Logic Visualizer | JavaScript

Flask Application Logic Flowchart

This document visualizes the logic and structure of a Flask app that generates random data using OpenAI's API. It details components like initialization, route handling, data generation, and error management through a comprehensive


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Prompt

from flask import Flask, render_template, request, send_file, jsonify, session import csv import io from io import BytesIO from io import StringIO import random import openai import os import traceback import logging import faker

app = Flask(name)

Set your OpenAI API key

openai.api_key = os.environ['OPENAIDEMO'] openai.api_key = os.getenv('OPENAIDEMO') if not openai.api_key: raise ValueError("OPENAIDEMO environment variable is not set. Please set it in the Secrets tab.")

print(f"API Key set: {'Yes' if openai.api_key else 'No'}") print(f"API Key length: {len(openai.api_key) if openai.api_key else 0}")

def extract_python_code(response): lines = response.split('\n') code_lines = [] in_code_block = False for line in lines: if line.strip().startswith('python'): in_code_block = True continue elif line.strip() == '' and in_code_block: in_code_block = False continue if in_code_block: code_lines.append(line) return '\n'.join(code_lines)

def generate_python_code(scenario, num_columns): prompt = f""" Create a Python function named 'generate_random_data' that generates random data for the following business scenario: {scenario}

The function should:
1. Take a 'num_rows' parameter
2. Generate exactly {num_columns} columns of data
3. Return a list of dictionaries, where each dictionary represents a row of data
4. Include appropriate field names and data types for the scenario
5. Use the random module to generate data

Provide ONLY the Python code for the function, enclosed in triple backticks, without any additional explanation.
"""
try:
    response = openai.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": "You are a Python code generator. Respond only with the requested Python code, enclosed in triple backticks."},
            {"role": "user", "content": prompt}
        ]
    )
    full_response = response.choices[0].message.content
    return extract_python_code(full_response)
except Exception as e:
    logging.error(f"Error in generating code: {str(e)}")
    return None

logging.basicConfig(level=logging.DEBUG)

@app.route('/', methods=['GET', 'POST']) def index(): if request.method == 'POST': logging.info(f"Received POST request with scenario: {request.form['scenario']}") try: scenario = request.form['scenario'] num_rows = int(request.form['num_rows']) num_columns = int(request.form['num_columns']) logging.info(f"Received request: scenario='{scenario}', num_rows={num_rows}") generated_code = generate_python_code(scenario, num_columns) logging.debug(f"Generated code: {generated_code}") if generated_code and isinstance(generated_code, str): try: exec(generated_code, globals()) if 'generate_random_data' not in globals(): raise NameError("The function 'generate_random_data' was not defined.") # Check if this is a preview request if 'preview' in request.form: preview_data = globals().get('generate_random_data', lambda num_rows: [])(5) # Generate 5 rows for preview return jsonify({'success': True, 'preview': preview_data}) # If not a preview, generate full data set data = globals().get('generate_random_data', lambda num_rows: [])(num_rows) logging.info(f"Generated {len(data)} rows of data") output = StringIO() writer = csv.DictWriter(output, fieldnames=data[0].keys()) writer.writeheader() writer.writerows(data) output.seek(0) return send_file(io.BytesIO(output.getvalue().encode()), mimetype='text/csv', as_attachment=True, download_name='data_scenario.csv') except Exception as e: logging.error(f"Error in executing generated code: {str(e)}") logging.error(traceback.format_exc()) return jsonify({'error': f'Error in executing generated code: {str(e)}'}), 500 else: logging.error("Failed to generate code") return jsonify({'error': 'Failed to generate code'}), 500 except Exception as e: logging.error(f"Unexpected error: {str(e)}") logging.error(traceback.format_exc()) return jsonify({'error': f'Unexpected error: {str(e)}'}), 500 else: # For GET requests, retrieve the selected scenario from the session selected_scenario = session.get('selected_scenario', '') return render_template('index.html', scenario=selected_scenario)

@app.route('/scenario-browser') def scenario_browser(): return render_template('scenario_browser.html') @app.route('/set-scenario', methods=['POST']) def set_scenario(): data = request.json scenario = data.get('scenario') # Store the scenario in session session['selected_scenario'] = scenario return jsonify({"success": True})

if name == 'main': app.run(host='0.0.0.0', port=8080, debug=True)

Answer

Visual Representation of Flask Application Logic

Overview

This document provides a visual representation of the logic and structure of a Flask web application. The application generates random data based on user input, utilizing OpenAI's API to produce Python code. The design includes route handling, data generation, and error management.

Flowchart Representation

Main Components

  • Flask App Initialization
  • Routes and Request Handling
  • Data Generation Logic
  • Error Logging and Handling

Flowchart

+----------------------------------------------------------+
|                  Flask Application (app)                 |
+----------------------------------------------------------+
|      (Init Performed)                                    |
|                                                          |
|  +--- Set OpenAI API key (from environment variable) ---+ 
|  |                                                       |
|  +---- Check for API key validity (raise error if not) |
|                                                          |
+---------------------------+------------------------------+
                            |
                            v
                +-------------------------+
                |      Route: '/'        |
                | (index function)       |
                +-------------------------+
                |  Handle GET and POST   |
                |   requests here        |
                +-------------------------+
                          |
         +----------------+-----------------+
         |                                  |
      (POST)                           (GET)
         |                                  |
         v                                  v
+--------------------------+         +---------------------------+
|  Retrieve Form Data     |         |    Render HTML Template    |
|  'scenario', 'num_rows',|         |    with selected scenario  |
|  'num_columns'          |         +---------------------------+
+--------------------------+
         |
         v
+---------------------------+
|   Generate Random Data    |
|  via OpenAI API (function)|
+---------------------------+
|  - Create Prompt          |
|  - Call OpenAI API        |
|  - Extract and Execute     |
|    Python Code            |
+---------------------------+
         |
        / \
       /   \ 
      /     \            
     v       v             
   Success    Error
     |          |
     |          |       
   +-------------------------+  
   |   Execute Generated Code |     
   |    (using exec())       |
   +-------------------------+
          |
      +---+---+
      |       |
     Data   Error
     |         |
+-------------------------+
|  Write Data to CSV     |
|  - Use StringIO for     |
|    in-memory file       |
+-------------------------+
           |
           v
+--------------------------+
|   Send CSV file to user  |
|  (send_file response)    |
+--------------------------+

Explanation of Key Components

  1. Flask App Initialization:

    • The application begins by importing necessary libraries and initializing the Flask application.
    • An API key is set from the environment. An error is raised if the key is not available.
  2. Route Handling:

    • The index() function handles both GET and POST requests.
    • On GET, it returns the HTML template.
    • On POST, it processes form submissions and captures scenario inputs.
  3. Data Generation:

    • The application generates Python code via OpenAI's API based on user-provided scenarios and column numbers.
    • The code is extracted and executed to create a function that generates random data.
    • The generate_random_data function's output is processed as per the request type (for previews or full datasets).
  4. Error Handling and Logging:

    • The application logs information and errors throughout the execution process.
    • Proper error messages are returned to the frontend in case of any exceptions.

Conclusion

This visualization encapsulates the structure and flow of the provided Flask application that integrates user inputs for dynamic data generation. By following this flowchart, one can gain insights into how the application processes requests, generates code, and handles errors effectively.

For further understanding of similar concepts, consider exploring courses available on the Enterprise DNA Platform.

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Description

This document visualizes the logic and structure of a Flask app that generates random data using OpenAI's API. It details components like initialization, route handling, data generation, and error management through a comprehensive flowchart.