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Normal_dataset.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b1eef41a054a65d47a5bcbfd4620a64563a0d9624b402dd8666a92146efe13f1
+size 796090

Tuberclosis_dataset.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7d16409f760033644d976755cbac28046c1822c87d1ca620e0205e185cf0e11c
+size 379194

app.py

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+import tensorflow as tf
+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', 'Tuberculosis']
+        self.model =tf.keras.models.load_model("chest_xray_tuberclosis_prediction_model.keras")
+        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=(256, 256))  # 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
+          # print(img_array)
+          # 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=="Tuberculosis":
+            conversation = [
+              {"role": "system", "content": "You are a medical assistant"},
+              {"role": "user", "content": f""" your task describe(classify) about the given disease as a 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
+
+
+
+    def unzip_image_data(self,filespath):
+        """
+        Unzips an image dataset into a specified directory.
+
+        Returns:
+            str: The path to the directory containing the extracted image files.
+        """
+        try:
+          with ZipFile(filespath,"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,filespath):
+          """
+          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(filespath)
+          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)
+
+          return example
+
+    def get_example_image(self):
+      normal_image="Normal_dataset.zip"
+      tuberclosis_image="Tuberclosis_dataset.zip"
+
+      normal_image_unziped=self.example_images(normal_image)
+      tuberclosis_image_unziped=self.example_images(tuberclosis_image)
+
+      return normal_image_unziped,tuberclosis_image_unziped
+
+    def gradio_interface(self):
+
+      with gr.Blocks(theme=gr.themes.Soft()) as demo:
+        gr.HTML("""<center><h1>Tuberclosis Disease Detection</h1></center>""")
+
+        normal_image,tuberclosis_image=self.get_example_image()
+
+        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=normal_image,
+        label="Normal X-ray Images",
+        inputs=[input_image],
+        outputs=[output,classify_disease_],
+        fn=self.classify_disease,
+        examples_per_page=5,
+        cache_examples=False)
+
+        gr.Examples(
+        examples=tuberclosis_image,
+        label="Tuberclosis X-ray Images",
+        inputs=[input_image],
+        outputs=[output,classify_disease_],
+        examples_per_page=5,
+        fn=self.classify_disease,
+        cache_examples=False)
+
+
+      demo.launch(debug=True)
+
+if __name__ == "__main__":
+    app = DiseaseDetectionApp()
+    result=app.gradio_interface()

requirements.txt

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+openai
+gradio
+tensorflow