IV. Results

To achieve the objective of this research, a methodology involving pre-processing the uploaded flowchart image, text extraction from the flowchart image using OCR, code generation with large language model inference was implemented. A diverse test dataset of flowchart images representing various programming tasks was created. This dataset was used for evaluating the created application. Python codes generated for the test flowchart images were executed in a code editor and the outputs produced were checked. Quality of the Python codes generated for the test flowchart images was evaluated for their accuracy, syntactic correctness, and adherence to best programming practices. Codes generated for 75% of the flowchart images from the test dataset executed without any compile time and runtime errors. Best programming practices were followed in 64% of the generated python codes for test flowcharts. The experiments demonstrated promising results, with the application successfully generating Python code and its brief explanation from flowchart images with a high degree of accuracy. It was observed that the performance of the application varied depending on the complexity of the flowchart and the clarity of the flowchart images. However, even for 62% of challenging cases where uploaded flowchart images were not clear and the text in the flowchart components wasn’t clearly visible, The application was able to generate the correct python codes.