
+Url:
+Headline:
+Image dimensions:
+Target dimensions: An image can be a good match for more than one dimension
+"""
+
+
+def get_headlines(summary: str, dimensions: List[str], image_labels: List[Dict]) -> str:
+    print_system("Generating headlines for images...")
+    instructions = PROMPT.format(
+        summary=summary,
+        images=json.dumps(image_labels, indent=2),
+        dimensions=dimensions,
+    )
+    messages = [{"role": "user", "content": instructions}]
+    return llm.next(messages, temperature=0)

ai_tasks/text_summary.py

@@ -0,0 +1,20 @@
+import json
+from typing import Dict, List
+
+from ai import llm
+from utils.io import print_system
+
+
+PROMPT = """
+Here is the text from a website:
+{text}
+
+What is this website about?
+"""
+
+
+def summarize_text(text: str) -> str:
+    print_system("Summarizing text...")
+    instructions = PROMPT.format(text=text)
+    messages = [{"role": "user", "content": instructions}]
+    return llm.next(messages)

code_tasks/custom.py

@@ -1,22 +1,29 @@
 from concurrent.futures import ThreadPoolExecutor
 from typing import Dict, List
 
+from dotenv import load_dotenv
 from google.cloud import vision
 
-from utils.io import print_system
+from code_tasks.image_dimensions import get_image_dimensions
 
+load_dotenv()
 
-def get_labels_from_image(url: str) -> List[str]:
+
+def get_image_info(url: str) -> Dict:
     client = vision.ImageAnnotatorClient()
     image = vision.Image()
     image.source.image_uri = url  # type: ignore
 
     response = client.label_detection(image=image)  # type: ignore
-    return [label.description for label in response.label_annotations]
+    labels = [label.description for label in response.label_annotations]
+    dimensions = get_image_dimensions(url)
+    return {
+        "url": url,
+        "labels": ", ".join(labels),
+        "dimensions": f"{dimensions[0]}x{dimensions[1]}",
+    }
 
 
-def get_labels_from_images(urls) -> Dict:
-    print_system("Generating labels for images...")
+def get_info_for_images(urls) -> List[Dict]:
     with ThreadPoolExecutor() as executor:
-        labels = list(executor.map(get_labels_from_image, urls))
-    return {urls[i]: labels[i] for i, url in enumerate(labels)}
+        return list(executor.map(get_image_info, urls))

code_tasks/image_dimensions.py

@@ -0,0 +1,8 @@
+from PIL import Image
+import requests
+from io import BytesIO
+
+def get_image_dimensions(image_url):
+    response = requests.get(image_url)
+    img = Image.open(BytesIO(response.content))
+    return img.size

control_flow/main.py

@@ -1,22 +1,44 @@
+import json
+
 from ai import image
-from ai_tasks.best_headlines import get_headlines
+
+# from ai_tasks.best_headlines import get_headlines
+from ai_tasks.headlines_for_images import get_headlines
 from ai_tasks.image_prompt import generate_prompt
-from code_tasks.custom import get_labels_from_images
+from ai_tasks.text_summary import summarize_text
+from code_tasks.custom import get_info_for_images
 from code_tasks.images_in_url import get_images_from_url
 from code_tasks.text_in_url import get_text_from_url
 from utils.io import print_assistant, print_system, user_input
 
 
+DIMENSIONS = [
+    "300x50",
+    "300x250",
+    "300x600",
+    "728x90",
+    "160x600",
+]
+
+
 def run():
-    url = user_input("URL: ")
+    # url = user_input("URL: ")
+    url = "https://www.beachterracemc.com/"
+    print_system("Getting URL data...")
     text = get_text_from_url(url)
     images = get_images_from_url(url)
-    image_labels = get_labels_from_images(images)
+    image_info = get_info_for_images(images)
+    print_system(json.dumps(image_info, indent=2))
 
-    headlines = get_headlines(text)
+    summary = summarize_text(text)
+    print_assistant(summary)
+    headlines = get_headlines(summary, DIMENSIONS, image_info)
     print_assistant(headlines)
-    prompt = generate_prompt(text)
-    print_assistant(prompt)
-    print_system("Generating images...")
-    image_urls = image.urls(prompt, n=4)
-    print_assistant("\n\n".join(image_urls))
+
+    # headlines = get_headlines(text)
+    # print_assistant(headlines)
+    # prompt = generate_prompt(text)
+    # print_assistant(prompt)
+    # print_system("Generating images...")
+    # image_urls = image.urls(prompt, n=4)
+    # print_assistant("\n\n".join(image_urls))

main.py

@@ -1,10 +1,5 @@
-from dotenv import load_dotenv
-
 from control_flow.main import run
 
 
-load_dotenv()
-
-
 if __name__ == "__main__":
     run()