Update Chest_Xray_Report_Generator-V2.py

Update the main file of the Web APP Version (Version 2)

Chest_Xray_Report_Generator-V2.py

@@ -1,13 +1,20 @@
-import os
+iimport os
 import transformers
 from transformers import pipeline
+
+### Gradio 
 import gradio as gr
+from gradio.themes.base import Base
+from gradio.themes.utils import colors, fonts, sizes
+from typing import Union, Iterable
+import time
+#####
+
+
 import cv2
 import numpy as np
 import pydicom
-import time
-
-import spaces  # Import the spaces module for ZeroGPU
+import re
 
 ##### Libraries For Grad-Cam-View
 import os
@@ -21,6 +28,17 @@ from pytorch_grad_cam.utils.image import show_cam_on_image, preprocess_image
 from pytorch_grad_cam.ablation_layer import AblationLayerVit
 from transformers import VisionEncoderDecoderModel
 
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+from openai import OpenAI
+client = OpenAI()
+
+import spaces  # Import the spaces module for ZeroGPU
+
+
 @spaces.GPU 
 def generate_gradcam(image_path, model_path, output_path, method='gradcam', use_cuda=True, aug_smooth=False, eigen_smooth=False):
     methods = {
@@ -48,8 +66,8 @@ def generate_gradcam(image_path, model_path, output_path, method='gradcam', use_
 
     #target_layers = [model.blocks[-1].norm1]  ## For ViT model
     #target_layers = model.blocks[-1].norm1    ## For EfficientNet-B7 model
-    target_layers = [model.encoder.encoder.layer[-1].layernorm_before]  ## For ViT-based VisionEncoderDecoder model
-    #target_layers = [model.encoder.encoder.layers[-1].blocks[-1].layernorm_before, model.encoder.encoder.layers[-1].blocks[0].layernorm_before] ## For Swin-based VisionEncoderDecoder mode
+    #target_layers = [model.encoder.encoder.layer[-1].layernorm_before]  ## For ViT-based VisionEncoderDecoder model
+    target_layers = [model.encoder.encoder.layers[-1].blocks[-0].layernorm_after, model.encoder.encoder.layers[-1].blocks[-1].layernorm_after] ## [model.encoder.encoder.layers[-1].blocks[-1].layernorm_before, model.encoder.encoder.layers[-1].blocks[0].layernorm_before]   For Swin-based VisionEncoderDecoder model
     
 
     if method == "ablationcam":
@@ -65,7 +83,7 @@ def generate_gradcam(image_path, model_path, output_path, method='gradcam', use_
                               reshape_transform=reshape_transform)
 
     rgb_img = cv2.imread(image_path, 1)[:, :, ::-1]
-    rgb_img = cv2.resize(rgb_img, (224, 224)) ## (224, 224)
+    rgb_img = cv2.resize(rgb_img, (384, 384)) ## (224, 224)
     rgb_img = np.float32(rgb_img) / 255
     input_tensor = preprocess_image(rgb_img, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
 
@@ -80,7 +98,8 @@ def generate_gradcam(image_path, model_path, output_path, method='gradcam', use_
     cv2.imwrite(output_file, cam_image)
 
 
-def reshape_transform(tensor, height=14, width=14):  ### height=14, width=14 for ViT-based Model
+
+def reshape_transform(tensor, height=12, width=12):  ### height=14, width=14 for ViT-based Model  
     batch_size, token_number, embed_dim = tensor.size()
     if token_number < height * width:
         pad = torch.zeros(batch_size, height * width - token_number, embed_dim, device=tensor.device)
@@ -93,11 +112,9 @@ def reshape_transform(tensor, height=14, width=14):  ### height=14, width=14 for
     return result
 
 
-
-
 # Example usage:
 #image_path = "/home/chayan/CGI_Net/images/images/CXR1353_IM-0230-1001.png"
-model_path = "./Mimic_test/"
+model_path = "./Model/"
 output_path = "./CAM-Result/"
 
 
@@ -108,6 +125,50 @@ def sentence_case(paragraph):
     formatted_paragraph = '. '.join(formatted_sentences)
     return formatted_paragraph
 
+def num2sym_bullets(text, bullet='-'):
+    """
+    Replaces '<num>.' bullet points with a specified symbol and formats the text as a bullet list.
+    
+    Args:
+        text (str): Input text containing '<num>.' bullet points.
+        bullet (str): The symbol to replace '<num>.' with.
+    
+    Returns:
+        str: Modified text with '<num>.' replaced and formatted as a bullet list.
+    """
+    sentences = re.split(r'<num>\.\s', text)
+    formatted_text = '\n'.join(f'{bullet} {sentence.strip()}' for sentence in sentences if sentence.strip())
+    return formatted_text
+
+def is_cxr(image_path):
+    """
+    Checks if the uploaded image is a Chest X-ray using basic image processing.
+    
+    Args:
+        image_path (str): Path to the uploaded image.
+    
+    Returns:
+        bool: True if the image is likely a Chest X-ray, False otherwise.
+    """
+    try:
+        
+        image = cv2.imread(image_path)
+
+        if image is None:
+            raise ValueError("Invalid image path.")
+    
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        color_std = np.std(image, axis=2).mean()
+    
+        if color_std > 0:
+            return False
+              
+        return True
+        
+    except Exception as e:
+        print(f"Error processing image: {e}")
+        return False
+
 def dicom_to_png(dicom_file, png_file):
     # Load DICOM file
     dicom_data = pydicom.dcmread(dicom_file)
@@ -125,12 +186,12 @@ def dicom_to_png(dicom_file, png_file):
     return img
 
 
-Image_Captioner = pipeline("image-to-text", model = "./Mimic_test/", device = 0)
+Image_Captioner = pipeline("image-to-text", model = "./Model/", device = 0)
 
 data_dir = "./CAM-Result"
 
 @spaces.GPU(duration=300)
-def xray_report_generator(Image_file):
+def xray_report_generator(Image_file, Query):
   if Image_file[-4:] =='.dcm':
     png_file = 'DCM2PNG.png'
     dicom_to_png(Image_file, png_file)
@@ -143,16 +204,32 @@ def xray_report_generator(Image_file):
   result = output[0]['generated_text']
   output_paragraph = sentence_case(result)
   
+  final_response = num2sym_bullets(output_paragraph, bullet='-')
+  
+  query_prompt = f""" You are analyzing the doctor's query based on the patient's history and the generated chest X-ray report. Extract only the information relevant to the query.
+  If the report mentions the queried condition, write only the exact wording without any introduction. If the condition is not mentioned, respond with: 'No relevant findings related to [query condition].'.   
+  """
+  
+  #If the condition is negated, respond with: 'There is no [query condition].'.
+  
+  completion = client.chat.completions.create(
+  model="gpt-4-turbo",  ### gpt-4-turbo ### gpt-3.5-turbo-0125
+  messages=[
+    {"role": "system", "content": query_prompt},
+    {"role": "user", "content": f"Generated Report: {final_response}\nHistory/Doctor's Query: {Query}"}
+   ],
+   temperature=0.2)
+  query_response = completion.choices[0].message.content
+  
   generate_gradcam(Image_file, model_path, output_path, method='gradcam', use_cuda=True)
   
   grad_cam_image =  output_path + 'gradcam_result.png'
 
-  return Image_file,grad_cam_image, output_paragraph
-  
+  return grad_cam_image, final_response, query_response
 
 
 # def save_feedback(feedback):
-#     feedback_dir = "./Feedback"  # Update this to your desired directory
+#     feedback_dir = "Chayan/Feedback/"  # Update this to your desired directory
 #     if not os.path.exists(feedback_dir):
 #         os.makedirs(feedback_dir)
 #     feedback_file = os.path.join(feedback_dir, "feedback.txt")
@@ -161,7 +238,6 @@ def xray_report_generator(Image_file):
 #     return "Feedback submitted successfully!"
 
 
-
 def save_feedback(feedback):
     feedback_dir = "Chayan/Feedback/"  # Update this to your desired directory
     if not os.path.exists(feedback_dir):
@@ -177,11 +253,62 @@ def save_feedback(feedback):
         print(f"Error saving feedback: {e}")
         return "Failed to submit feedback!"
   
-  
+
+# Custom Theme Definition
+class Seafoam(Base):
+    def __init__(
+        self,
+        *,
+        primary_hue: Union[colors.Color, str] = colors.emerald,
+        secondary_hue: Union[colors.Color, str] = colors.blue,
+        neutral_hue: Union[colors.Color, str] = colors.gray,
+        spacing_size: Union[sizes.Size, str] = sizes.spacing_md,
+        radius_size: Union[sizes.Size, str] = sizes.radius_md,
+        text_size: Union[sizes.Size, str] = sizes.text_lg,
+        font: Union[fonts.Font, str, Iterable[Union[fonts.Font, str]]] = (
+            fonts.GoogleFont("Quicksand"),
+            "ui-sans-serif",
+            "sans-serif",
+        ),
+        font_mono: Union[fonts.Font, str, Iterable[Union[fonts.Font, str]]] = (
+            fonts.GoogleFont("IBM Plex Mono"),
+            "ui-monospace",
+            "monospace",
+        ),
+    ):
+        super().__init__(
+            primary_hue=primary_hue,
+            secondary_hue=secondary_hue,
+            neutral_hue=neutral_hue,
+            spacing_size=spacing_size,
+            radius_size=radius_size,
+            text_size=text_size,
+            font=font,
+            font_mono=font_mono,
+        )
+        
+        self.set(
+            body_background_fill="linear-gradient(114.2deg, rgba(184,215,21,1) -15.3%, rgba(21,215,98,1) 14.5%, rgba(21,215,182,1) 38.7%, rgba(129,189,240,1) 58.8%, rgba(219,108,205,1) 77.3%, rgba(240,129,129,1) 88.5%)"
+        )
+# Initialize the theme
+seafoam = Seafoam()
+
+
+
 # Custom CSS styles
 custom_css = """
 <style>
 
+/* Set background color for the entire Gradio app */
+body, .gradio-container {
+    background-color: #f2f7f5 !important;
+}
+
+/* Optional: Add padding or margin for aesthetics */
+.gradio-container {
+    padding: 20px;
+}
+
 #title {
     color: green;
     font-size: 36px;
@@ -192,15 +319,25 @@ custom_css = """
     font-size: 22px;
 }
 
+#title-row {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    margin-bottom: 0px;
+}
+#title-header h1 {
+    margin: 0;
+}
+
 
 #submit-btn {
-    background-color: #1E90FF; /* DodgerBlue */
+    background-color: #f5dec6; /* Banana leaf */
     color: green;
     padding: 15px 32px;
     text-align: center;
     text-decoration: none;
     display: inline-block;
-    font-size: 20px;
+    font-size: 30px;
     margin: 4px 2px;
     cursor: pointer;
 }
@@ -208,6 +345,7 @@ custom_css = """
     background-color: #00FFFF;
 }
 
+
 .intext textarea {
     color: green;
     font-size: 20px;
@@ -262,16 +400,43 @@ def show_acknowledgment():
     yield gr.update(visible=False)
 
 
-with gr.Blocks(css = custom_css) as demo:
+with gr.Blocks(theme=seafoam, css=custom_css) as demo:
 
     #gr.HTML(custom_css)  # Inject custom CSS
+    
+    
+    with gr.Row(elem_id="title-row"):
+        with gr.Column(scale=0):
+            gr.Image(
+                value="./AURA-CXR-Logo.png",
+                show_label=False,
+                width=60,
+                container=False
+                )
+        with gr.Column():
+            gr.Markdown(
+                """
+                <h1 style="color:blue; font-size: 32px; font-weight: bold; margin: 0;">
+                    AURA-CXR: Explainable Diagnosis of Chest Diseases from X-rays
+                </h1>
+                """,
+                elem_id="title-header"
+            )
 
     gr.Markdown(
-         """
-        <h1 style="color:blue; font-size: 36px; font-weight: bold">Chest X-ray Report Generator</h1>
-        <p id="description">Upload an X-ray image and get its report with heat-map visualization.</p>
-        """
+        "<p id='description'>Upload an X-ray image and get its report with heat-map visualization.</p>"
     )
+    
+    
+
+   # gr.Markdown(
+   #      """
+   #      <h1 style="color:blue; font-size: 36px; font-weight: bold; margin: 0;">AURA-CXR: Explainable Diagnosis of Chest Diseases from X-rays</h1>
+   #       <p id="description">Upload an X-ray image and get its report with heat-map visualization.</p>
+   #     """
+   # )
+    
+    #<h1 style="color:blue; font-size: 36px; font-weight: bold">AURA-CXR: Explainable Diagnosis of Chest Diseases from X-rays</h1>
 
     with gr.Row():
         inputs = gr.File(label="Upload Chest X-ray Image File", type="filepath")
@@ -279,17 +444,37 @@ with gr.Blocks(css = custom_css) as demo:
     with gr.Row():
         with gr.Column(scale=1, min_width=300):
             outputs1 = gr.Image(label="Image Viewer")
+            history_query = gr.Textbox(label="History/Doctor's Query", elem_classes="intext")
         with gr.Column(scale=1, min_width=300):
             outputs2 = gr.Image(label="Grad_CAM-Visualization")
         with gr.Column(scale=1, min_width=300):
             outputs3 = gr.Textbox(label="Generated Report", elem_classes = "intext")
+            outputs4 = gr.Textbox(label = "Query's Response", elem_classes = "intext")
             
 
-    submit_btn = gr.Button("Generate Report", elem_id="submit-btn")
+    submit_btn = gr.Button("Generate Report", elem_id="submit-btn", variant="primary")
+    
+    def show_image(file_path):
+        if is_cxr(file_path):  # Check if it's a valid Chest X-ray
+            return file_path, "Valid Image"   # Show the image in Image Viewer
+        else:
+            return None, "Invalid image. Please upload a proper Chest X-ray." 
+            
+            
+    # Show the uploaded image immediately in the Image Viewer
+    inputs.change(
+        fn=show_image,  # Calls the function to return the same file path
+        inputs=inputs,
+        outputs=[outputs1, outputs3]
+    )
+    
+    
+    
+    
     submit_btn.click(
         fn=xray_report_generator,
-        inputs=inputs,
-        outputs=[outputs1, outputs2, outputs3])
+        inputs=[inputs,history_query],
+        outputs=[outputs2, outputs3, outputs4])
         
     
     gr.Markdown(
@@ -310,7 +495,6 @@ with gr.Blocks(css = custom_css) as demo:
                 )
 
 
-
       # Feedback section
     gr.Markdown(
         """
@@ -320,8 +504,10 @@ with gr.Blocks(css = custom_css) as demo:
 
     with gr.Row():
         feedback_input = gr.Textbox(label="Your Feedback", lines=4, placeholder="Enter your feedback here...")
-        feedback_submit_btn = gr.Button("Submit Feedback", elem_classes="small-button")
-        feedback_output = gr.Textbox(label="Feedback Status", interactive=True)
+        feedback_submit_btn = gr.Button("Submit Feedback", elem_classes="small-button", variant="secondary")
+        feedback_output = gr.Textbox(label="Feedback Status", interactive=False)
+
+    
 
     feedback_submit_btn.click(
         fn=save_feedback,
@@ -329,6 +515,7 @@ with gr.Blocks(css = custom_css) as demo:
         outputs=feedback_output
     )
    
+
     # Buttons and Markdown for Contact Us and Acknowledgment
     with gr.Row():
         contact_btn = gr.Button("Contact Us", elem_classes="small-button", variant="secondary")
@@ -339,6 +526,12 @@ with gr.Blocks(css = custom_css) as demo:
 
     # Update the content and make it visible when the buttons are clicked
     contact_btn.click(fn=show_contact_info, outputs=contact_info, show_progress=False)
-    ack_btn.click(fn=show_acknowledgment, outputs=acknowledgment_info, show_progress=False)  
+    ack_btn.click(fn=show_acknowledgment, outputs=acknowledgment_info, show_progress=False)
+
+    # Update the content and make it visible when the buttons are clicked
+    # contact_btn.click(fn=show_contact_info, outputs=contact_info, show_progress=False)
+    # ack_btn.click(fn=show_acknowledgment, outputs=acknowledgment_info, show_progress=False)
+
    
 demo.launch(share=True)
+