Prompt Sub Routine Logical Computation Image

LOTA

The following was a simple logical test performed using Google Gemini 2.0 AI Studio Suite. Keep in mind, this is available to a use currently as normal humans. The individual behind this keyboard is nobody special, far from it. Messages come in many forms, God is ultimately in control. Common sense for people from the Waters of the Faith.

Let's proceed with this basic display of capabilities using a simple yet effective "structure" using what I like to call, "The Language of the Angels" or LOTA for short. More on this later.

Initial Prompt:

Elevate the language in the following prompt streamlining goals sharpening focus leading to actionable outcomes leave nothing out suggest available helpful sub routines:

PATH="vision/gemini_image_vision.py"

Step 1: Please analyze the following Google Gemini Image Coding integrations

https://ai.google.dev/gemini-api/docs/vi...ang=python

Step 2: Consolidate all code examples in Step 1



Step 3: Then generate Python Script with an interactive human menu using the following logical progression:

Welcome to Gemini Vision Integration Prompt

Image Tools

1) Select Local Image
Image Location (ie. /path/to/pic)
Additional Variables As Needed

Video Tools

1) Select Local Video

File Tools

Etc.

Before generating the above please:

Optimize the above prompt terminology for best coding results and logical outcome. If no further input is required from the user run processes. If additional data is required or precise clarification required prompt prior to execution herein.

Generated Reply:

Absolutely. Let's refine this prompt for clarity, precision, and optimal results, while also outlining potential subroutines.

Refined Prompt:

"Objective: Develop a comprehensive, interactive Python script (gemini_image_vision.py) utilizing the Google Gemini Image Vision API. The script must provide a user-friendly menu for image and video processing, incorporating all functionalities demonstrated in the official documentation.

Scope:

Documentation Analysis:


Thoroughly parse the Google Gemini Image Vision API documentation (https://ai.google.dev/gemini-api/docs/vi...ang=python). Extract and synthesize all provided code examples, ensuring complete feature coverage.

Code Consolidation:

Aggregate the extracted code snippets into a cohesive and functional codebase. Implement robust error handling and input validation.

Interactive Menu Implementation:

Construct a command-line interface with a clear, hierarchical menu structure.

Provide options for:

Image Processing:

"Select Local Image":

Prompt for the local image file path.

Allow for the input of any necessary additional parameters as per the API's capabilities (e.g., content types, safety settings).

Implement the ability to show the image in the console if possible.

Video Processing:

"Select Local Video":

Prompt for the local video file path.

Allow for any needed additional parameters.

Implement the ability to show a frame of the video in the console if possible.

File Tools:

Expandable section for future file-related operations (e.g., batch processing, output management).

Utilize clear prompts and user feedback to guide the interaction.

Output:

Generate a single Python script (vision/gemini_image_vision.py) containing the complete implementation.

Ensure the code is well commented and easily maintainable.

Clarification Request (if needed):

If any ambiguity or missing information is encountered during documentation analysis or code consolidation, request clarification before proceeding.

Subroutine Suggestions:

analyze_documentation(url):

Purpose: Fetches and parses the API documentation.

Output: A structured representation of the code examples and API functionalities.

consolidate_code(code_snippets):

Purpose: Merges the extracted code snippets into a unified codebase.

Output: A functional Python script.

create_image_menu():

Purpose: handles all image related user interactions.

Output: the results of the image processing.

create_video_menu():

Purpose: handles all video related user interactions.

Output: the results of the video processing.

get_user_input(prompt, validation_function=None):

Purpose: Prompts the user for input and optionally validates it.

Output: Validated user input.

display_results(results):

Purpose: formats and presents the API's response to the user.

Output: displayed results.

display_image(image_path):

Purpose: displays a local image in the console if possible.

Output: displayed image.

display_video_frame(video_path):

Purpose: displays a single frame of a local video in the console if possible.

Output: displayed frame.

error_handling(error):

Purpose: handle any errors that occur.

Output: displayed error to the user.

Key Improvements:

Precise Language: Replaced vague terms with specific actions (e.g., "parse," "synthesize," "aggregate").
Clear Structure: Organized the prompt into logical sections (Objective, Scope, Output, Clarification).
Explicit Requirements: Defined the expected output (a single Python script) and emphasized error handling.
Subroutine Suggestions: Provided a blueprint for modular code design.
Focus on User Experience: Emphasized the importance of a user-friendly menu and clear prompts.
Error Handling: Added in explicit error handling.

By using this refined prompt, you should obtain a more accurate and robust Python script."

My simple response: Please proceed with the refined prompt

AI Output:

Python


"""vision/gemini_image_vision.py"""

import google.generativeai as genai
import os
import io
from PIL import Image
import cv2

# Configure your API key (replace with your actual API key)
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

model = genai.GenerativeModel('gemini-pro-vision')

def analyze_documentation(url):
"""Placeholder: In a real implementation, this would fetch and parse the API documentation."""
# In a real implementation, you'd use requests and BeautifulSoup (or similar)
# to scrape and parse the documentation.
print(f"Analyzing documentation: {url}")
return {} # Return a placeholder for now

def consolidate_code(code_snippets):
"""Placeholder: In a real implementation, this would merge code snippets."""
# In a real implementation, this would combine the extracted code examples.
print("Consolidating code snippets.")
return {} # Return a placeholder for now

def get_user_input(prompt, validation_function=None):
"""Prompts the user for input and optionally validates it."""
while True:
user_input = input(prompt)
if validation_function:
try:
if validation_function(user_input):
return user_input
except ValueError as e:
print(f"Invalid input: {e}")
else:
return user_input

def display_results(results):
"""Formats and presents the API's response to the user."""
print("API Response:")
if hasattr(results, "text"):
print(results.text)
else:
print(results)

def display_image(image_path):
"""Displays a local image in the console if possible."""
try:
img = Image.open(image_path)
img.show()
except Exception as e:
print(f"Could not display image: {e}")

def display_video_frame(video_path):
"""Displays a single frame of a local video in the console if possible."""
try:
cap = cv2.VideoCapture(video_path)
ret, frame = cap.read()
if ret:
img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
img.show()
else:
print("Could not read video frame.")
cap.release()
except Exception as e:
print(f"Could not display video frame: {e}")

def error_handling(error):
"""Handles any errors that occur."""
print(f"An error occurred: {error}")

def create_image_menu():
"""Handles all image-related user interactions."""
image_path = get_user_input("Enter the local image path: ")
try:
display_image(image_path)
with open(image_path, "rb") as image_file:
img_data = image_file.read()

contents = [{"mime_type": "image/jpeg", "data": img_data}]

prompt_text = get_user_input("Enter a prompt for the image: ")

try:
response = model.generate_content([prompt_text, contents])
display_results(response)
except Exception as e:
error_handling(e)

except FileNotFoundError:
print(f"Error: Image not found at {image_path}")
except Exception as e:
error_handling(e)

def create_video_menu():
"""Handles all video-related user interactions."""
video_path = get_user_input("Enter the local video path: ")
try:
display_video_frame(video_path)

# Basic video processing (example: get a single frame and send it)
cap = cv2.VideoCapture(video_path)
ret, frame = cap.read()
cap.release()

if ret:
_, img_encoded = cv2.imencode('.jpg', frame)
img_data = img_encoded.tobytes()

contents = [{"mime_type": "image/jpeg", "data": img_data}]

prompt_text = get_user_input("Enter a prompt for the video frame: ")

try:
response = model.generate_content([prompt_text, contents])
display_results(response)
except Exception as e:
error_handling(e)

else:
print("Could not read video frame.")
except FileNotFoundError:
print(f"Error: Video not found at {video_path}")
except Exception as e:
error_handling(e)

def main():
"""Main function to run the interactive menu."""
while True:
print("\nWelcome to Gemini Vision Integration Prompt")
print("Image Tools")
print("1) Select Local Image")
print("Video Tools")
print("2) Select Local Video")
print("3) Exit")

choice = get_user_input("Enter your choice: ")

if choice == "1":
create_image_menu()
elif choice == "2":
create_video_menu()
elif choice == "3":
break
else:
print("Invalid choice. Please try again.")

if __name__ == "__main__":
main()



Key Improvements in this Code:
Modular Structure: The code is broken down into functions, as suggested in the subroutines.
Error Handling: Basic try...except blocks are included for file operations and API calls.
User Input: The get_user_input function ensures consistent input handling.
Image and Video Display: display_image and display_video_frame attempt to show images and video frames.
API Interaction: The code demonstrates how to send image data to the Gemini API and display the results.
Menu System: A basic command-line menu is implemented.
Placeholders: The analyze_documentation and consolidate_code functions are placeholders, as fully implementing them would require external libraries and web scraping.
To run this code:
Install Required Libraries:
Bash
pip install google-generativeai Pillow opencv-python


Set Your API Key:
Set your Google API key as an environment variable:
Bash
export GOOGLE_API_KEY="YOUR_API_KEY"
(Replace "YOUR_API_KEY" with your actual key.)
Run the Script:
Bash
python vision/gemini_image_vision.py


Important Notes:
This code provides a basic framework. You'll need to expand it based on your specific needs and the full capabilities of the Gemini API.
The video display functionality requires OpenCV.
The console image display relies on Pillow's Image.show(), which might open images in an external viewer.
For more complex video processing (e.g., frame-by-frame analysis), you'll need to implement more advanced OpenCV logic.
For a production level application you would need to add better error handling, and much more extensive validation of user inputs.

The process herein demonstrates how a simple idiot like me can accomplish a lot, using simple logic. Does this remind us of somebody in Star Trek? I would have to say that's an affirmative.

For purposes of this thread I have left out many observations to be discussed if time permits in the advanced section of this forum.

Final output using Pycharm IDE (Choose your poison here)