This project demonstrates an integrated pipeline that combines Optical Character Recognition (OCR), data extraction, machine learning analysis, and natural language interaction using ChatGPT. It begins by leveraging pytesseract and Pillow to extract text from images, making it possible to digitize and interpret printed text from various documents. The extracted text is then processed using regular expressions to identify and extract specific information, such as dates formatted in MM/DD/YYYY. This structured data is crucial for subsequent analysis and is collected in a manner that allows for easy extension and customization to include other relevant entities.
Following the data extraction, a pre-trained machine learning model is employed to analyze the data and provide predictions. This analysis step is facilitated by joblib for loading the model and custom feature engineering functions tailored to the model's requirements. The insights derived from the machine learning model are then enriched through interaction with the OpenAI GPT-3 model via the openai library. This interaction enables a conversational AI response that provides deeper insights based on the extracted data and predictions. The entire process is encapsulated in the main function, which orchestrates these components to offer a seamless workflow from image processing to insightful reporting, making it a powerful tool for document analysis and interactive data interpretation.
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