| import pytesseract | |
| import pandas as pd | |
| import re | |
| def extract_text(image): | |
| """ | |
| Extract text from the image using Tesseract. | |
| return pytesseract.image_to_string(image) | |
| def clean_and_parse_extracted_text(raw_text): | |
| """ | |
| Parse and clean the raw text to extract structured data. | |
| """ | |
| # Split the text into lines and clean up | |
| lines = raw_text.split("\n") | |
| lines = [line.strip() for line in lines if line.strip()] | |
| # Identify and extract rows with valid components | |
| data = [] | |
| for line in lines: | |
| # Match rows containing numeric ranges and values | |
| match = re.match( | |
| r"^(.*?)(\d+(\.\d+)?)(\s*-?\s*\d+(\.\d+)?\s*-?\s*\d+(\.\d+)?)?\s*([a-zA-Z/%]+)?\s*(H|L|Normal)?$", | |
| line, | |
| unit = match.group(7) | |
| flag = "Normal" # Default flag | |
| # Determine the flag based on value and range | |
| if min_val is not None and max_val is not None: | |
| if value < min_val: | |
| flag = "L" | |
| elif value > max_val: | |
| flag = "H" | |
| # Only append the data if the flag is abnormal (L or H) | |
| if flag != "Normal": | |
| data.append([component, value, min_val, max_val, unit, flag]) | |
| # Create a DataFrame | |
| df = pd.DataFrame(data, columns=["Component", "Your Value", "Min", "Max", "Units", "Flag"]) | |
| # Fix misspellings and inconsistencies (if any known issues exist) | |
| correction_map = { | |
| "emoglobin": "Hemoglobin", | |
| "ematocrit": "Hematocrit", | |
| return df | |
| def display_results(df): | |
| """ | |
| Display the flagged abnormalities in a table format. | |
| """ | |
| st.dataframe(df, use_container_width=True) | |
| # Streamlit app | |
| st.title("Blood Report Analyzer") | |
| st.write("Upload an image of a blood test report to analyze.") | |
| uploaded_file = st.file_uploader("Upload Image", type=["png", "jpg", "jpeg"]) | |
| # Parse the extracted text into a structured format | |
| parsed_data = clean_and_parse_extracted_text(extracted_text) | |
| # Display the structured data (only abnormalities) | |
| st.subheader("Flagged Abnormalities") | |
| display_results(parsed_data) | |
| except Exception as e: |