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import tensorflow as tf
import random
from PIL import Image
from tensorflow import keras
import numpy as np
import os
import logging
from tensorflow.keras.preprocessing import image as keras_image
from huggingface_hub import from_pretrained_keras
from openai import AzureOpenAI
import gradio as gr
from zipfile import ZipFile
logging.basicConfig(level=logging.INFO)
class DiseaseDetectionApp:
def __init__(self):
self.class_names = [
"Normal",
"PNEUMONIA",
]
keras.utils.set_random_seed(42)
self.model = from_pretrained_keras("ryefoxlime/PneumoniaDetection")
self.client=AzureOpenAI()
def predict_disease(self, image_path):
"""
Predict the disease present in the X-Ray image.
Args:
- image_data: PIL image data
Returns:
- predicted_disease: string
"""
try:
# Load the image file, resizing it to the dimensions expected by the model
img = keras_image.load_img(image_path, target_size=(224, 224)) # Adjust target_size according to your model's expected input dimensions
# Convert the image to a numpy array
img_array = keras_image.img_to_array(img)
# Add an additional dimension to the array: (1, height, width, channels)
img_array = tf.expand_dims(img_array, 0) # Model expects a batch of images, but we're only passing a single image
# Make predictions
predictions =self.model.predict(img_array)
# Extract the predicted class and confidence
predict_class = self.class_names[np.argmax(predictions[0])]
# confidence = round(100 * np.max(predictions[0]), 2)
return predict_class
except Exception as e:
logging.error(f"Error predicting disease: {str(e)}")
return None
def classify_disease(self,image_path):
disease_name=self.predict_disease(image_path)
print(disease_name)
if disease_name=="PNEUMONIA":
conversation = [
{"role": "system", "content": "You are a medical assistant"},
{"role": "user", "content": f""" your task describe(classify) about the given disease as summary only in 3 lines.
```{disease_name}```
"""}
]
# Generate completion using ChatGPT model
response = self.client.chat.completions.create(
model="ChatGPT",
messages=conversation,
temperature=0,
max_tokens=1000
)
# Get the generated topics message
result = response.choices[0].message.content
return disease_name,result
elif disease_name=="Normal":
result="No problem in your xray image"
return disease_name,result
else:
logging.error("Error classify_disease disease")
return "Something went wrong"
def unzip_image_data(self):
"""
Unzips an image dataset into a specified directory.
Returns:
str: The path to the directory containing the extracted image files.
"""
try:
with ZipFile("image_dataset.zip","r") as extract:
directory_path="dataset_image"
extract.extractall(f"{directory_path}")
return f"{directory_path}"
except Exception as e:
logging.error(f"An error occurred during extraction: {e}")
return ""
def example_images(self):
"""
Unzips the image dataset and generates a list of paths to the individual image files and use image for showing example
Returns:
List[str]: A list of file paths to each image in the dataset.
"""
image_dataset_folder = self.unzip_image_data()
image_extensions = ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp']
image_count = len([name for name in os.listdir(image_dataset_folder) if os.path.isfile(os.path.join(image_dataset_folder, name)) and os.path.splitext(name)[1].lower() in image_extensions])
example=[]
for i in range(image_count):
for name in os.listdir(image_dataset_folder):
path=(os.path.join(os.path.dirname(image_dataset_folder),os.path.join(image_dataset_folder,name)))
example.append(path)
print()
return example
def gradio_interface(self):
with gr.Blocks(theme='JohnSmith9982/small_and_pretty') as demo:
gr.HTML("""<center><h1>Pneumonia Disease Detection</h1></center>""")
exam_img=self.example_images()
with gr.Row():
input_image =gr.Image(type="filepath",sources="upload")
with gr.Column():
output=gr.Label(label="Disease Name")
with gr.Row():
classify_disease_=gr.Textbox(label="About disease")
with gr.Row():
button =gr.Button(value="Detect The Disease")
button.click(self.classify_disease,[input_image],[output,classify_disease_])
gr.Examples(
examples=exam_img,
inputs=[input_image],
outputs=[output,classify_disease_],
fn=self.classify_disease,
cache_examples=False)
demo.launch(debug=True)
if __name__ == "__main__":
app = DiseaseDetectionApp()
result=app.gradio_interface()
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