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| import base64 | |
| from PIL import Image | |
| import pytesseract | |
| import google.generativeai as genai | |
| import os | |
| # Step 1: Configure the Gemini API | |
| GEMINI_API_KEY = os.getenv('GEMINI') | |
| genai.configure(api_key=GEMINI_API_KEY) | |
| # Initialize the Gemini model | |
| gemini_model = genai.GenerativeModel('gemini-1.5-flash') | |
| # Step 2: Function to convert the image to base64 | |
| def convert_image_to_base64(file_path): | |
| try: | |
| with open(file_path, "rb") as image_file: | |
| encoded_string = base64.b64encode(image_file.read()).decode() | |
| return encoded_string | |
| except FileNotFoundError: | |
| print(f"File {file_path} not found. Please check the path and try again.") | |
| return None | |
| # Step 3: Function to extract text from the image using OCR (pytesseract) | |
| def extract_text_from_image(file_path): | |
| try: | |
| image = Image.open(file_path) | |
| extracted_text = pytesseract.image_to_string(image) | |
| print("\nExtracted Text from the Image:") | |
| print(extracted_text) | |
| return extracted_text | |
| except Exception as e: | |
| print(f"Error processing the image file: {e}") | |
| return None | |
| # Step 4: Function to analyze the health report using the Gemini model | |
| def analyze_health_report(encoded_image, extracted_text): | |
| prompt = f""" | |
| Analyze the following health report image and provide: | |
| 1. Key findings in the report. | |
| 2. Explanation of findings in report. | |
| 3. Recommendations for lifestyle changes or immediate actions. | |
| 4. Suggested doctor specialty to consult. | |
| Extracted Text from the Report: | |
| {extracted_text} | |
| """ | |
| try: | |
| response = gemini_model.generate_content(prompt) | |
| # Display the AI analysis result | |
| if response: | |
| response_text = response.text.strip() | |
| print("\n### AI Analysis and Recommendations ###") | |
| print(response_text) | |
| else: | |
| print("No response received from the AI model.") | |
| except Exception as e: | |
| print(f"Error while analyzing the report: {e}") | |
| # Main Execution Flow | |
| def main(): | |
| import argparse | |
| # Argument parser for input file | |
| parser = argparse.ArgumentParser(description="Health Report Analyzer") | |
| parser.add_argument("image_file", type=str, help="Path to the health/laboratory report image") | |
| args = parser.parse_args() | |
| # Convert the image to base64 | |
| encoded_image = convert_image_to_base64(args.image_file) | |
| if not encoded_image: | |
| return | |
| # Extract text from the image using OCR | |
| extracted_text = extract_text_from_image(args.image_file) | |
| if not extracted_text: | |
| return | |
| # Analyze the health report | |
| analyze_health_report(encoded_image, extracted_text) | |
| if __name__ == "__main__": | |
| # Ensure pytesseract is available | |
| try: | |
| pytesseract.get_tesseract_version() | |
| except EnvironmentError: | |
| print("pytesseract is not installed or configured. Install it using `sudo apt install tesseract-ocr` or equivalent.") | |
| exit(1) | |
| main() | |