Project: Visual Story Generator: GPT-4o + DALL·E image flow based on prompt narrative

1. Setup & Initialization:
   • The script imports necessary libraries (openai, os, time, dotenv, datetime).
   • It loads the OPENAI_API_KEY from your .env file.
   • It initializes the client = OpenAI(...) object, which will be used for all API interactions. Error handling is included here.
   • Constants for the assistant's configuration, polling interval, and timeout are defined.
2. Create/Retrieve Assistant (create_or_retrieve_assistant function):
   • This function first checks if an assistant with the specified ASSISTANT_NAME already exists to avoid creating duplicates.
   • If not found, it calls client.beta.assistants.create(...) using the defined name, instructions, model (gpt-4o), and crucially enables the image_generation tool. This tool uses DALL·E 3 behind the scenes.
3. Start a Thread (generate_visual_story function):
   • A new conversation thread is created using client.beta.threads.create(). Threads store the message history.
4. Send User Prompt (generate_visual_story function):
   • The user's story idea (user_prompt) is added to the thread using client.beta.threads.messages.create(...) with role="user".
5. Run the Assistant (generate_visual_story function):
   • The assistant is instructed to process the thread using client.beta.threads.runs.create(...), passing the assistant_id and thread_id.
6. Wait for Completion (poll_run_status function):
   • Creating the story and images takes time. The script enters a loop, periodically checking the run's status using client.beta.threads.runs.retrieve(...).
   • It prints the status (queued, in_progress, completed, etc.) for user feedback.
   • The loop continues until the status is completed or another terminal state (failed, cancelled, expired), or until the timeout is reached.
7. Retrieve Story Text & Image File IDs (generate_visual_story function):
   • Once the run is complete, client.beta.threads.messages.list(...) retrieves all messages from the thread (using order="asc" to get them chronologically).
   • The code iterates through the assistant's messages.
   • For content of type="text", it prints the scene description.
   • For content of type="image_file", it prints the file_id associated with the generated image. This ID is the key to getting the actual image.
8. Retrieve Image Content (save_image_from_file_id function):
   • This is the crucial step added. For each file_id obtained in the previous step, this helper function is called.
   • It uses client.files.retrieve_content(file_id) to fetch the raw binary data of the image.
   • It then saves this binary data into a .png file in a specified output directory (story_images), including a timestamp and the file ID in the filename for uniqueness.

Optional Extensions

• Add page numbers or captions based on the scene_count.
• Modify the ASSISTANT_INSTRUCTIONS to accept a visual style parameter (e.g., "Generate images in a watercolor style").
• Implement logic to load a previous thread_id to continue or remix stories.
• Use a library like ReportLab or HTML generation to create a formatted PDF or web page output.

What You’ve Built

By running this script, you'll have executed a complete AI storytelling engine that:

• Accepts a natural language prompt.
• Uses GPT-4o to generate sequential scenes with descriptions.
• Invokes the DALL·E 3 tool to render matching illustrations.
• Retrieves both text descriptions and actual image files.
• Builds the components of a visually compelling narrative experience.

This project effectively combines multimodal handling (text prompt -> text + image generation), tool chaining within the Assistants API, asynchronous operation handling, and file retrieval into one practical and fun application.

1. Setup & Initialization:
   • The script imports necessary libraries (openai, os, time, dotenv, datetime).
   • It loads the OPENAI_API_KEY from your .env file.
   • It initializes the client = OpenAI(...) object, which will be used for all API interactions. Error handling is included here.
   • Constants for the assistant's configuration, polling interval, and timeout are defined.
2. Create/Retrieve Assistant (create_or_retrieve_assistant function):
   • This function first checks if an assistant with the specified ASSISTANT_NAME already exists to avoid creating duplicates.
   • If not found, it calls client.beta.assistants.create(...) using the defined name, instructions, model (gpt-4o), and crucially enables the image_generation tool. This tool uses DALL·E 3 behind the scenes.
3. Start a Thread (generate_visual_story function):
   • A new conversation thread is created using client.beta.threads.create(). Threads store the message history.
4. Send User Prompt (generate_visual_story function):
   • The user's story idea (user_prompt) is added to the thread using client.beta.threads.messages.create(...) with role="user".
5. Run the Assistant (generate_visual_story function):
   • The assistant is instructed to process the thread using client.beta.threads.runs.create(...), passing the assistant_id and thread_id.
6. Wait for Completion (poll_run_status function):
   • Creating the story and images takes time. The script enters a loop, periodically checking the run's status using client.beta.threads.runs.retrieve(...).
   • It prints the status (queued, in_progress, completed, etc.) for user feedback.
   • The loop continues until the status is completed or another terminal state (failed, cancelled, expired), or until the timeout is reached.
7. Retrieve Story Text & Image File IDs (generate_visual_story function):
   • Once the run is complete, client.beta.threads.messages.list(...) retrieves all messages from the thread (using order="asc" to get them chronologically).
   • The code iterates through the assistant's messages.
   • For content of type="text", it prints the scene description.
   • For content of type="image_file", it prints the file_id associated with the generated image. This ID is the key to getting the actual image.
8. Retrieve Image Content (save_image_from_file_id function):
   • This is the crucial step added. For each file_id obtained in the previous step, this helper function is called.
   • It uses client.files.retrieve_content(file_id) to fetch the raw binary data of the image.
   • It then saves this binary data into a .png file in a specified output directory (story_images), including a timestamp and the file ID in the filename for uniqueness.

Optional Extensions

• Add page numbers or captions based on the scene_count.
• Modify the ASSISTANT_INSTRUCTIONS to accept a visual style parameter (e.g., "Generate images in a watercolor style").
• Implement logic to load a previous thread_id to continue or remix stories.
• Use a library like ReportLab or HTML generation to create a formatted PDF or web page output.

What You’ve Built

By running this script, you'll have executed a complete AI storytelling engine that:

• Accepts a natural language prompt.
• Uses GPT-4o to generate sequential scenes with descriptions.
• Invokes the DALL·E 3 tool to render matching illustrations.
• Retrieves both text descriptions and actual image files.
• Builds the components of a visually compelling narrative experience.

This project effectively combines multimodal handling (text prompt -> text + image generation), tool chaining within the Assistants API, asynchronous operation handling, and file retrieval into one practical and fun application.

1. Setup & Initialization:
   • The script imports necessary libraries (openai, os, time, dotenv, datetime).
   • It loads the OPENAI_API_KEY from your .env file.
   • It initializes the client = OpenAI(...) object, which will be used for all API interactions. Error handling is included here.
   • Constants for the assistant's configuration, polling interval, and timeout are defined.
2. Create/Retrieve Assistant (create_or_retrieve_assistant function):
   • This function first checks if an assistant with the specified ASSISTANT_NAME already exists to avoid creating duplicates.
   • If not found, it calls client.beta.assistants.create(...) using the defined name, instructions, model (gpt-4o), and crucially enables the image_generation tool. This tool uses DALL·E 3 behind the scenes.
3. Start a Thread (generate_visual_story function):
   • A new conversation thread is created using client.beta.threads.create(). Threads store the message history.
4. Send User Prompt (generate_visual_story function):
   • The user's story idea (user_prompt) is added to the thread using client.beta.threads.messages.create(...) with role="user".
5. Run the Assistant (generate_visual_story function):
   • The assistant is instructed to process the thread using client.beta.threads.runs.create(...), passing the assistant_id and thread_id.
6. Wait for Completion (poll_run_status function):
   • Creating the story and images takes time. The script enters a loop, periodically checking the run's status using client.beta.threads.runs.retrieve(...).
   • It prints the status (queued, in_progress, completed, etc.) for user feedback.
   • The loop continues until the status is completed or another terminal state (failed, cancelled, expired), or until the timeout is reached.
7. Retrieve Story Text & Image File IDs (generate_visual_story function):
   • Once the run is complete, client.beta.threads.messages.list(...) retrieves all messages from the thread (using order="asc" to get them chronologically).
   • The code iterates through the assistant's messages.
   • For content of type="text", it prints the scene description.
   • For content of type="image_file", it prints the file_id associated with the generated image. This ID is the key to getting the actual image.
8. Retrieve Image Content (save_image_from_file_id function):
   • This is the crucial step added. For each file_id obtained in the previous step, this helper function is called.
   • It uses client.files.retrieve_content(file_id) to fetch the raw binary data of the image.
   • It then saves this binary data into a .png file in a specified output directory (story_images), including a timestamp and the file ID in the filename for uniqueness.

Optional Extensions

• Add page numbers or captions based on the scene_count.
• Modify the ASSISTANT_INSTRUCTIONS to accept a visual style parameter (e.g., "Generate images in a watercolor style").
• Implement logic to load a previous thread_id to continue or remix stories.
• Use a library like ReportLab or HTML generation to create a formatted PDF or web page output.

What You’ve Built

By running this script, you'll have executed a complete AI storytelling engine that:

• Accepts a natural language prompt.
• Uses GPT-4o to generate sequential scenes with descriptions.
• Invokes the DALL·E 3 tool to render matching illustrations.
• Retrieves both text descriptions and actual image files.
• Builds the components of a visually compelling narrative experience.

This project effectively combines multimodal handling (text prompt -> text + image generation), tool chaining within the Assistants API, asynchronous operation handling, and file retrieval into one practical and fun application.

1. Setup & Initialization:
   • The script imports necessary libraries (openai, os, time, dotenv, datetime).
   • It loads the OPENAI_API_KEY from your .env file.
   • It initializes the client = OpenAI(...) object, which will be used for all API interactions. Error handling is included here.
   • Constants for the assistant's configuration, polling interval, and timeout are defined.
2. Create/Retrieve Assistant (create_or_retrieve_assistant function):
   • This function first checks if an assistant with the specified ASSISTANT_NAME already exists to avoid creating duplicates.
   • If not found, it calls client.beta.assistants.create(...) using the defined name, instructions, model (gpt-4o), and crucially enables the image_generation tool. This tool uses DALL·E 3 behind the scenes.
3. Start a Thread (generate_visual_story function):
   • A new conversation thread is created using client.beta.threads.create(). Threads store the message history.
4. Send User Prompt (generate_visual_story function):
   • The user's story idea (user_prompt) is added to the thread using client.beta.threads.messages.create(...) with role="user".
5. Run the Assistant (generate_visual_story function):
   • The assistant is instructed to process the thread using client.beta.threads.runs.create(...), passing the assistant_id and thread_id.
6. Wait for Completion (poll_run_status function):
   • Creating the story and images takes time. The script enters a loop, periodically checking the run's status using client.beta.threads.runs.retrieve(...).
   • It prints the status (queued, in_progress, completed, etc.) for user feedback.
   • The loop continues until the status is completed or another terminal state (failed, cancelled, expired), or until the timeout is reached.
7. Retrieve Story Text & Image File IDs (generate_visual_story function):
   • Once the run is complete, client.beta.threads.messages.list(...) retrieves all messages from the thread (using order="asc" to get them chronologically).
   • The code iterates through the assistant's messages.
   • For content of type="text", it prints the scene description.
   • For content of type="image_file", it prints the file_id associated with the generated image. This ID is the key to getting the actual image.
8. Retrieve Image Content (save_image_from_file_id function):
   • This is the crucial step added. For each file_id obtained in the previous step, this helper function is called.
   • It uses client.files.retrieve_content(file_id) to fetch the raw binary data of the image.
   • It then saves this binary data into a .png file in a specified output directory (story_images), including a timestamp and the file ID in the filename for uniqueness.

Optional Extensions

• Add page numbers or captions based on the scene_count.
• Modify the ASSISTANT_INSTRUCTIONS to accept a visual style parameter (e.g., "Generate images in a watercolor style").
• Implement logic to load a previous thread_id to continue or remix stories.
• Use a library like ReportLab or HTML generation to create a formatted PDF or web page output.

What You’ve Built

By running this script, you'll have executed a complete AI storytelling engine that:

• Accepts a natural language prompt.
• Uses GPT-4o to generate sequential scenes with descriptions.
• Invokes the DALL·E 3 tool to render matching illustrations.
• Retrieves both text descriptions and actual image files.
• Builds the components of a visually compelling narrative experience.

This project effectively combines multimodal handling (text prompt -> text + image generation), tool chaining within the Assistants API, asynchronous operation handling, and file retrieval into one practical and fun application.