POD Design Generator From URL
Initial Research on Guidelines
The agent begins by familiarizing itself with Amazon's print-on-demand guidelines. This involves performing a web search to locate relevant documentation and using the browse_page action to read the content, ensuring understanding of rules such as content restrictions, design originality, and listing requirements.
This step is crucial to ensure all subsequent actions comply with platform policies, which may include avoiding copyrighted material and ensuring designs meet quality standards.
Input and Data Extraction
The user provides a URL linking to a shirt design on Amazon or Etsy. The agent uses the real time amazon data to receive product information
Title, typically found in the tag or specific divs.
Bullet points and description, often located in designated sections of the page.
The main image URL, essential for design analysis.
This parsing requires understanding the HTML structure, which may vary between Amazon and Etsy, necessitating flexible parsing techniques like CSS selectors or XPath.
Trademark Check on Original Listing
To identify potential trademarked elements, the agent performs two checks:
Text Analysis: Extracts keywords and phrases from the title, bullet points, and description. For each, it uses the web_search action to query "[keyword] trademark" to determine if the term is registered, flagging any hits associated with known brands.
Image Analysis: Conducts a reverse image search by navigating to a site like Google Images (Google Images) using browse_page, inputs the image URL, and parses results to see if the image or similar ones are linked to trademarked designs, such as branded logos or patterns.
This step ensures the agent avoids replicating protected elements in the new design.
Keyword Extraction and Image Description
From the extracted text, the agent filters out potential trademarked terms identified in step 3, retaining non-trademarked keywords that describe the design (e.g., "vintage," "graphic tee").
It also analyzes the original image to describe its style, colors, and patterns in text, preparing for image generation. This description might note, for example, "red shirt with abstract geometric patterns."
Generation of New Design
Image Generation: The agent crafts a prompt combining the non-trademarked keywords and image description (e.g., "generate a red shirt with abstract geometric patterns, inspired by vintage style"). It then uses an online text-to-image generation tool, accessible via browse_page, such as DreamStudio (DreamStudio). It interacts with the site to input the prompt, retrieves the generated image URL, and ensures the design is similar but distinct from the original.
Text Generation: Creates new title, bullet points, and description using the keywords, ensuring no trademarked terms are included and aligning with Amazon's guidelines (e.g., clear, descriptive, and non-misleading text).
Final Trademark and Compliance Check
The agent repeats the trademark check on the new text and image:
Searches for each new keyword with "trademark" to ensure no issues.
Performs another reverse image search on the new image to confirm it doesn't resemble any trademarked designs.
It also verifies the new design complies with Amazon's print-on-demand rules, such as avoiding offensive content or ensuring high resolution.
User Approval and Delivery
The agent presents the new design (image and text) to the user for approval, likely through an interface displaying both elements.
Upon approval, it provides options to download the image and copy the text, facilitating integration into the user's workflow, such as listing on Amazon.
Challenges and Considerations
Image Generation Complexity: Interacting with text-to-image tools via browse_page requires simulating user actions, which may be limited by website interfaces. The agent must handle variations in tool functionality, potentially requiring multiple attempts to generate a suitable image.
Trademark Verification: Determining trademark status is complex, as it involves legal nuances beyond simple web searches. The agent's checks (keyword searches, reverse image searches) are rudimentary and may not capture all issues, necessitating user discretion.
Platform Variability: Amazon and Etsy pages have different HTML structures, requiring robust parsing strategies to extract consistent data.
Surprising Detail: Extensive Web Interaction: The need for reverse image searches and image generation via external websites adds significant complexity, as the agent must navigate and parse dynamic web content, a task not typically associated with simple AI automation.
here is the API you need to use for image recognition which is from chatgpt vision 2.1 and this is the code snippet
curl --request POST \
--url https://chatgpt-vision1.p.rapidapi.com/matagvision21 \
--header 'Content-Type: application/json' \
--header 'x-rapidapi-host: chatgpt-vision1.p.rapidapi.com' \
--header 'x-rapidapi-key: 0dea502dd6mshefa43cb00153aedp1b2cdejsn596b802861cd' \
--data '{"messages":[{"role":"user","content":[{"type":"text","text":"what'\''s in the image"},{"type":"image","url":"https://raw.githubusercontent.com/rphrp1985/mycodes/main/matagimage.png"}]}],"web_access":false}'
this is the code snippet and API for real time amazon URL or ASIN extractor
curl --request GET \
--url 'https://real-time-amazon-data.p.rapidapi.com/product-details?asin=B07ZPKBL9V&country=US' \to
--header 'x-rapidapi-host: real-time-amazon-data.p.rapidapi.com' \
--header 'x-rapidapi-key: 0dea502dd6mshefa43cb00153aedp1b2cdejsn596b802861cd'
here is the search trademark to make sure the listing text or image is not trademarked and we can use it for creating a new listing. I am providing you the snippet and API
curl --request GET \
--url https://uspto-trademark-search-api.p.rapidapi.com/google/namesearch/1/10 \
--header 'x-rapidapi-host: uspto-trademark-search-api.p.rapidapi.com' \
--header 'x-rapidapi-key: 0dea502dd6mshefa43cb00153aedp1b2cdejsn596b802861cd'
for ideogram API with snippets
curl -X POST https://api.ideogram.ai/remix \
-H "Api-Key: penEs2TUqS98IfthOgjX4QLs4IjPRzBLgPp4MOPLsXE7isV6ivlT2OMX7RVYhUjvGPI1ALf0tQko-3uVKnw1AA" \
-H "Content-Type: multipart/form-data" \
-F image_request='{
"prompt": "",
"aspect_ratio": "ASPECT_1_1",
"image_weight": 95,
"magic_prompt_option": "OFF",
"model": "V_2",
"num_images": 6,
"style_type": "DESIGN",
"negative_prompt": "no t-shirt mockup, no blur, no low quality, no background"
}' \
-F image_file=@"Screenshot tshirt.png;type=image/png"
here is ai-image-upscaler snippet and API
curl --request POST \
--url https://ai-image-upscaler1.p.rapidapi.com/v1 \
--header 'Content-Type: multipart/form-data' \
--header 'x-rapidapi-host: ai-image-upscaler1.p.rapidapi.com' \
--header 'x-rapidapi-key: 0dea502dd6mshefa43cb00153aedp1b2cdejsn596b802861cd' \
--form image=a-black-t-shirt-with-a-colorful-text-des_CPtyFhCdQIykXQB_CnJAkQ_R6V4EV_eSbqgOrHZfnkO2A.png
here is a background removal with its API and code snippets:
curl --request POST \
--url https://ai-background-remover.p.rapidapi.com/image/matte/v1 \
--header 'Content-Type: multipart/form-data' \
--header 'x-rapidapi-host: ai-background-remover.p.rapidapi.com' \
--header 'x-rapidapi-key: 0dea502dd6mshefa43cb00153aedp1b2cdejsn596b802861cd' \
--form image=in-a-vivid-circular-illustration-the-phr_xcVsx4evRsWPOcmSvE2rqg_MVOd8NgcQn2G0exmjbLACw.png
I need all image to be saved with transparent background and .png format. it also needs to be 300 dpi 4500 x 5400 pixels
here is merch on demand policy guidelines
Merch by Amazon Use Policy: Key Aspects for Design Content
Merch by Amazon has a Content Policy that outlines what types of designs are permitted on their platform. It's essential to adhere to this policy to avoid having your designs rejected or your account suspended. Here are the critical points:
No Infringement of Intellectual Property Rights:
Copyright: You cannot use copyrighted material in your designs without proper authorization. This includes images, text, logos, and characters from books, movies, TV shows, video games, etc.
Trademark: You cannot use trademarks in your designs without authorization. This includes brand names, slogans, and logos. You need to conduct thorough trademark research before creating designs. Using a trademark checker API is recommended.
Patent: You cannot use patented material in your designs without permission.
Right of Publicity: You cannot use the likeness, image, or name of a celebrity or public figure without permission.
No Offensive, Abusive, or Illegal Content:
Hate Speech: Designs that promote hatred, discrimination, or violence against individuals or groups based on race, ethnicity, religion, gender, sexual orientation, disability, etc., are prohibited.
Violence: Designs that promote or glorify violence are not allowed.
Illegal Activities: Designs that promote or condone illegal activities (e.g., drug use, terrorism) are prohibited.
Obscene Content: Designs that are sexually explicit, promote child exploitation, or are otherwise obscene are not allowed.
Bullying/Harassment: Designs that target individuals for harassment or bullying are prohibited.
Accurate and Honest Representation:
Misleading Claims: Designs should not make false or misleading claims about products or services.
Deceptive Practices: Designs should not engage in deceptive practices, such as creating fake reviews or using misleading advertising.
Content Guidelines Specific to Events:
For designs related to current events (e.g., holidays, political events), you must ensure they do not exploit, trivialize, or lack reasonable sensitivity toward tragic or controversial events.
Amazon Brand Assets:
You cannot use Amazon's trademarks, logos, or other brand assets without express permission.
Content Policy Enforcement:
Amazon has the right to review and remove any design that violates their content policy.
Repeated violations can lead to account suspension or termination.
Amazon may also cooperate with law enforcement in cases of illegal activity.
Responsibility:
You are solely responsible for ensuring your designs comply with Amazon's content policy.
Practical Steps to Ensure Compliance
Research, Research, Research: Before creating any design, thoroughly research potential trademark and copyright issues.
Use Trademark Search Tools: Utilize tools like the USPTO website or trademark checker APIs to identify potential conflicts.
Read the Fine Print: Carefully review the terms of any images, fonts, or other design elements you use to ensure you have the necessary licenses.
Err on the Side of Caution: If you're unsure whether a design might violate the content policy, it's best to avoid using it.
Stay Updated: Amazon's content policy may change, so it's essential to stay informed of any updates.
make sure you are using the API's I have provided you BOT=API
you will need to make sure you are using all the API's provided to you and the snippets for each API
API provided above are:
real time amazon data extraction API
chatgpt vision 2.1 API
Ideogram API for image generation API
Remove.bg APIs for editing tasks.
Trademark Check API for validation.
ai image upscaler API
</h3>
<br>
<h1>First, pre-entered info...</h1>
<br>
<h3>final-output2</h3>
<textarea>The final output is a high-resolution PNG file (300 DPI, 4500 × 5400 pixels, transparent background) representing a newly generated T-shirt design, plus a text-based file (for example, a plain text or simple JSON file) containing the listing details (title, bullet points, description).</textarea>
<br>
<h3>subagents-extracted2</h3>
<textarea>OVERVIEW OF SUBAGENTS
• Subagent 1: DataExtractionBot
• Subagent 2: ComplianceCheckerBot
• Subagent 3: DesignGeneratorBot
• Subagent 4: ListingTextCreatorBot
• Subagent 5: FinalOutputBot
SUBAGENT 1: DataExtractionBot
Name: DataExtractionBot
Final Output (description):
• A single structured set of product details (title, bullet points, description, main image URL).
Skills/Actions:
1) #226 - Extract Structured Data From 1x URL
- Input: The Amazon/Etsy product URL, specifying needed fields (title, bullets, description, main image).
- Output: A single JSON or text summary (for example, { "title": "...", "bullets": [...], "description": "...", "imageURL": "..." }).
SUBAGENT REQUIRED INPUT: {product-url}
SUBAGENT FINAL OUTPUT: [extracted-product-details]
SUBAGENT 2: ComplianceCheckerBot
Name: ComplianceCheckerBot
Final Output (description):
• Clean, policy-compliant text and keywords in a single consolidated output.
Skills/Actions:
1) #223 - Prompt LLM to identify possible trademark/policy violations in the text. Group all flagged terms.
2) #224 (optional, limited queries) - Check suspicious terms against a trademark database.
3) #190 - Rewrite or remove flagged terms, producing safe text and final keywords (consolidated into one block of text or data structure).
SUBAGENT REQUIRED INPUT: [extracted-product-details]
SUBAGENT FINAL OUTPUT: [compliant-text-and-keywords]
SUBAGENT 3: DesignGeneratorBot
Name: DesignGeneratorBot
Final Output (description):
• A single PNG file (4500 × 5400, 300 DPI, transparent background).
Skills/Actions:
1) #222 - Generate an AI image (transparent background if possible) using the cleaned keywords.
2) #191 - Resize and confirm the final PNG is 4500 × 5400 at 300 DPI.
SUBAGENT REQUIRED INPUT: [compliant-text-and-keywords]
SUBAGENT FINAL OUTPUT: [new-tshirt-design-png]
SUBAGENT 4: ListingTextCreatorBot
Name: ListingTextCreatorBot
Final Output (description):
• A single text block containing the revised listing fields: title, bullet points, description.
Skill/Action:
1) #190 - Write updated listing details, ensuring compliance and using the safe keywords from Subagent 2.
SUBAGENT REQUIRED INPUT: [compliant-text-and-keywords]
SUBAGENT FINAL OUTPUT: [revised-listing-text]
SUBAGENT 5: FinalOutputBot
Name: FinalOutputBot
Final Output (description):
• Delivers the final PNG (the newly generated T-shirt design) and the final text file (title, bullet points, description).
Skills/Actions:
1) #224 (single check if necessary) - Quick final trademark or policy confirmation on the new text or design before delivering.
2) #223 (Powerful LLM Prompt-to-Text Response) - Summarize or collate the references (PNG file path + text-based file) into a single final output for the user (so the user clearly receives both outputs with one handoff).
SUBAGENT REQUIRED INPUT: [new-tshirt-design-png], [revised-listing-text]
SUBAGENT FINAL OUTPUT: [final-package]</textarea>
<br>
<h3>subagentnumber</h3>
<textarea>[subagentnumber]</textarea>
<h1>Then, agent response...</h1>
<br><br>
<h3>subagent1refined-new-json-core</h3>
<textarea>I'll provide the complete subagent summary for SUBAGENT 1 (DataExtractionBot), including the new G) TASKFLOW SUMMARY section.
A) SUBAGENT SUMMARY
This subagent (DataExtractionBot) crawls the provided Amazon/Etsy product URL to retrieve title, bullet points, description, and main image URL in a clean, structured format.
B) FINAL TASK OUTPUT
[extracted-product-details] → A single JSON (or structured text) object containing:
{
"title": "...",
"bullets": [ "...", "..." ],
"description": "...",
"imageURL": "..."
}
C) SUBAGENT INPUT
{product-url} → The Amazon/Etsy product page URL to extract data from.
E) SUBAGENT TASK SUMMARY
1) #226 - Extract Structured Data From 1x URL
- INPUT: Product URL and fields to extract
- OUTPUT: Structured text data
2) #223 - Transform extracted data to JSON format
- INPUT: Structured text from #226
- OUTPUT: Final JSON object
F) SILOS
• Data extraction and conversion to JSON.
G) TASKFLOW SUMMARY
Task 1:
- ID: #226
- Title task: Extract Structured Product Data from Amazon/Etsy URL
- Description: Uses the real-time Amazon data API to extract specific product information including title, bullet points, description, and main image URL from the provided product page
- Input(s): {product-url}, real-time-amazon-data-api-key
- Output token name: [raw-extracted-data]
Task 2:
- ID: #223
- Title task: Transform Raw Data into Standardized JSON Format
- Description: Processes the raw extracted data and transforms it into a clean, standardized JSON structure with specific keys for title, bullets, description, and imageURL
- Input(s): [raw-extracted-data]
- Output token name: [extracted-product-details]
The taskflow follows this sequence:
{product-url} → #226 → [raw-extracted-data] → #223 → [extracted-product-details]
Each task utilizes the provided API endpoints, specifically the real-time Amazon data API:
```curl
curl --request GET \
--url 'https://real-time-amazon-data.p.rapidapi.com/product-details?asin=B07ZPKBL9V&country=US' \
--header 'x-rapidapi-host: real-time-amazon-data.p.rapidapi.com' \
--header 'x-rapidapi-key: 0dea502dd6mshefa43cb00153aedp1b2cdejsn596b802861cd'
```</textarea>
<br>
<h3>subagent1mermaid</h3>
<textarea>
<!DOCTYPE html>
<html>
<head>
<title>Mermaid Diagram Example
graph TD
A[Start - Product URL Input] --> B[Task #226: Extract Data]
B --> C{Valid URL?}
C -->|No| D[Error: Invalid URL]
D --> Z[End - Error State]
C -->|Yes| E[Call Real-Time Amazon API]
E --> F{API Response OK?}
F -->|No| G[Error: API Failed]
G --> Z
F -->|Yes| H[Extract Required Fields]
H --> I[raw-extracted-data]
I --> J[Task #223: Transform Data]
J --> K[Parse Raw Data]
K --> L[Create JSON Structure]
L --> M{JSON Valid?}
M -->|No| N[Error: Invalid JSON]
N --> Z
M -->|Yes| O[extracted-product-details]
O --> P[End - Success State]
subgraph API Dependencies
E --> Q[API Key Check]
Q --> R[Rate Limit Check]
end
subgraph Data Fields
H --> S[Title]
H --> T[Bullets]
H --> U[Description]
H --> V[Image URL]
end