app.py

import os
import sys
from env import config_env


config_env()


import gradio as gr
from huggingface_hub import snapshot_download
import cv2 
import dotenv
dotenv.load_dotenv()
import numpy as np
import gradio as gr
import glob
from inference_sam import segmentation_sam
from explanations import explain
from inference_resnet import get_triplet_model
from inference_beit import get_triplet_model_beit
import pathlib
import tensorflow as tf
from closest_sample import get_images,get_diagram


if not os.path.exists('images'):
    REPO_ID='Serrelab/image_examples_gradio'
    snapshot_download(repo_id=REPO_ID, token=os.environ.get('READ_TOKEN'),repo_type='dataset',local_dir='images')

if not os.path.exists('dataset'):
  REPO_ID='Serrelab/Fossils'
  token = os.environ.get('READ_TOKEN')
  print(f"Read token:{token}")
  if token is None:
     print("warning! A read token in env variables is needed for authentication.")
  snapshot_download(repo_id=REPO_ID, token=token,repo_type='dataset',local_dir='dataset')

HEADER = '''
<h2><b>Official Gradio Demo</b></h2><h2><a href='https://huggingface.co./spaces/Serrelab/fossil_app' target='_blank'><b>Identifying Florissant Leaf Fossils to Family using Deep Neural Networks </b></a></h2>
Code: <a href='https://github.com/orgs/serre-lab/projects/2' target='_blank'>GitHub</a>. Paper: <a href='' target='_blank'>ArXiv</a>.


'''

"""
**Fossil** a brief intro to the project.
# ❗️❗️❗️**Important Notes:**
# - some notes to users some notes to users some notes to users some notes to users some notes to users some notes to users .
# - some notes to users some notes to users some notes to users some notes to users some notes to users some notes to users.

"""

USER_GUIDE = """
                <div style='background-color: #f0f0f0; padding: 20px; border-radius: 10px;'>
                    <h2>❗️ User Guide</h2>
                    <p>Welcome to the interactive fossil exploration tool. Here's how to get started:</p>
                    <ul>
                        <li><strong>Upload an Image:</strong> Drag and drop or choose from given samples to upload images of fossils.</li>
                        <li><strong>Process Image:</strong> After uploading, click the 'Process Image' button to analyze the image.</li>
                        <li><strong>Explore Results:</strong> Switch to the 'Workbench' tab to check out detailed analysis and results.</li>
                    </ul>
                    <h3>Tips</h3>
                    <ul>
                        <li>Zoom into images on the workbench for finer details.</li>
                        <li>Use the examples below as references for what types of images to upload.</li>
                    </ul>
                    <p>Enjoy exploring! 🌟</p>
                </div>
                """
                
TIPS = """
                ## Tips
                - Zoom into images on the workbench for finer details.
                - Use the examples below as references for what types of images to upload.
                
                Enjoy exploring! 
                """
CITATION = '''
📧 **Contact** <br>
If you have any questions, feel free to contact us at <b>[email protected]</b>.
'''
"""
📝 **Citation**
cite using this bibtex:...
```
```
📋 **License**
"""
def get_model(model_name):
   
        
    if model_name=='Mummified 170':
        n_classes = 170
        model = get_triplet_model(input_shape = (600, 600, 3),
                        embedding_units = 256,
                        embedding_depth = 2,
                        backbone_class=tf.keras.applications.ResNet50V2,
                        nb_classes = n_classes,load_weights=False,finer_model=True,backbone_name ='Resnet50v2')
        model.load_weights('model_classification/mummified-170.h5')
    elif model_name=='Rock 170':
        n_classes = 171
        model = get_triplet_model(input_shape = (600, 600, 3),
                        embedding_units = 256,
                        embedding_depth = 2,
                        backbone_class=tf.keras.applications.ResNet50V2,
                        nb_classes = n_classes,load_weights=False,finer_model=True,backbone_name ='Resnet50v2')
        model.load_weights('model_classification/rock-170.h5')
    elif model_name == 'Fossils 142':
        n_classes = 142
        model = get_triplet_model_beit(input_shape = (384, 384, 3),
                                  embedding_units = 256,
                                  embedding_depth = 2,
                                  n_classes = n_classes)
        model.load_weights('model_classification/fossil-142.h5')
    elif model_name == 'Fossils new':
        n_classes = 142
        model = get_triplet_model_beit(input_shape = (384, 384, 3),
                                  embedding_units = 256,
                                  embedding_depth = 2,
                                  n_classes = n_classes)
        model.load_weights('model_classification/fossil-new.h5')
    else:
        raise ValueError(f"Model name '{model_name}' is not recognized") 
    return model,n_classes


def segment_image(input_image):
    img = segmentation_sam(input_image)
    return img

def classify_image(input_image, model_name):
    #segmented_image = segment_image(input_image)
    if 'Rock 170' ==model_name:
        from inference_resnet import inference_resnet_finer
        model,n_classes= get_model(model_name)
        result = inference_resnet_finer(input_image,model,size=600,n_classes=n_classes)
        return result 
    elif 'Mummified 170' ==model_name:
        from inference_resnet import inference_resnet_finer
        model, n_classes= get_model(model_name)
        result = inference_resnet_finer(input_image,model,size=600,n_classes=n_classes)
        return result 
    elif 'Fossils 142' ==model_name:
        from inference_beit import inference_resnet_finer_beit
        model,n_classes = get_model(model_name)
        result = inference_resnet_finer_beit(input_image,model,size=384,n_classes=n_classes)
        return result
    elif 'Fossils new' ==model_name:
        from inference_beit import inference_resnet_finer_beit
        model,n_classes = get_model(model_name)
        result = inference_resnet_finer_beit(input_image,model,size=384,n_classes=n_classes)
        return result
    return None

def get_embeddings(input_image,model_name):
    if 'Rock 170' ==model_name:
        from inference_resnet import inference_resnet_embedding
        model,n_classes= get_model(model_name)
        result = inference_resnet_embedding(input_image,model,size=600,n_classes=n_classes)
        return result 
    elif 'Mummified 170' ==model_name:
        from inference_resnet import inference_resnet_embedding
        model, n_classes= get_model(model_name)
        result = inference_resnet_embedding(input_image,model,size=600,n_classes=n_classes)
        return result 
    elif 'Fossils 142' ==model_name:
        from inference_beit import inference_resnet_embedding_beit
        model,n_classes = get_model(model_name)
        result = inference_resnet_embedding_beit(input_image,model,size=384,n_classes=n_classes)
        return result
    elif 'Fossils new' ==model_name:
        from inference_beit import inference_resnet_embedding_beit
        model,n_classes = get_model(model_name)
        result = inference_resnet_embedding_beit(input_image,model,size=384,n_classes=n_classes)
        return result
    return None
    

def find_closest(input_image,model_name):
    embedding = get_embeddings(input_image,model_name)
    classes, paths = get_images(embedding)
    #outputs = classes+paths
    return classes,paths

def generate_diagram_closest(input_image,model_name,top_k):
    embedding = get_embeddings(input_image,model_name)
    diagram_path = get_diagram(embedding,top_k)
    return diagram_path

def explain_image(input_image,model_name,explain_method,nb_samples):
    model,n_classes= get_model(model_name)
    if model_name=='Fossils 142' or 'Fossils new':
        size = 384
    else:
        size = 600
    #saliency, integrated, smoothgrad,
    classes,exp_list = explain(model,input_image,explain_method,nb_samples,size = size, n_classes=n_classes)
    #original =  saliency + integrated + smoothgrad 
    print('done')
    
    return classes,exp_list

def setup_examples():
    paths = sorted(pathlib.Path('images/').rglob('*.jpg'))
    samples = [path.as_posix() for path in paths if 'fossils' in str(path)][:19]
    examples_fossils = gr.Examples(samples, inputs=input_image,examples_per_page=5,label='Fossils Examples from the dataset')
    samples=[[path.as_posix()] for path in paths  if 'leaves' in  str(path) ][:19]
    examples_leaves = gr.Examples(samples, inputs=input_image,examples_per_page=5,label='Leaves Examples from the dataset')
    return examples_fossils,examples_leaves

def preprocess_image(image, output_size=(300, 300)):
    #shape (height, width, channels)
    h, w = image.shape[:2]

    #padding
    if h > w:
        padding = (h - w) // 2
        image_padded = cv2.copyMakeBorder(image, 0, 0, padding, padding, cv2.BORDER_CONSTANT, value=[0, 0, 0])
    else:
        padding = (w - h) // 2
        image_padded = cv2.copyMakeBorder(image, padding, padding, 0, 0, cv2.BORDER_CONSTANT, value=[0, 0, 0])
    
    # resize
    image_resized = cv2.resize(image_padded, output_size, interpolation=cv2.INTER_AREA)

    return image_resized

def update_display(image):
    original_image = image
    processed_image = preprocess_image(image) 
    instruction = "Image ready. Please switch to the 'Specimen Workbench' tab to check out further analysis and outputs."
    model_name = "Fossils new"
    
    # gr.Dropdown(
    #                 ["Mummified 170", "Rock 170","Fossils 142","Fossils new"],
    #                 multiselect=False,
    #                 value="Fossils new", # default option
    #                 label="Model",
    #                 interactive=True,
    #                 info="Choose the model you'd like to use"
    #             )
    explain_method = "Rise"
    
    # gr.Dropdown(
    #     ["Sobol", "HSIC","Rise","Saliency"],
    #     multiselect=False,
    #     value="Rise", # default option
    #     label="Explain method",
    #     interactive=True,
    #     info="Choose one method to explain the model"
    # )
    sampling_size = 5
    # gr.Slider(1, 5000, value=2000, label="Sampling Size in Rise",interactive=True,visible=True,
    #                                       info="Choose between 1 and 5000")
                
    top_k = 50
    # gr.Slider(10,200,value=50,label="Number of Closest Samples for Distribution Chart",interactive=True,info="Choose between 10 and 200")
    class_predicted = None # gr.Label(label='Class Predicted',num_top_classes=10)
    exp_gallery = None
    # gr.Gallery(label="Explanation Heatmaps for top 5 predicted classes", show_label=False,elem_id="gallery",columns=[5], rows=[1],height='auto', allow_preview=True, preview=None)
    closest_gallery = None
    # gr.Gallery(label="Closest Images", show_label=False,elem_id="gallery",columns=[5], rows=[1],height='auto', allow_preview=True, preview=None)
    diagram= None
    # gr.Image(label = 'Bar Chart')
    return original_image,processed_image,processed_image,instruction,model_name,explain_method,sampling_size,top_k,class_predicted,exp_gallery,closest_gallery,diagram
def update_slider_visibility(explain_method):
    bool = explain_method=="Rise"
    return {sampling_size: gr.Slider(1, 5000, value=2000, label="Sampling Size in Rise", visible=bool, interactive=True)}

#minimalist theme
with gr.Blocks(theme='sudeepshouche/minimalist') as demo:
    
    with gr.Tab(" Florrissant Fossils"):
        gr.Markdown(HEADER)
        with gr.Row():
            with gr.Column():
                gr.Markdown(USER_GUIDE)
            with gr.Column(scale=2):
                with gr.Column(scale=2):
                    instruction_text = gr.Textbox(label="Instructions", value="Upload/Choose an image and click 'Process Image'.")
                    input_image = gr.Image(label="Input",width="100%",container=True)
                    process_button = gr.Button("Process Image")
                with gr.Column(scale=1):
                    examples_fossils,examples_leaves = setup_examples()
                    
        gr.Markdown(CITATION)
                
    with gr.Tab("Specimen Workbench"):
        with gr.Row():
            with gr.Column():
                original_image = gr.Image(visible = False)
                workbench_image = gr.Image(label="Workbench Image")
                classify_image_button = gr.Button("Classify Image")
            
            # with gr.Column():
            #     #segmented_image = gr.outputs.Image(label="SAM output",type='numpy')
            #     segmented_image=gr.Image(label="Segmented Image", type='numpy')
            #     segment_button = gr.Button("Segment Image")
            #     #classify_segmented_button = gr.Button("Classify Segmented Image")
                
            with gr.Column():
                model_name = gr.Dropdown(
                    ["Mummified 170", "Rock 170","Fossils 142","Fossils new"],
                    multiselect=False,
                    value="Fossils new", # default option
                    label="Model",
                    interactive=True,
                    info="Choose the model you'd like to use"
                )
                explain_method = gr.Dropdown(
                    ["Sobol", "HSIC","Rise","Saliency"],
                    multiselect=False,
                    value="Rise", # default option
                    label="Explain method",
                    interactive=True,
                    info="Choose one method to explain the model"
                )
                # explain_method = gr.CheckboxGroup(["Sobol", "HSIC","Rise","Saliency"],
                #                                   label="explain method",
                #                                   value="Rise",
                #                                   multiselect=False,
                #                                   interactive=True,)
                sampling_size = gr.Slider(1, 30, value=5, label="Sampling Size in Rise",interactive=True,visible=True,
                                          info="Choose between 1 and 30")
                
                top_k = gr.Slider(10,200,value=50,label="Number of Closest Samples for Distribution Chart",interactive=True,info="Choose between 10 and 200")
                explain_method.change(
                    fn=update_slider_visibility, 
                    inputs=explain_method, 
                    outputs=sampling_size
                )
        with gr.Row():
            with gr.Column(scale=1):
                class_predicted = gr.Label(label='Class Predicted',num_top_classes=10)
            with gr.Column(scale=4):
                with gr.Accordion("Explanations "):
                    gr.Markdown("Computing Explanations from the model")
                    with gr.Column():
                        with gr.Row():

                        #original_input = gr.Image(label="Original Frame")
                        #saliency  = gr.Image(label="saliency")
                        #gradcam = gr.Image(label='integraged gradients')
                        #guided_gradcam = gr.Image(label='gradcam')
                        #guided_backprop = gr.Image(label='guided backprop')
                            # exp1 = gr.Image(label = 'Class_name1')
                            # exp2= gr.Image(label = 'Class_name2')
                            # exp3= gr.Image(label = 'Class_name3')
                            # exp4= gr.Image(label = 'Class_name4')
                            # exp5= gr.Image(label = 'Class_name5')
                        
                            exp_gallery = gr.Gallery(label="Explanation Heatmaps for top 5 predicted classes", show_label=False,elem_id="gallery",columns=[5], rows=[1],height='auto', allow_preview=True, preview=None)
                    
                    generate_explanations = gr.Button("Generate Explanations")
                        
                # with gr.Accordion('Closest Images'):
                #     gr.Markdown("Finding the closest images in the dataset")
                #     with gr.Row():
                #         with gr.Column():
                #             label_closest_image_0 = gr.Markdown('')
                #             closest_image_0 = gr.Image(label='Closest Image',image_mode='contain',width=200, height=200)
                #         with gr.Column():
                #             label_closest_image_1 = gr.Markdown('')
                #             closest_image_1 = gr.Image(label='Second Closest Image',image_mode='contain',width=200, height=200)
                #         with gr.Column():
                #             label_closest_image_2 = gr.Markdown('')
                #             closest_image_2 = gr.Image(label='Third Closest Image',image_mode='contain',width=200, height=200)
                #         with gr.Column():
                #             label_closest_image_3 = gr.Markdown('')
                #             closest_image_3 = gr.Image(label='Forth Closest Image',image_mode='contain', width=200, height=200)
                #         with gr.Column():
                #             label_closest_image_4 = gr.Markdown('')
                #             closest_image_4 = gr.Image(label='Fifth Closest Image',image_mode='contain',width=200, height=200)
                #     find_closest_btn = gr.Button("Find Closest Images")
                with gr.Accordion('Closest Fossil Images'):
                    gr.Markdown("Finding the closest images in the dataset")
                    
                    with gr.Row():
                        closest_gallery = gr.Gallery(label="Closest Images", show_label=False,elem_id="gallery",columns=[5], rows=[1],height='auto', allow_preview=True, preview=None)
                        #.style(grid=[1, 5], height=200, width=200)

                    find_closest_btn = gr.Button("Find Closest Images")
                    
                #segment_button.click(segment_image, inputs=input_image, outputs=segmented_image)
                classify_image_button.click(classify_image, inputs=[original_image,model_name], outputs=class_predicted)
                # generate_exp.click(exp_image, inputs=[input_image,model_name,explain_method,sampling_size], outputs=[exp1,exp2,exp3,exp4,exp5]) #
                with gr.Accordion('Closest Leaves Images'):
                    gr.Markdown("5 closest leaves")
                with gr.Accordion("Class Distribution of Closest Samples "):
                    gr.Markdown("Visualize class distribution of top-k closest samples in our dataset")
                    with gr.Column():
                        with gr.Row():
                            diagram= gr.Image(label = 'Bar Chart')
                    
                    generate_diagram = gr.Button("Generate Diagram")

               
            
        # with gr.Accordion("Using Diffuser"):
        #     with gr.Column(): 
        #         prompt = gr.Textbox(lines=1, label="Prompt")
        #         output_image = gr.Image(label="Output")
        #         generate_button = gr.Button("Generate Leave")    
        #     with gr.Column():
        #         class_predicted2 = gr.Label(label='Class Predicted from diffuser')
        #         classify_button = gr.Button("Classify Image")


        def update_exp_outputs(input_image,model_name,explain_method,nb_samples):
            labels, images = explain_image(input_image,model_name,explain_method,nb_samples)
            #labels_html = "".join([f'<div style="display: inline-block; text-align: center; width: 18%;">{label}</div>' for label in labels])
            #labels_markdown = f"<div style='width: 100%; text-align: center;'>{labels_html}</div>"
            image_caption=[]
            for i in range(5):
                image_caption.append((images[i],"Predicted Class "+str(i)+": "+labels[i]))
            return image_caption

        generate_explanations.click(fn=update_exp_outputs, inputs=[original_image,model_name,explain_method,sampling_size], outputs=[exp_gallery])

        #find_closest_btn.click(find_closest, inputs=[input_image,model_name], outputs=[label_closest_image_0,label_closest_image_1,label_closest_image_2,label_closest_image_3,label_closest_image_4,closest_image_0,closest_image_1,closest_image_2,closest_image_3,closest_image_4])
        def update_closest_outputs(input_image,model_name):
            labels, images = find_closest(input_image,model_name)
            #labels_html = "".join([f'<div style="display: inline-block; text-align: center; width: 18%;">{label}</div>' for label in labels])
            #labels_markdown = f"<div style='width: 100%; text-align: center;'>{labels_html}</div>"
            image_caption=[]
            for i in range(5):
                image_caption.append((images[i],labels[i]))
            return image_caption

        find_closest_btn.click(fn=update_closest_outputs, inputs=[original_image,model_name], outputs=[closest_gallery])
        #classify_segmented_button.click(classify_image, inputs=[segmented_image,model_name], outputs=class_predicted)

        generate_diagram.click(generate_diagram_closest, inputs=[original_image,model_name,top_k], outputs=diagram)

    process_button.click(
                        fn=update_display,
                        inputs=input_image,
                        outputs=[original_image,input_image,workbench_image,instruction_text,model_name,explain_method,sampling_size,top_k,class_predicted,exp_gallery,closest_gallery,diagram]
                    )

    
        
        
demo.queue()     # manage multiple incoming requests
   
if os.getenv('SYSTEM') == 'spaces':
    demo.launch(width='40%',auth=(os.environ.get('USERNAME'), os.environ.get('PASSWORD')))
else: 
    demo.launch()