AFG-Gumball 0.2.0

A Python package for processing medical X-ray images and AI-based diagnosis

AFG_Gumball Module Documentation

Overview

The AFG_Gumball module is a Python package designed for medical image processing and AI-assisted diagnosis, specifically for X-ray images. The module provides tools for X-ray image preprocessing, pathology detection, body part segmentation, and AI-assisted medical diagnosis using the Gemini AI backend. The module is divided into two submodules: xray_processing for image processing tasks and medical_ai for AI-based patient interaction and diagnosis functions.

Module Structure

project/
├── AFG_Gumball/
│ ├── __init__.py
│ ├── __init__.pyi
│ ├── xray_processing/
│ │ ├── __init__.py
│ │ ├── image_loader.py
│ │ ├── gradcam.py
│ │ ├── segmentation.py
│ │ ├── model_utils.py
│ │ └── enums.py
│ ├── medical_ai/
│ │ ├── __init__.py
│ │ ├── gemini_client.py
│ │ ├── patient_ai.py
│ │ ├── doctor_diagnostic_ai.py
│ │ ├── doctor_enhance_ai.py
│ │ └── xray_analysis_expert_ai.py

• xray_processing: Handles X-ray image loading, preprocessing, pathology detection with Grad-CAM heatmaps, and body part segmentation.
• medical_ai: Provides AI-based functions for patient interaction, medical record creation, diagnosis validation, and advanced X-ray image analysis using the Gemini AI API.

Installation

Requirements

• Python 3.8 or higher
• Required libraries:

pip install torch torchvision numpy matplotlib scikit-image pillow python-dotenv google-generativeai>=0.8.0 torchxrayvision

• A valid Gemini API key stored in the .env file named GEMINI_API_KEY.

Setup

1. Download or copy the AFG_Gumball module to your project directory.

2. Make sure the directory structure matches the structure above.

3. Create a .env file in the project root directory with the Gemini API key:

GEMINI_API_KEY=your_api_key_here

4. Install the dependent libraries:

pip install -r requirements.txt

(Create a requirements.txt file with the above libraries if needed.) 5. Run the scripts in the package context to ensure relative imports work:

python -m project.your_script

Instructions

1. Submodule xray_processing

This submodule provides tools for X-ray image processing, including loading, pathology detection, Grad-CAM heatmap generation, and body part segmentation.

Main Components

• Import:

from AFG_Gumball.xray_processing import load_xray_image, process_xray_image, compute_gradcam, get_body_part_segment, BodyPart

• Functions:

• load_xray_image(image_input: Union[str, bytes]) -> torch.Tensor: Load and preprocess X-ray images from file path or bytes data. Return tensor of size [1, 512, 512].

• process_xray_image(img_path: str) -> Tuple[List[Tuple[str, float]], List[Dict[str, Any]]]: Process X-ray images to detect pathologies (probability > 0.5) and create Grad-CAM heatmaps. Returns a tuple containing a list of pathologies and a list of heatmap dictionaries.

• compute_gradcam(model: torch.nn.Module, img_tensor: torch.Tensor, target_class_idx: int) -> np.ndarray: Generates a Grad-CAM heatmap for a given pathology. Returns a combined image array.

• get_body_part_segment(image: torch.Tensor, part: BodyPart) -> torch.Tensor: Segments a given body part from an X-ray image using the PSPNet model. Returns a tensor of size [512, 512].

• Enum:

• BodyPart: Enum for body parts (e.g. LEFT_LUNG, HEART, SPINE).

Example

from AFG_Gumball.xray_processing import load_xray_image, process_xray_image, get_body_part_segment, BodyPart

# Load and preprocess X-ray images
image_tensor = load_xray_image("path/to/xray.jpg")

# Process images to detect pathologies
pathologies, gradcam_images = process_xray_image("path/to/xray.jpg")
for pathology, prob in pathologies:
print(f"Pathology: {pathology}, Probability: {prob:.2f}")

# Heart segmentation
heart_segment = get_body_part_segment(image_tensor, BodyPart.HEART)
print(heart_segment.shape) # torch.Size([512, 512])

2. Submodule medical_ai

This submodule provides AI-based patient diagnostic and interaction tools, using the Gemini AI API for natural language processing and image analysis.

Main Components

• Import:

from AFG_Gumball.medical_ai import GeminiAI, PatientAI, DoctorDiagnosticAI, DoctorEnhanceAI, XrayAnalysisExpertAI

• Classes:
• GeminiAI: Manages interactions with the Gemini AI API, including file uploads and content creation.
• PatientAI: Provides patient-friendly answers and diagnoses based on X-rays and symptoms.
• DoctorDiagnosticAI: Generates medical records, treatment suggestions, and inferences from symptoms for professional purposes.
• DoctorEnhanceAI: Improves medical records and validates diagnoses.
• XrayAnalysisExpertAI: Analyzes original X-ray images and symptoms, returning JSON results with diagnosis and abnormal areas.

Example: Load and analyze X-ray images

from AFG_Gumball.medical_ai import PatientAI, XrayAnalysisExpertAI

# Load X-ray images
image_path = "path/to/xray.jpg"

# Patient-friendly diagnosis
patient_ai = PatientAI()
with open(image_path, "rb") as f:
image_bytes = f.read()
diagnosis, heatmaps = patient_ai.diagnose_images(
image_bytes_list=[image_bytes],
symptoms="Prolonged cough",
include_symptoms=True,
include_xray_image=True
)
print(f"Diagnosis: {diagnosis}")

# In-depth analysis
expert_ai = XrayAnalysisExpertAI()
result = expert_ai.analyze_xray(
image_paths=[image_path],
symptoms="Prolonged cough"
)
print(f"Analysis result: {result}")

Note when upload image

• Image format: X-ray images must be in JPEG format.
• How ​​to upload images:
• For PatientAI.diagnose_images:

with open("path/to/xray.jpg", "rb") as f:
image_bytes = f.read()

The image_bytes data is passed in as a list ([image_bytes]).

• For DoctorDiagnosticAI.create_medical_record and XrayAnalysisExpertAI.analyze_xray:

image_paths = ["path/to/xray.jpg"]

• Upload images to Gemini AI:
• Use the file upload method to upload images first:

import google.generativeai as genai
genai.configure(api_key="your_api_key")
response = genai.upload_file("path/to/xray.jpg", mime_type="image/jpeg")
file_uri = response.uri

• Pass the URI to generate_content with the correct structure:

contents = [
{
"role": "user",
"parts": [
{"file_data": {"file_uri": file_uri, "mime_type": "image/jpeg"}},
{"text": "X-ray image analysis."}
]
}
]
model = genai.GenerativeModel("gemini-2.0-flash")
response = model.generate_content(contents)

• Multiple image processing: Methods that support image lists, up to 5 images.

3. Combining Submodules

The medical_ai submodule integrates with xray_processing to create comprehensive workflows. For example, PatientAI and DoctorDiagnosticAI use process_xray_image to analyze X-ray images before generating diagnoses or medical records.

Workflow example

from AFG_Gumball.medical_ai import PatientAI, DoctorDiagnosticAI
from AFG_Gumball.xray_processing import process_xray_image

# X-ray image processing
pathologies, gradcam_images = process_xray_image("path/to/xray.jpg")

# Create medical record
doctor_ai = DoctorDiagnosticAI()
record = doctor_ai.create_medical_record(
patient_info="Male, 50 years old",
symptoms="Prolonged cough, shortness of breath",
image_paths=["path/to/xray.jpg"],
include_xray_image=True
)
print(f"Medical record: {record}")

Note

• Gemini API Key: Make sure GEMINI_API_KEY is set in .env file to use the functions of medical_ai.

• Image format: X-ray images must be in JPEG format for processing in PatientAI and XrayAnalysisExpertAI. Use PIL to check and convert formats if necessary:

from PIL import Image
img = Image.open("path/to/xray.png").convert("L")
img.save("xray.jpg", format="JPEG")

• Library version: Make sure to use google-generativeai>=0.8.0 to support file uploads and format contents with role.

• Contents format: Each element in contents must have a role (user or model) and parts containing a list of text or file_data. For example:

contents = [{"role": "user", "parts": [{"text": "Prompt"}]}]

• Relative imports: Run the script in the package context (e.g. python -m project.your_script) to avoid import errors.
• Error handling: All functions and methods include input checks and provide detailed error messages for invalid input.