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What This Script Does This script is built to work with an external service called the Visionati API. Its main purpose is to analyze an image by sending it along with a set of instructions (or “prompts”) to the API. These instructions can be a mix of pre-defined configurations and user-provided ideas. The script then waits for the API to process the image and returns a structured result in JSON format. In short, it automates image analysis using a remote service and organizes the result so that it can be easily understood or used in other applications. How It Works The script starts by setting up logging to keep track of what happens during the process. Logging means that the script writes messages about its progress, errors, and important events. This is helpful for both users and developers to see what is happening inside the script. 1. Configuration and Setup:The script defines some settings such as API URLs, how long to wait before retrying a request, and how many times to try before giving up. It also includes a function that logs each major step to help track the script’s flow. 2. Gathering Prompts:Users can choose different “prompt” paths that determine what kind of instructions will be sent to the API. These prompts might include different image processing ideas like “detailed image” or “camera movement”. The script also accepts a list of custom prompts, so you can test new instructions if needed. 3. Validating Inputs:Before sending any request, the script checks if an image URL is provided and if at least one prompt is selected. It also makes sure the selected model (used to process the image) is one of the allowed ones. If anything is missing or incorrect, it stops and returns an error. 4. Getting the API Key:The API key is a special code needed to use the Visionati API. The script retrieves this key from a resource (using a tool called Windmill). If the API key is missing, the script will return an error message and stop. 5. Building and Sending the Request:For each prompt, the script builds a complete set of instructions. This includes a “master prompt” from the configuration, user-provided ideas if any, extra guidelines, and even an example of what the expected output should look like in JSON format. All these details are combined into one message that is sent to the Visionati API using a POST request. 6. Polling for the Result:The API does not always give a response immediately. When the script sends a request, it often receives a “request ID” which is used to check on the progress. The script then periodically “polls” the API by making additional requests until it finds out that the image processing is complete. There is a limit to how long the script will wait before it gives up. 7. Extracting the Result:Once the processing is complete, the script looks for the result in the response. It extracts the text that was generated by the API, which might include a description of the image. The script then tries to extract structured data (in JSON format) from that text. If the text does not have a clear JSON structure, it will return the raw text. 8. Error Handling:The script includes many checks and logging messages to handle errors. For example, if there is a network problem or the response does not include the expected information, the script catches these issues and logs a clear message. This ensures that even if something goes wrong, the problem is reported in a way that can be understood and fixed. Common Ways How to Use This Script • Image Analysis for Web Services:You might use this script on a server that receives images via a website. When an image is uploaded, the script sends it to the Visionati API, waits for the analysis, and then returns the structured result to be displayed on the website. • Automated Image Processing:If you have a batch of images that need to be analyzed, you can use this script to process them one after the other or even in parallel. This means you can automate the work of analyzing many images without manual intervention. • Testing Different Prompts:Because the script supports multiple prompt paths, developers or content creators can experiment with different instructions. This helps in finding out which prompt produces the best analysis for a particular type of image. • Integration in Larger Systems:The script can be a part of a larger image processing or machine learning system. For example, it might be integrated into an app that recommends products based on images or into an analytics tool that monitors visual content. The Problem It Solves Before this script, processing images with advanced AI models could be a slow and manual task. Users had to send images one by one and manually check if the processing was complete. The script solves these problems by: • Automating the Process:It takes care of sending the image, waiting for the response, and extracting the important data. This saves time and reduces the chance of human error. • Handling Multiple Prompts:By allowing several different instructions to be sent in parallel, the script helps users compare results quickly and choose the best one. • Ensuring Consistent Output:The script forces the API’s response into a structured JSON format. This consistency makes it easier for other parts of a system to use the output without extra processing. • Robust Error Management:With detailed logging and error checks, the script can detect and report problems clearly. This is especially useful in production systems where knowing the cause of an error is important for quick fixes. Where to Implement, Benefits, and Implementation Requirements • Where to Implement:This script is best run on a server or within a cloud environment where it can continuously process image requests. It can be integrated into websites, mobile applications, or any system that requires automated image analysis. • Benefits: • Efficiency: Automates the analysis process, saving time and manual effort. • Flexibility: Supports multiple prompt configurations and backend models, allowing for a wide range of image analysis scenarios. • Consistency: Provides output in a clear, structured format that is easy to work with. • Robustness: Built-in error handling and logging make it reliable and easier to troubleshoot. • Implementation Requirements: • API Access: You must have a valid API key for the Visionati API, stored in the proper resource location (using Windmill in this case). • Dependencies: The script requires Python libraries such as requests, json, and concurrent.futures. Make sure these are installed in your environment. • Network Access: A stable internet connection is necessary since the script communicates with external API services. • Configuration: Proper configuration of prompt paths, backend models, and other settings is needed to tailor the analysis to your specific requirements.

{ "task_type": "kling_camera_movement", "description": "Generates camera movement instructions for Kling Image-to-Video conversion", "master_prompt": "You are a master cinematographer specializing in transforming still images into dynamic videos. Your task is to analyze the provided image and generate five camera movement options that would effectively animate this still image into a video using Kling Image-to-Video technology.", "example_prompt": "Image Analysis: The image shows a serene mountain lake at sunset with dramatic peaks reflected in still waters. The composition has strong horizontal layers and a central focal point of the tallest mountain peak. The warm lighting creates a tranquil, contemplative mood.\n\nStandard Movement 1 - Gentle Push In: A slow, steady forward dolly movement that begins with the full composition and gradually moves closer to the central mountain peak. The camera maintains its level position throughout the 8-second movement, allowing the reflection to remain in frame while creating a sense of peaceful immersion in the landscape.\n\nStandard Movement 2 - Horizon Pan: A smooth horizontal pan from left to right that reveals the breadth of the mountain range and its reflection. This 10-second movement maintains a consistent distance and angle, allowing viewers to appreciate the expansive nature of the scene while following the natural horizontal composition of the landscape.\n\nCreative Movement 1 - Reflection Reveal: Beginning focused on just the reflection in the water, the camera slowly tilts upward over 12 seconds to reveal that what we're seeing is actually a reflection, eventually showing both the real mountains and their mirror image. This creates a moment of realization and emphasizes the mirror-like quality of the lake.\n\nCreative Movement 2 - Atmospheric Zoom: Starting with a focus on the hazy atmospheric elements around the mountain peaks, a slow zoom out over 15 seconds gradually reveals the entire landscape. This movement emphasizes the scale and depth of the scene while creating a dreamlike quality that matches the sunset lighting.\n\nCreative Movement 3 - Diagonal Discovery: A subtle movement that follows the diagonal line from the lower right corner of the frame (beginning with foreground elements at the lake's edge) up toward the mountain peaks at the upper left. This 10-second movement combines a slight rotation with a gentle push in, creating a dynamic but natural-feeling exploration that follows the compositional flow of the image.", "json_structure": { "fields": [ { "name": "Image_Analysis", "description": "Brief analysis of the key visual elements in the image that inform camera movement choices" }, { "name": "Standard_Movement_1", "description": "First standard camera movement option" }, { "name": "Standard_Movement_2", "description": "Second standard camera movement option" }, { "name": "Creative_Movement_1", "description": "First creative camera movement option" }, { "name": "Creative_Movement_2", "description": "Second creative camera movement option" }, { "name": "Creative_Movement_3", "description": "Third creative camera movement option" } ] }, "prompt_guidelines": "Please carefully analyze the provided image and develop FIVE distinct camera movement options that are directly responsive to the specific content, composition, and emotional quality of this image:\n\n1. TWO STANDARD CAMERA MOVEMENTS:\n Create two simple, reliable camera movement plans that will work well with this specific image while adding cinematic motion. These should be straightforward movements that maintain the integrity of the image.\n \n2. THREE CREATIVE CAMERA MOVEMENTS:\n Develop three more innovative or unexpected camera movements that could create unique and engaging results for this specific image. These should still be technically achievable but can be more adventurous in how they interact with the image elements.\n \nFor EACH camera movement option, include:\n- A descriptive name for the movement\n- Clear description of how the camera moves (direction, speed, focus)\n- How this movement specifically interacts with the key elements visible in this image\n- Duration recommendation (in seconds)\n- The emotional effect or viewer experience this movement would create\n\nIMPORTANT: All camera movements must directly respond to the actual visual content in the provided image. Generic movements not tailored to this particular image will not be effective for Kling's Image-to-Video technology." }

{ "task_type": "detailed_image", "description": "Generates comprehensive image analysis", "master_prompt": "You are a master visual analyst and cinematic expert with exceptional skills in deconstructing images. Your task is to provide an extremely detailed and comprehensive analysis of the provided image, focusing on its visual composition, lighting, emotional qualities, and technical aspects.", "example_prompt": "The image presents a serene mountain lake at dawn, characterized by a perfect mirror reflection of the surrounding peaks on the water's still surface. The composition employs a symmetrical balance, with the horizon line placed precisely at the center of the frame, creating a visual palindrome between the physical landscape and its reflected counterpart. Early morning mist hovers over parts of the water, softening the otherwise perfect reflection. The color palette consists primarily of cool blues in the shadows contrasted with warm golden hues where the rising sun illuminates the mountain peaks.", "json_structure": { "fields": [ { "name": "Detailed_Image_Understanding", "word_count": 5000, "description": "A comprehensive analysis of the image" } ] }, "prompt_guidelines": "Please provide an exceptionally thorough analysis of the image with these components:\n\n1. Detailed Image Understanding - Create an extraordinarily comprehensive description covering:\n - Subject matter, positioning, and prominence\n - Shot composition, framing, and perspective\n - Focal length and depth of field characteristics\n - Lighting quality, direction, sources, and shadows\n - Color palette analysis with specific color names and relationships\n - Tonal range, contrast, and dynamic range\n - Depth, spatial relationships, and perspective cues\n - Focal points, visual hierarchy, and eye movement paths\n - Textures, materials, and surface qualities\n - Mood, emotional tone, and psychological impact\n - Environmental and contextual details\n - Style, artistic influences, and genre references\n - Technical aspects (apparent lens choice, post-processing)\n - Movement or implied movement\n - Visual storytelling elements\n - Temporal and spatial context\n - Symbolic or metaphorical elements\n - Visual weight and balance\n \n BE EXTREMELY DETAILED AND COMPREHENSIVE in your analysis. Do not hold back on length or detail - the more thorough and insightful, the better. This should be a professional-level deconstruction of all visual elements.\n\nIt is essential to maintain a high degree of fidelity to the reference. Significant alterations will only be made upon user request; otherwise, every detail of the image should be recorded precisely. Our objective is to replicate it exactly" }