Create app.py

app.py

@@ -0,0 +1,161 @@
+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):
+
+        # api_key=os.getenv['AZURE_OPENAI_API_KEY']
+        # endpoint=os.getenv['AZURE_OPENAI_ENDPOINT']
+        # api_version=os.getenv['OPENAI_API_VERSION'] 
+      
+        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()