Update Chest_Xray_Report_Generator-Web-V2.py

Chest_Xray_Report_Generator-Web-V2.py

@@ -1,537 +1,577 @@
-import 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 re
-
-##### Libraries For Grad-Cam-View
-import os
-import cv2
-import numpy as np
-import torch
-from functools import partial
-from torchvision import transforms
-from pytorch_grad_cam import GradCAM, ScoreCAM, GradCAMPlusPlus, AblationCAM, XGradCAM, EigenCAM, EigenGradCAM, LayerCAM, FullGrad
-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 = {
-        "gradcam": GradCAM,
-        "scorecam": ScoreCAM,
-        "gradcam++": GradCAMPlusPlus,
-        "ablationcam": AblationCAM,
-        "xgradcam": XGradCAM,
-        "eigencam": EigenCAM,
-        "eigengradcam": EigenGradCAM,
-        "layercam": LayerCAM,
-        "fullgrad": FullGrad
-    }
-
-    if method not in methods:
-        raise ValueError(f"Method should be one of {list(methods.keys())}")
-
-    model = VisionEncoderDecoderModel.from_pretrained(model_path)
-    model.encoder.eval()
-
-    if use_cuda and torch.cuda.is_available():
-        model.encoder = model.encoder.cuda()
-    else:
-        use_cuda = False
-
-    #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[-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":
-        cam = methods[method](model=model.encoder,
-                              target_layers=target_layers,
-                              use_cuda=use_cuda,
-                              reshape_transform=reshape_transform,
-                              ablation_layer=AblationLayerVit())
-    else:
-        cam = methods[method](model=model.encoder,
-                              target_layers=target_layers,
-                              use_cuda=use_cuda,
-                              reshape_transform=reshape_transform)
-
-    rgb_img = cv2.imread(image_path, 1)[:, :, ::-1]
-    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])
-
-    targets = None
-    cam.batch_size = 16
-
-    grayscale_cam = cam(input_tensor=input_tensor, targets=targets, eigen_smooth=eigen_smooth, aug_smooth=aug_smooth)
-    grayscale_cam = grayscale_cam[0, :]
-
-    cam_image = show_cam_on_image(rgb_img, grayscale_cam)
-    output_file = os.path.join(output_path, 'gradcam_result.png')
-    cv2.imwrite(output_file, cam_image)
-
-
-
-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)
-        tensor = torch.cat([tensor, pad], dim=1)
-    elif token_number > height * width:
-        tensor = tensor[:, :height * width, :]
-
-    result = tensor.reshape(batch_size, height, width, embed_dim)
-    result = result.transpose(2, 3).transpose(1, 2)
-    return result
-
-
-# Example usage:
-#image_path = "/home/chayan/CGI_Net/images/images/CXR1353_IM-0230-1001.png"
-model_path = "./Model/"
-output_path = "./CAM-Result/"
-
-
-
-def sentence_case(paragraph):
-    sentences = paragraph.split('. ')
-    formatted_sentences = [sentence.capitalize() for sentence in sentences if sentence]
-    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)
-    dicom_data.PhotometricInterpretation = 'MONOCHROME1'
-
-    # Normalize pixel values to 0-255
-    img = dicom_data.pixel_array
-    img = img.astype(np.float32)
-
-    img = cv2.normalize(img, None, 0, 255, cv2.NORM_MINMAX)
-    img = img.astype(np.uint8)
-
-    # Save as PNG
-    cv2.imwrite(png_file, img)
-    return img
-
-
-Image_Captioner = pipeline("image-to-text", model = "./Model/", device = 0)
-
-data_dir = "./CAM-Result"
-
-@spaces.GPU(duration=300)
-def xray_report_generator(Image_file, Query):
-  if Image_file[-4:] =='.dcm':
-    png_file = 'DCM2PNG.png'
-    dicom_to_png(Image_file, png_file)
-    Image_file = os.path.join(data_dir, png_file)
-    output = Image_Captioner(Image_file, max_new_tokens=512)
-
-  else:
-    output = Image_Captioner(Image_file, max_new_tokens=512)
-
-  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 grad_cam_image, final_response, query_response
-
-
-# def save_feedback(feedback):
-#     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")
-#     with open(feedback_file, "a") as f:
-#         f.write(feedback + "\n")
-#     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):
-        os.makedirs(feedback_dir)
-    feedback_file = os.path.join(feedback_dir, "feedback.txt")
-    
-    try:
-        with open(feedback_file, "a") as f:
-            f.write(feedback + "\n")
-        print(f"Feedback saved at: {feedback_file}")
-        return "Feedback submitted successfully!"
-    except Exception as e:
-        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;
-    font-weight: bold;
-}
-#description {
-    color: green;
-    font-size: 22px;
-}
-
-#title-row {
-    display: flex;
-    align-items: center;
-    gap: 10px;
-    margin-bottom: 0px;
-}
-#title-header h1 {
-    margin: 0;
-}
-
-
-#submit-btn {
-    background-color: #f5dec6; /* Banana leaf */
-    color: green;
-    padding: 15px 32px;
-    text-align: center;
-    text-decoration: none;
-    display: inline-block;
-    font-size: 30px;
-    margin: 4px 2px;
-    cursor: pointer;
-}
-#submit-btn:hover {
-    background-color: #00FFFF;
-}
-
-
-.intext textarea {
-    color: green;
-    font-size: 20px;
-    font-weight: bold;
-}
-
-
-.small-button {
-    color: green;
-    padding: 5px 10px;
-    font-size: 20px;
-}
-
-</style>
-"""
-
-# Sample image paths
-sample_images = [
-    "./Test-Images/0d930f0a-46f813a9-db3b137b-05142eef-eca3c5a7.jpg",
-    "./Test-Images/93681764-ec39480e-0518b12c-199850c2-f15118ab.jpg",
-    "./Test-Images/6ff741e9-6ea01eef-1bf10153-d1b6beba-590b6620.jpg"
-    #"sample4.png",
-    #"sample5.png"
-]
-
-def set_input_image(image_path):
-    return gr.update(value=image_path)
-
-def show_contact_info():
-    yield gr.update(visible=True, value="""
-    **Contact Us:**
-    - Chayan Mondal
-    - Email: [email protected]
-    - Associate Prof. Sonny Pham
-    - Email: [email protected]
-    - Dr. Ashu Gupta
-    - Email: [email protected]
-    """)
-    # Wait for 20 seconds (you can adjust the time as needed)
-    time.sleep(20)
-    # Hide the content after 5 seconds
-    yield gr.update(visible=False)
-
-def show_acknowledgment():
-    yield gr.update(visible=True, value="""
-    **Acknowledgment:**
-    This Research has been supported by the Western Australian Future Health Research and Innovation Fund.
-    """)
-    # Wait for 20 seconds
-    time.sleep(20)
-    # Hide the acknowledgment
-    yield gr.update(visible=False)
-
-
-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(
-        "<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")
-    
-    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", 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,history_query],
-        outputs=[outputs2, outputs3, outputs4])
-        
-    
-    gr.Markdown(
-        """
-        <h2 style="color:green; font-size: 24px;">Or choose a sample image:</h2>
-        """
-    )
-
-    with gr.Row():
-        for idx, sample_image in enumerate(sample_images):
-            with gr.Column(scale=1):
-                #sample_image_component = gr.Image(value=sample_image, interactive=False)
-                select_button = gr.Button(f"Select Sample Image {idx+1}")
-                select_button.click(
-                    fn=set_input_image,
-                    inputs=gr.State(value=sample_image),
-                    outputs=inputs
-                )
-
-
-      # Feedback section
-    gr.Markdown(
-        """
-        <h2 style="color:green; font-size: 24px;">Provide Your Valuable Feedback:</h2>
-        """
-    )
-
-    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", variant="secondary")
-        feedback_output = gr.Textbox(label="Feedback Status", interactive=False)
-
-    
-
-    feedback_submit_btn.click(
-        fn=save_feedback,
-        inputs=feedback_input,
-        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")
-        ack_btn = gr.Button("Acknowledgment", elem_classes="small-button", variant="secondary")
-
-    contact_info = gr.Markdown(visible=False)  # Initially hidden
-    acknowledgment_info = gr.Markdown(visible=False)  # Initially hidden
-
-    # 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)
-
-    # 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)
-
+import 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 re
+
+##### Libraries For Grad-Cam-View
+import os
+import cv2
+import numpy as np
+import torch
+from functools import partial
+from torchvision import transforms
+from pytorch_grad_cam import GradCAM, ScoreCAM, GradCAMPlusPlus, AblationCAM, XGradCAM, EigenCAM, EigenGradCAM, LayerCAM, FullGrad
+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()
+
+# === SET THESE ===
+# REPO_ID = "ChayanDeb/AURA-CXR_Feedback"
+# LOCAL_DIR = "feedback_repo"
+# FEEDBACK_FILE = os.path.join(LOCAL_DIR, "feedback.txt")
+
+# # Only once at app start
+# def setup_repo():
+#     token = os.getenv("HF_TOKEN")
+#     if not token:
+#         raise ValueError("HF_TOKEN not set in environment variables")
+
+#     login(token=token)
+
+#     if not os.path.exists(LOCAL_DIR):
+#         print("Cloning feedback repo...")
+#         Repository(local_dir=LOCAL_DIR, clone_from=REPO_ID, use_auth_token=token)
+#     else:
+#         print("Repo already exists locally.")
+
+# setup_repo()
+
+# # Call this on user feedback submission
+# def save_feedback(feedback):
+#     try:
+#         now = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+#         entry = f"{now} | {feedback.strip()}\n"
+
+#         # Append to local file
+#         with open(FEEDBACK_FILE, "a") as f:
+#             f.write(entry)
+
+#         # Push to hub
+#         repo = Repository(local_dir=LOCAL_DIR, clone_from=REPO_ID, use_auth_token=os.getenv("HF_TOKEN"))
+#         repo.push_to_hub(commit_message="Add new feedback")
+
+#         return "Feedback submitted!"
+#     except Exception as e:
+#         print("Error:", e)
+#         return "Failed to submit feedback."
+
+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 = {
+        "gradcam": GradCAM,
+        "scorecam": ScoreCAM,
+        "gradcam++": GradCAMPlusPlus,
+        "ablationcam": AblationCAM,
+        "xgradcam": XGradCAM,
+        "eigencam": EigenCAM,
+        "eigengradcam": EigenGradCAM,
+        "layercam": LayerCAM,
+        "fullgrad": FullGrad
+    }
+
+    if method not in methods:
+        raise ValueError(f"Method should be one of {list(methods.keys())}")
+
+    model = VisionEncoderDecoderModel.from_pretrained(model_path)
+    model.encoder.eval()
+
+    if use_cuda and torch.cuda.is_available():
+        model.encoder = model.encoder.cuda()
+    else:
+        use_cuda = False
+
+    #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[-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":
+        cam = methods[method](model=model.encoder,
+                              target_layers=target_layers,
+                              use_cuda=use_cuda,
+                              reshape_transform=reshape_transform,
+                              ablation_layer=AblationLayerVit())
+    else:
+        cam = methods[method](model=model.encoder,
+                              target_layers=target_layers,
+                              use_cuda=use_cuda,
+                              reshape_transform=reshape_transform)
+
+    rgb_img = cv2.imread(image_path, 1)[:, :, ::-1]
+    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])
+
+    targets = None
+    cam.batch_size = 16
+
+    grayscale_cam = cam(input_tensor=input_tensor, targets=targets, eigen_smooth=eigen_smooth, aug_smooth=aug_smooth)
+    grayscale_cam = grayscale_cam[0, :]
+
+    cam_image = show_cam_on_image(rgb_img, grayscale_cam)
+    output_file = os.path.join(output_path, 'gradcam_result.png')
+    cv2.imwrite(output_file, cam_image)
+
+
+
+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)
+        tensor = torch.cat([tensor, pad], dim=1)
+    elif token_number > height * width:
+        tensor = tensor[:, :height * width, :]
+
+    result = tensor.reshape(batch_size, height, width, embed_dim)
+    result = result.transpose(2, 3).transpose(1, 2)
+    return result
+
+
+# Example usage:
+#image_path = "/home/chayan/CGI_Net/images/images/CXR1353_IM-0230-1001.png"
+model_path = "./Model/"
+output_path = "./CAM-Result/"
+
+
+
+def sentence_case(paragraph):
+    sentences = paragraph.split('. ')
+    formatted_sentences = [sentence.capitalize() for sentence in sentences if sentence]
+    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)
+    dicom_data.PhotometricInterpretation = 'MONOCHROME1'
+
+    # Normalize pixel values to 0-255
+    img = dicom_data.pixel_array
+    img = img.astype(np.float32)
+
+    img = cv2.normalize(img, None, 0, 255, cv2.NORM_MINMAX)
+    img = img.astype(np.uint8)
+
+    # Save as PNG
+    cv2.imwrite(png_file, img)
+    return img
+
+
+Image_Captioner = pipeline("image-to-text", model = "./Model/", device = 0)
+
+data_dir = "./CAM-Result"
+
+@spaces.GPU(duration=300)
+def xray_report_generator(Image_file, Query):
+  if Image_file[-4:] =='.dcm':
+    png_file = 'DCM2PNG.png'
+    dicom_to_png(Image_file, png_file)
+    Image_file = os.path.join(data_dir, png_file)
+    output = Image_Captioner(Image_file, max_new_tokens=512)
+
+  else:
+    output = Image_Captioner(Image_file, max_new_tokens=512)
+
+  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 grad_cam_image, final_response, query_response
+
+
+# def save_feedback(feedback):
+#     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")
+#     with open(feedback_file, "a") as f:
+#         f.write(feedback + "\n")
+#     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):
+        os.makedirs(feedback_dir)
+    feedback_file = os.path.join(feedback_dir, "feedback.txt")
+    
+    try:
+        with open(feedback_file, "a") as f:
+            f.write(feedback + "\n")
+        print(f"Feedback saved at: {feedback_file}")
+        return "Feedback submitted successfully!"
+    except Exception as e:
+        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;
+    font-weight: bold;
+}
+#description {
+    color: green;
+    font-size: 22px;
+}
+
+#title-row {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    margin-bottom: 0px;
+}
+#title-header h1 {
+    margin: 0;
+}
+
+
+#submit-btn {
+    background-color: #f5dec6; /* Banana leaf */
+    color: green;
+    padding: 15px 32px;
+    text-align: center;
+    text-decoration: none;
+    display: inline-block;
+    font-size: 30px;
+    margin: 4px 2px;
+    cursor: pointer;
+}
+#submit-btn:hover {
+    background-color: #00FFFF;
+}
+
+
+.intext textarea {
+    color: green;
+    font-size: 20px;
+    font-weight: bold;
+}
+
+
+.small-button {
+    color: green;
+    padding: 5px 10px;
+    font-size: 20px;
+}
+
+</style>
+"""
+
+# Sample image paths
+sample_images = [
+    "./Test-Images/0d930f0a-46f813a9-db3b137b-05142eef-eca3c5a7.jpg",
+    "./Test-Images/93681764-ec39480e-0518b12c-199850c2-f15118ab.jpg",
+    "./Test-Images/6ff741e9-6ea01eef-1bf10153-d1b6beba-590b6620.jpg"
+    #"sample4.png",
+    #"sample5.png"
+]
+
+def set_input_image(image_path):
+    return gr.update(value=image_path)
+
+def show_contact_info():
+    yield gr.update(visible=True, value="""
+    **Contact Us:**
+    - Chayan Mondal
+    - Email: [email protected]
+    - Associate Prof. Sonny Pham
+    - Email: [email protected]
+    - Dr. Ashu Gupta
+    - Email: [email protected]
+    """)
+    # Wait for 20 seconds (you can adjust the time as needed)
+    time.sleep(20)
+    # Hide the content after 5 seconds
+    yield gr.update(visible=False)
+
+def show_acknowledgment():
+    yield gr.update(visible=True, value="""
+    **Acknowledgment:**
+    This Research has been supported by the Western Australian Future Health Research and Innovation Fund.
+    """)
+    # Wait for 20 seconds
+    time.sleep(20)
+    # Hide the acknowledgment
+    yield gr.update(visible=False)
+
+
+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(
+        "<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")
+    
+    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", 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,history_query],
+        outputs=[outputs2, outputs3, outputs4])
+        
+    
+    gr.Markdown(
+        """
+        <h2 style="color:green; font-size: 24px;">Or choose a sample image:</h2>
+        """
+    )
+
+    with gr.Row():
+        for idx, sample_image in enumerate(sample_images):
+            with gr.Column(scale=1):
+                #sample_image_component = gr.Image(value=sample_image, interactive=False)
+                select_button = gr.Button(f"Select Sample Image {idx+1}")
+                select_button.click(
+                    fn=set_input_image,
+                    inputs=gr.State(value=sample_image),
+                    outputs=inputs
+                )
+
+
+      # Feedback section
+    gr.Markdown(
+        """
+        <h2 style="color:green; font-size: 24px;">Provide Your Valuable Feedback:</h2>
+        """
+    )
+
+    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", variant="secondary")
+        feedback_output = gr.Textbox(label="Feedback Status", interactive=False)
+
+    
+
+    feedback_submit_btn.click(
+        fn=save_feedback,
+        inputs=feedback_input,
+        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")
+        ack_btn = gr.Button("Acknowledgment", elem_classes="small-button", variant="secondary")
+
+    contact_info = gr.Markdown(visible=False)  # Initially hidden
+    acknowledgment_info = gr.Markdown(visible=False)  # Initially hidden
+
+    # 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)
+
+    # 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)
+