Welcome to the Slow Paced Faster innovation on Text to Code Generative AI Agentic Models
Recently I published this AIAgenticOptimizedCodeLLM model using python script
To speed up innovative code generative models into the optimized code or improvement code
this below text to optimized code generative model will help you stay ahead of the game
Simple process flow
Step 1: Developer Created ML pipeline workflow model code
Step 2: From that code gaining the ML model metric related insights
Step 3: Save ML model metric details in text file and ML pipeline code in .py python file
Step 4: Ingest step 3 text file, .py python file and then user end can able prompt further more improved / optimized code versions
Step 5: New optimized code as well as ML model metric improvement related suggestion also provided into html responses.
领英推荐
Step 6: Loop back from Step 1 Until you get satisfied on high quality ML model metrics data
I used Google's Gemini Flash 2.0 API calls to get these details, in future versions, will have multiple draft generative code responses from google gemma, deekseek r1, any other LLM models !!
this is the below code available in that kaggle ml model --> Andrometocs | AIAgenticOptimizedCodeLLM | Kaggle
import os
from kaggle_secrets import UserSecretsClient
from google import genai
from IPython.display import display, HTML
class AIAgenticOptimizedCodeLLM:
def __init__(self, api_key, metrics_file_path, code_file_path, optimized_html_file_path):
"""
Initialize the optimizer with the API key and file paths.
"""
os.environ["GOOGLE_API_KEY"] = api_key
self.client = genai.Client()
self.metrics_file_path = metrics_file_path
self.code_file_path = code_file_path
self.optimized_html_file_path = optimized_html_file_path
def read_metrics(self):
"""
Read and return the contents of the metrics file.
"""
with open(self.metrics_file_path, "r", encoding="utf-8") as file:
return file.read()
def read_code(self):
"""
Read and return the contents of the original code file.
"""
with open(self.code_file_path, "r", encoding="utf-8") as file:
return file.read()
def generate_html_response(self, model_id, prompt):
"""
Uses the GenAI client to generate content based on the provided prompt.
"""
response = self.client.models.generate_content(
model=model_id,
contents=prompt
)
return response.text # Returns the generated HTML content
def write_optimized_html(self, html_content):
"""
Write the generated HTML content to the output file.
"""
with open(self.optimized_html_file_path, "w", encoding="utf-8") as file:
file.write(html_content)
def optimize_model_code(self, user_prompt):
"""
Combine metrics, code, and user instructions into a single HTML prompt.
Instruct the model to produce a complete HTML page that includes:
- Analysis of the current pipeline and bottlenecks.
- Detailed suggestions for reducing RMSE and improving generalization.
- Optimized code (if applicable) in formatted code blocks.
"""
metrics = self.read_metrics()
code = self.read_code()
# Create an HTML template as the combined prompt.
combined_prompt = f"""<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>Optimized Model Code and Suggestions</title>
<style>
body {{
font-family: Arial, sans-serif;
background-color: #f4f4f4;
color: #333;
margin: 20px;
}}
.container {{
max-width: 800px;
margin: auto;
background-color: #fff;
padding: 20px;
border-radius: 8px;
box-shadow: 0 0 10px rgba(0,0,0,0.1);
}}
h2 {{
color: #0056b3;
border-bottom: 2px solid #0056b3;
padding-bottom: 5px;
margin-top: 20px;
}}
pre {{
background-color: #f4f4f4;
color: #333;
padding: 10px;
border-radius: 5px;
overflow-x: auto;
}}
code {{
font-family: monospace;
}}
ul {{
margin-left: 20px;
}}
</style>
</head>
<body>
<div class="container">
<h2>Metrics</h2>
<pre>{metrics}</pre>
<h2>Original Code</h2>
<pre><code>{code}</code></pre>
<h2>Instructions</h2>
<pre>{user_prompt}</pre>
<p>Please provide your response as a complete HTML page with the following sections:</p>
<ul>
<li><strong>Analysis:</strong> A detailed analysis of the current pipeline and any bottlenecks.</li>
<li><strong>Suggestions:</strong> Detailed suggestions for reducing RMSE and ensuring the model generalizes well.</li>
<li><strong>Optimized Code:</strong> An improved version of the code incorporating these suggestions.</li>
</ul>
<p>Your response should be a complete, self-contained HTML document.</p>
</div>
</body>
</html>
"""
model_id = "gemini-2.0-flash-exp"
try:
html_response = self.generate_html_response(model_id, combined_prompt)
print("HTML Response:")
print(html_response)
self.write_optimized_html(html_response)
print(f"Optimized HTML saved to {self.optimized_html_file_path}")
return html_response
except Exception as e:
print(f"An error occurred: {e}")
return None
Using this Kaggle ML model, utilized in these two notebooks
On any competitive landscapes this will be pretty much useful
Happy to build many ML/ LLM models in upcoming days !!