Gradio app e2e

ai_tasks/__init__.py

@@ -1 +1 @@
-
+from . import headlines_for_ai_images, headlines_for_images, text_summary

ai_tasks/headlines_for_images.py

@@ -16,11 +16,11 @@ Summary of the website:
 {summary}
 
 Image urls, with labels and dimensions:
-{images}
+{image_infos}
 
 Dimensions for the ad: {dimensions}.
 
-Use the folliowing format.
+Use the following format.
 
 Why the image was chosen:
 Url:
@@ -30,12 +30,12 @@ Original dimensions:
 
 
 def get_headline_for_image(
-    summary: str, dimensions: str, image_labels: List[Dict]
+    summary: str, dimensions: str, image_infos: List[Dict]
 ) -> str:
     print_system("Generating ad from images...")
     instructions = PROMPT.format(
         summary=summary,
-        images=json.dumps(image_labels, indent=2),
+        image_infos=json.dumps(image_infos, indent=2),
         dimensions=dimensions,
     )
     messages = [{"role": "user", "content": instructions}]

code_tasks/__init__.py

@@ -0,0 +1 @@
+from . import images_in_url, text_in_url

control_flow/main.py

@@ -1,13 +1,10 @@
-from typing import Dict
-import json
-
 from ai import image
+from ai_tasks.headlines_for_ai_images import generate_headline_and_prompt
 from ai_tasks.headlines_for_images import get_headline_for_image
-from ai_tasks.headlines_ai_images import generate_headline_and_prompt
 from ai_tasks.text_summary import summarize_text
-from code_tasks.custom import get_image_info, run_parallel_jobs
 from code_tasks.images_in_url import get_images_from_url
 from code_tasks.text_in_url import get_text_from_url
+from custom_code.image_analysis import analyze_images
 from utils.io import print_assistant, print_system, user_input
 
 
@@ -28,19 +25,20 @@ def run():
     print_system("Getting URL data...")
     text = get_text_from_url(url)
     images = get_images_from_url(url)
-    image_info = run_parallel_jobs(get_image_info, images, max=20)
-    print_system(json.dumps(image_info, indent=2))
+    image_infos = analyze_images(images)
 
     # AI tasks
     summary = summarize_text(text)
     print_assistant(summary)
     # Pick an image and generate a headline
-    headlines = get_headline_for_image(summary, dimensions, image_info)
-    print_assistant(headlines)
+    headline = get_headline_for_image(summary, dimensions, image_infos)
+    print_assistant(headline)
     # Generate a headline and an image
     headline_prompt = generate_headline_and_prompt(summary, dimensions)
     print_system("Generating AI images...")
-    ai_image = image.urls(headline_prompt["prompt"], size=headline_prompt["dimension_to_map"])[0]
+    ai_image = image.urls(
+        headline_prompt["prompt"], size=headline_prompt["dimension_to_map"]
+    )[0]
     print_system(f"Prompt: {headline_prompt['prompt']}")
     print_assistant(headline_prompt["ad_dimension"])
     print_assistant(headline_prompt["headline"])

custom_code/__init__.py

@@ -0,0 +1 @@
+from . import image_analysis

code_tasks/custom.py → custom_code/image_analysis.py

@@ -15,21 +15,22 @@ from code_tasks.image_dimensions import get_image_dimensions
 load_dotenv()
 
 
-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
-    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 run_parallel_jobs(job, inputs, max: int = 100) -> List[Dict]:
+def analyze_images(images) -> List[Dict]:
+    max = 20
+
+    def get_image_info(image_url: str) -> Dict:
+        client = vision.ImageAnnotatorClient()
+        image = vision.Image()
+        image.source.image_uri = image_url  # type: ignore
+
+        response = client.label_detection(image=image)  # type: ignore
+        labels = [label.description for label in response.label_annotations]
+        dimensions = get_image_dimensions(image_url)
+        return {
+            "url": image_url,
+            "labels": ", ".join(labels),
+            "dimensions": f"{dimensions[0]}x{dimensions[1]}",
+        }
+
     with ThreadPoolExecutor() as executor:
-        return list(executor.map(job, inputs[:max]))
+        return list(executor.map(get_image_info, images[:max]))

gradio_app.py

@@ -1,23 +1,135 @@
 import gradio as gr
-import random
-import time
-from typing import List
+
+import ai_tasks
+import code_tasks
+import custom_code
+from control_flow.main import _run
+
+
+def open__get_text_from_url() -> str:
+    with open("code_tasks/text_in_url.py") as f:
+        return f.read()
+
+
+def open__get_images_from_url() -> str:
+    with open("code_tasks/images_in_url.py") as f:
+        return f.read()
+
+
+def open__get_image_infos() -> str:
+    with open("custom_code/image_analysis.py") as f:
+        return f.read()
+
+
+def get_text_and_images_from_url(url):
+    return (
+        code_tasks.text_in_url.get_text_from_url(url),
+        code_tasks.images_in_url.get_images_from_url(url),
+    )
+
+
+def get_images_analysis(images):
+    return custom_code.image_analysis.analyze_images(eval(images))
 
 
 with gr.Blocks() as demo:
-    chatbot = gr.Chatbot()
-    msg = gr.Textbox()
-    clear = gr.Button("Clear")
-
-    def respond(message: str, chat_history: List):
-        print(type(message))
-        print(type(chat_history))
-        bot_message = random.choice(["How are you?", "I love you", "I'm very hungry"])
-        chat_history.append((message, bot_message))
-        time.sleep(1)
-        return "", chat_history
-
-    msg.submit(respond, [msg, chatbot], [msg, chatbot])
-    clear.click(lambda: None, None, chatbot, queue=False)
+    gr.Markdown(
+        """
+        ## Ad Generator
+        Enter an url and the dimensions for an image (eg, 300x600) and get the image and headline for an ad."""
+    )
+
+    url = gr.Textbox(label="Input: {url}")
+    dimensions = gr.Textbox(label="Input: {dimensions}")
+    execute = gr.Button("Run")
+
+    with gr.Box():
+        gr.Markdown("Code task")
+        with gr.Row():
+            with gr.Column():
+                gr.Textbox(
+                    "write a python function that given an url returns all text in the website",
+                    label="ChatGPT-4 prompt",
+                )
+                with gr.Accordion("Input: {url}", open=False):
+                    gr.Code(open__get_text_from_url(), "python")
+            with gr.Column():
+                text = gr.Textbox(
+                    label="Output: {text}", lines=10, max_lines=10, interactive=False
+                )
+
+    with gr.Box():
+        gr.Markdown("Code task")
+        with gr.Row():
+            with gr.Column():
+                gr.Textbox(
+                    "write a python function that given an url returns all images in the website",
+                    label="ChatGPT-4 prompt",
+                )
+                with gr.Accordion("Input: {url}", open=False):
+                    gr.Code(open__get_images_from_url(), "python")
+            with gr.Column():
+                images = gr.Textbox(
+                    label="Output: {images}", lines=10, max_lines=10, interactive=False
+                )
+
+    with gr.Box():
+        gr.Markdown("Custom code: analyze images with Google Vision")
+        with gr.Row():
+            with gr.Column():
+                with gr.Accordion("Input: {images}", open=False):
+                    gr.Code(open__get_image_infos(), "python")
+            with gr.Column():
+                image_infos = gr.Textbox(
+                    label="Output: {image_infos}",
+                    lines=10,
+                    max_lines=10,
+                    interactive=False,
+                )
+
+    with gr.Box():
+        gr.Markdown("AI task: summarize text")
+        with gr.Row():
+            with gr.Column():
+                gr.Textbox(
+                    ai_tasks.text_summary.PROMPT,
+                    label="Instructions",
+                    interactive=True,
+                )
+            with gr.Column():
+                summary = gr.Textbox(
+                    label="Output: {summary}", lines=10, max_lines=10, interactive=False
+                )
+
+    with gr.Box():
+        gr.Markdown("AI task: generate headline for image")
+        with gr.Row():
+            with gr.Column():
+                gr.Textbox(
+                    ai_tasks.headlines_for_images.PROMPT,
+                    label="Instructions",
+                    interactive=True,
+                )
+            with gr.Column():
+                headline = gr.Textbox(
+                    label="Output: {headline}",
+                    lines=20,
+                    max_lines=10,
+                    interactive=False,
+                )
+
+    execute.click(
+        get_text_and_images_from_url, inputs=[url], outputs=[text, images]
+    ).success(
+        get_images_analysis,
+        inputs=[images],
+        outputs=[image_infos],
+    ).success(
+        ai_tasks.text_summary.summarize_text, inputs=[text], outputs=[summary]
+    ).success(
+        ai_tasks.headlines_for_images.get_headline_for_image,
+        inputs=[summary, dimensions, image_infos],
+        outputs=[headline],
+    )
 
 demo.launch()