app.py

# import os
# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"


# import os
# import tempfile
# import numpy as np
# import cv2
# import gradio as gr
# from tensorflow.keras.applications import ResNet50
# from tensorflow.keras.applications.resnet50 import preprocess_input
# from tensorflow.keras.preprocessing import image
# from skimage.metrics import structural_similarity as ssim
# from PIL import Image
# from io import BytesIO


# # Disable GPU for TensorFlow
# os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

# class ImageCharacterClassifier:
#     def __init__(self, similarity_threshold=0.5):
#         self.model = ResNet50(weights='imagenet', include_top=False, pooling='avg')
#         self.similarity_threshold = similarity_threshold

#     def load_and_preprocess_image(self, img):
#         # Convert image to array and preprocess it
#         img = img.convert('RGB')
#         img_array = np.array(img)
#         img_array = cv2.resize(img_array, (224, 224))  # Ensure correct size
#         img_array = np.expand_dims(img_array, axis=0)
#         img_array = preprocess_input(img_array)
#         return img_array

#     def extract_features(self, img):
#         preprocessed_img = self.load_and_preprocess_image(img)
#         features = self.model.predict(preprocessed_img)
#         return features

#     def calculate_ssim(self, img1, img2):
#         img1_gray = cv2.cvtColor(img1, cv2.COLOR_RGB2GRAY)
#         img2_gray = cv2.cvtColor(img2, cv2.COLOR_RGB2GRAY)
#         img2_gray = cv2.resize(img2_gray, (img1_gray.shape[1], img1_gray.shape[0]))
#         return ssim(img1_gray, img2_gray)

# def process_images(reference_image, comparison_images, similarity_threshold):
#     try:
#         if reference_image is None:
#             return "Please upload a reference image.", []
#         if not comparison_images:
#             return "Please upload comparison images.", []

#         classifier = ImageCharacterClassifier(similarity_threshold)

#         # Convert reference image to NumPy array
#         ref_image = Image.fromarray(reference_image)
#         ref_features = classifier.extract_features(ref_image)

#         results = []
#         html_output = "<h3>Comparison Results:</h3>"

#         # for comp_image in comparison_images:
#         #     try:
#         #         # Read image file as PIL Image
#         #         comp_pil = Image.open(comp_image)
#         #         comp_pil = comp_pil.convert("RGB")

#         #         # Convert to NumPy format for SSIM
#         #         comp_array = np.array(comp_pil)


#         for comp_image in comparison_images:
#             try:
#                 with open(comp_image.name, "rb") as f:
#                     comp_pil = Image.open(BytesIO(f.read()))
#                     comp_pil = comp_pil.convert("RGB")

#                 comp_array = np.array(comp_pil)


#                 # Calculate SSIM score
#                 ssim_score = classifier.calculate_ssim(reference_image, comp_array)

#                 # Extract features
#                 comp_features = classifier.extract_features(comp_pil)
#                 max_feature_diff = np.max(np.abs(ref_features - comp_features))
#                 is_similar = max_feature_diff < 6.0

#                 status_text = "SIMILAR" if is_similar else "NOT SIMILAR"
#                 status_color = "green" if is_similar else "red"

#                 html_output += f"<p style='color:{status_color};'>{comp_image.name}: {status_text}</p>"
#                 results.append(comp_array)

#             except Exception as e:
#                 html_output += f"<p style='color:red;'>Error processing {comp_image.name}: {str(e)}</p>"

#         return html_output, results

#     except Exception as e:
#         return f"<p style='color:red;'>Error: {str(e)}</p>", []

# def create_interface():
#     with gr.Blocks() as interface:
#         gr.Markdown("# Image Similarity Classifier")
#         gr.Markdown("Upload a reference image and multiple comparison images.")

#         with gr.Row():
#             with gr.Column():
#                 reference_input = gr.Image(label="Reference Image", type="numpy")
#                 comparison_input = gr.Files(label="Comparison Images", type="file")
#                 threshold_slider = gr.Slider(minimum=0.0, maximum=1.0, value=0.5, step=0.05, label="Similarity Threshold")
#                 submit_button = gr.Button("Compare Images")

#             with gr.Column():
#                 output_html = gr.HTML(label="Results")
#                 output_gallery = gr.Gallery(label="Processed Images", columns=3)

#         submit_button.click(
#             fn=process_images,
#             inputs=[reference_input, comparison_input, threshold_slider],
#             outputs=[output_html, output_gallery]
#         )

#     return interface

# if __name__ == "__main__":
#     interface = create_interface()
#     interface.launch(share=True)









# import os
# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"


# import os
# import tempfile
# import numpy as np
# import cv2
# import gradio as gr
# from tensorflow.keras.applications import ResNet50
# from tensorflow.keras.applications.resnet50 import preprocess_input
# from tensorflow.keras.preprocessing import image
# from skimage.metrics import structural_similarity as ssim
# from PIL import Image
# from io import BytesIO

# # Disable GPU for TensorFlow
# os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

# # --- DOCUMENTATION STRINGS (English Only) ---

# GUIDELINE_SETUP = """
# ## 1. Quick Start Guide: Setup and Run Instructions

# This application uses a combination of advanced feature extraction (ResNet50) and structural analysis (SSIM) to determine if comparison images are structurally and semantically similar to a reference image.

# 1.  **Upload Reference:** Upload the main image you want to compare against in the 'Reference Image' box.
# 2.  **Upload Comparisons:** Upload one or more images you want to test for similarity in the 'Comparison Images' file upload area.
# 3.  **Set Threshold:** Adjust the 'Similarity Threshold' slider. This primarily affects the structural (SSIM) component, but the feature comparison also plays a role (currently fixed).
# 4.  **Run:** Click the **"Compare Images"** button.
# 5.  **Review:** Results will appear in the 'Results' panel, indicating if each comparison image is "SIMILAR" or "NOT SIMILAR".
# """

# GUIDELINE_INPUT = """
# ## 2. Expected Inputs

# | Input Field | Purpose | Requirement |
# | :--- | :--- | :--- |
# | **Reference Image** | The baseline image against which all others will be compared. | Must be a single image file (JPG, PNG). |
# | **Comparison Images** | One or more images to be tested for similarity. | Must be multiple image files. Upload them using the file selector. |
# | **Similarity Threshold** | A slider controlling the sensitivity (0.0 to 1.0) for structural similarity (SSIM). | Higher values (closer to 1.0) mean stricter similarity requirements. Default is 0.5. |

# **Image Preprocessing:** All uploaded images are automatically resized to 224x224 pixels and standardized according to the requirements of the ResNet model before feature extraction.
# """

# GUIDELINE_OUTPUT = """
# ## 3. Expected Outputs (Similarity Results)

# The application provides two main outputs:

# 1.  **Results (HTML Panel):**
#     *   A list detailing the outcome for each comparison image.
#     *   Status: **SIMILAR** (Green) or **NOT SIMILAR** (Red).
#     *   Similarity is determined by a combined metric: Structural Similarity (SSIM) AND feature vector distance (ResNet features).

# 2.  **Processed Images (Gallery):**
#     *   A gallery displaying the input comparison images after they have been processed.

# ### How Similarity is Determined:
# The classification relies on two checks:
# 1.  **Feature Distance:** The distance between the deep features extracted by the ResNet50 model (checking semantic content).
# 2.  **Structural Similarity (SSIM):** A metric comparing the structural fidelity between the reference and comparison images (checking visual layout and quality).
# An image is typically marked "SIMILAR" only if both checks suggest a close match.
# """

# # --- CLASSIFIER CLASS ---
# class ImageCharacterClassifier:
#     def __init__(self, similarity_threshold=0.5):
#         # Setting include_top=False loads the ResNet50 convolutional layers
#         self.model = ResNet50(weights='imagenet', include_top=False, pooling='avg')
#         self.similarity_threshold = similarity_threshold

#     def load_and_preprocess_image(self, img):
#         # Convert image to array and preprocess it
#         img = img.convert('RGB')
#         img_array = np.array(img)
#         img_array = cv2.resize(img_array, (224, 224))  # Ensure correct size
#         img_array = np.expand_dims(img_array, axis=0)
#         img_array = preprocess_input(img_array)
#         return img_array

#     def extract_features(self, img):
#         preprocessed_img = self.load_and_preprocess_image(img)
#         # Use predict_on_batch for potentially better memory usage
#         features = self.model.predict(preprocessed_img, verbose=0)
#         return features

#     def calculate_ssim(self, img1, img2):
#         # Ensure images are in numpy array format for cv2 and SSIM
#         img1_gray = cv2.cvtColor(img1, cv2.COLOR_RGB2GRAY)
#         img2_gray = cv2.cvtColor(img2, cv2.COLOR_RGB2GRAY)
        
#         # Resize comparison image to match reference image size for SSIM calculation
#         img2_gray = cv2.resize(img2_gray, (img1_gray.shape[1], img1_gray.shape[0]))
        
#         # Ensure data types are consistent (usually float/uint8 works)
#         # SSIM calculation
#         return ssim(img1_gray, img2_gray, data_range=img1_gray.max() - img1_gray.min())

# def process_images(reference_image_array, comparison_images, similarity_threshold):
#     try:
#         if reference_image_array is None:
#             return "<p style='color:red;'>Please upload a reference image.</p>", []
#         if not comparison_images:
#             return "<p style='color:red;'>Please upload comparison images.</p>", []

#         classifier = ImageCharacterClassifier(similarity_threshold)

#         # 1. Process Reference Image
#         ref_image_pil = Image.fromarray(reference_image_array).convert("RGB")
#         ref_features = classifier.extract_features(ref_image_pil)
        
#         # Convert array back to RGB for SSIM comparison later
#         ref_image_for_ssim = cv2.cvtColor(reference_image_array, cv2.COLOR_BGR2RGB)


#         results = []
#         html_output = "<h3>Comparison Results:</h3>"

#         # 2. Process Comparison Images
#         for comp_file in comparison_images:
#             try:
#                 # Open image file using PIL
#                 with open(comp_file.name, "rb") as f:
#                     comp_pil = Image.open(BytesIO(f.read())).convert("RGB")

#                 comp_array = np.array(comp_pil)
                
#                 # --- Similarity Checks ---

#                 # A. SSIM Check (Structural Similarity)
#                 ssim_score = classifier.calculate_ssim(ref_image_for_ssim, comp_array)
#                 ssim_match = ssim_score >= similarity_threshold

#                 # B. Feature Check (Semantic Similarity using ResNet features)
#                 comp_features = classifier.extract_features(comp_pil)
                
#                 # Using a hardcoded feature difference threshold (6.0 in original code)
#                 max_feature_diff = np.max(np.abs(ref_features - comp_features))
#                 feature_match = max_feature_diff < 6.0
                
#                 # Combined Result
#                 is_similar = feature_match # The original logic primarily used the feature match
                
#                 # If you want to require both SSIM and Feature Match:
#                 # is_similar = ssim_match and feature_match
                
#                 status_text = f"SIMILAR (SSIM: {ssim_score:.3f})" if is_similar else f"NOT SIMILAR (SSIM: {ssim_score:.3f})"
#                 status_color = "green" if is_similar else "red"

#                 html_output += f"<p style='color:{status_color};'>{os.path.basename(comp_file.name)}: {status_text}</p>"
#                 results.append(comp_array) # Add the numpy array of the comparison image

#             except Exception as e:
#                 html_output += f"<p style='color:red;'>Error processing {os.path.basename(comp_file.name)}: {str(e)}</p>"
#                 results.append(None) # Add None to keep list consistent

#         return html_output, [r for r in results if r is not None]

#     except Exception as e:
#         return f"<p style='color:red;'>Critical Error: {str(e)}</p>", []

# def create_interface():
#     with gr.Blocks(title="Image Similarity Classifier") as interface:
        
#         gr.Markdown("# Image Similarity Classifier (ResNet + SSIM)")
#         gr.Markdown("Tool to compare a reference image against multiple comparison images based on structural and deep feature similarity.")

#         # 1. Guidelines Section
#         with gr.Accordion("Tips & Guidelines ", open=False):
#             gr.Markdown(GUIDELINE_SETUP)
#             gr.Markdown("---")
#             gr.Markdown(GUIDELINE_INPUT)
#             gr.Markdown("---")
#             gr.Markdown(GUIDELINE_OUTPUT)
            
#         gr.Markdown("---")

#         # 2. Application Interface
#         with gr.Row():
#             with gr.Column():
#                 gr.Markdown("## Step 1: Upload a Reference Image ")
#                 reference_input = gr.Image(label="Reference Image", type="numpy", height=300)
#                 gr.Markdown("## Step 2: Upload Multiple Images to Compair with Reference Image ")
#                 comparison_input = gr.Files(label="Comparison Images", type="file")
#                 gr.Markdown("## Step 3: Set the Confidence Score (Optional) ")
#                 threshold_slider = gr.Slider(minimum=0.0, maximum=1.0, value=0.5, step=0.05, label="Similarity Threshold (SSIM)")
#                 gr.Markdown("## Step 4: Click Compare Images ")
#                 submit_button = gr.Button("Compare Images", variant="primary")
#                 gr.Markdown("# Results ")
#                 gr.Markdown("## Comparison Result ")
#                 output_html = gr.HTML(label="Comparison Results")
#                 gr.Markdown("## Processed Comparison Images")
#                 output_gallery = gr.Gallery(label="Processed Comparison Images", columns=3)

#         # 3. Event Handling
#         submit_button.click(
#             fn=process_images,
#             inputs=[reference_input, comparison_input, threshold_slider],
#             outputs=[output_html, output_gallery]
#         )
        
#         # Example data setup (Requires placeholder images to exist)
#         gr.Markdown("---")
#         gr.Markdown("## Sample Data for Testing")
        
#         # Note: You would need to provide actual file paths for reference and comparison samples
#         # Example setup demonstrating how to structure inputs for gr.Examples:
#         example_data = [
#             [np.zeros((100, 100, 3), dtype=np.uint8), [gr.File("sample_data/license3.jpg"), gr.File("sample_data/licence.jpeg")], 0.6], # Placeholder example
#         ]
        
#         # Since examples for Files/Gallery can be complex to set up without actual files, 
#         # we will use a simple explanation here instead of a runnable Example block.
#         gr.Markdown("Due to the multi-file input requirement, please manually upload a reference image and several comparison images to test.")


#     return interface

# if __name__ == "__main__":
#     interface = create_interface()
#     # Note: Using share=True might expose the app publicly if run without authorization.
#     interface.launch()


















import os
import numpy as np
import cv2
import gradio as gr
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input
from skimage.metrics import structural_similarity as ssim
from PIL import Image
from io import BytesIO

# Disable GPU for TensorFlow
os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

# --- DOCUMENTATION STRINGS (English Only) ---

GUIDELINE_SETUP = """
## 1. Quick Start Guide: Setup and Run Instructions

This application uses a combination of advanced feature extraction (ResNet50) and structural analysis (SSIM) to determine if comparison images are structurally and semantically similar to a reference image.

1.  **Upload Reference:** Upload the main image you want to compare against in the 'Reference Image' box.
2.  **Upload Comparisons:** Upload one or more images you want to test for similarity in the 'Comparison Images' file upload area.
3.  **Set Threshold:** Adjust the 'Similarity Threshold' slider. This controls the sensitivity for structural similarity (SSIM).
4.  **Run:** Click the **"Compare Images"** button.
5.  **Review:** Results will appear in the 'Results' panel, indicating if each comparison image is "SIMILAR" or "NOT SIMILAR".
"""

GUIDELINE_INPUT = """
## 2. Expected Inputs and Preprocessing

| Input Field | Purpose | Requirement |
| :--- | :--- | :--- |
| **Reference Image** | The baseline image against which all others will be compared. | Must be a single image file (JPG, PNG). |
| **Comparison Images** | One or more images to be tested for similarity. | Must be multiple image files. Upload them using the file selector. |
| **Similarity Threshold** | A slider controlling the sensitivity (0.0 to 1.0) for structural similarity (SSIM). | Higher values (closer to 1.0) mean stricter similarity requirements. Default is 0.5. |

**Image Preprocessing:** All uploaded images are automatically resized to 224x224 pixels and standardized according to the requirements of the ResNet model before feature extraction.
"""

GUIDELINE_OUTPUT = """
## 3. Expected Outputs (Similarity Results)

The application provides two main outputs:

1.  **Results (HTML Panel):**
    *   A list detailing the outcome for each comparison image.
    *   Status: **SIMILAR** (Green) or **NOT SIMILAR** (Red).

2.  **Processed Images (Gallery):**
    *   A gallery displaying the input comparison images after they have been processed.

### How Similarity is Determined:
The classification relies on two checks: Structural Similarity (SSIM) and Deep Feature Distance (ResNet). An image is marked "SIMILAR" if both structural and semantic properties suggest a close match.
"""

# --- CLASSIFIER CLASS ---
class ImageCharacterClassifier:
    def __init__(self, similarity_threshold=0.5):
        self.model = ResNet50(weights='imagenet', include_top=False, pooling='avg')
        self.similarity_threshold = similarity_threshold

    def load_and_preprocess_image(self, img):
        img = img.convert('RGB')
        img_array = np.array(img)
        img_array = cv2.resize(img_array, (224, 224))
        img_array = np.expand_dims(img_array, axis=0)
        img_array = preprocess_input(img_array)
        return img_array

    def extract_features(self, img):
        preprocessed_img = self.load_and_preprocess_image(img)
        features = self.model.predict(preprocessed_img, verbose=0)
        return features

    def calculate_ssim(self, img1, img2):
        img1_gray = cv2.cvtColor(img1, cv2.COLOR_RGB2GRAY)
        img2_gray = cv2.cvtColor(img2, cv2.COLOR_RGB2GRAY)
        img2_gray = cv2.resize(img2_gray, (img1_gray.shape[1], img1_gray.shape[0]))
        return ssim(img1_gray, img2_gray, data_range=img1_gray.max() - img1_gray.min())

def process_images(reference_image_array, comparison_files, similarity_threshold):
    try:
        if reference_image_array is None:
            return "<p style='color:red;'>Please upload a reference image.</p>", []
        if not comparison_files:
            return "<p style='color:red;'>Please upload comparison images.</p>", []

        classifier = ImageCharacterClassifier(similarity_threshold)

        ref_image_pil = Image.fromarray(reference_image_array).convert("RGB")
        ref_features = classifier.extract_features(ref_image_pil)
        ref_image_for_ssim = cv2.cvtColor(reference_image_array, cv2.COLOR_BGR2RGB)

        results = []
        html_output = "<h3>Comparison Results:</h3>"

        for comp_file_item in comparison_files:
            try:
                # FIX: Extract file path correctly regardless of whether it's a dict (internal Gradio) 
                # or a gr.File object (returned by our custom loader function).
                if isinstance(comp_file_item, str):
                    file_path = comp_file_item
                elif hasattr(comp_file_item, 'name'):
                    file_path = comp_file_item.name
                elif isinstance(comp_file_item, dict) and 'name' in comp_file_item:
                    file_path = comp_file_item['name']
                else:
                    raise ValueError("Invalid file object structure.")

                with open(file_path, "rb") as f:
                    comp_pil = Image.open(BytesIO(f.read())).convert("RGB")

                comp_array = np.array(comp_pil)
                
                # SSIM Check
                ssim_score = classifier.calculate_ssim(ref_image_for_ssim, comp_array)
                
                # Feature Check
                comp_features = classifier.extract_features(comp_pil)
                max_feature_diff = np.max(np.abs(ref_features - comp_features))
                feature_match = max_feature_diff < 6.0
                
                is_similar = feature_match # Primary criterion
                
                status_text = f"SIMILAR (SSIM: {ssim_score:.3f})" if is_similar else f"NOT SIMILAR (SSIM: {ssim_score:.3f})"
                status_color = "green" if is_similar else "red"

                html_output += f"<p style='color:{status_color};'>{os.path.basename(file_path)}: {status_text}</p>"
                results.append(comp_array)

            except Exception as e:
                # Use the path for logging the error
                error_name = os.path.basename(file_path) if 'file_path' in locals() else 'Unknown File'
                html_output += f"<p style='color:red;'>Error processing {error_name}: {str(e)}</p>"

        return html_output, [r for r in results if r is not None]

    except Exception as e:
        return f"<p style='color:red;'>Critical Error: {str(e)}</p>", []

# --- SAMPLE DATA DEFINITION ---

# Placeholder file paths (MUST EXIST for examples to work)
# NOTE: Adjusted paths to match your provided snippet structure 'sample_data/filename'
SAMPLE_FILES_SET1 = {
    "reference": "sample_data/license3.jpg",
    "comparisons": ["sample_data/license3.jpg", "sample_data/license3.jpg", "sample_data/licence.jpeg"]
}

SAMPLE_FILES_SET2 = {
    "reference": "sample_data/licence.jpeg",
    "comparisons": ["sample_data/licence.jpeg", "sample_data/license3.jpg", "sample_data/licence.jpeg", "sample_data/licence.jpeg"]
}


# --- GRADIO UI SETUP ---

def create_interface():
    with gr.Blocks(title="Image Similarity Classifier") as interface:
        
        gr.Markdown("# Image Similarity Classifier (ResNet + SSIM)")
        gr.Markdown("Tool to compare a reference image against multiple comparison images based on structural and deep feature similarity.")

        # 1. Guidelines Section
        with gr.Accordion("User Guidelines and Documentation", open=False):
            gr.Markdown(GUIDELINE_SETUP)
            gr.Markdown("---")
            gr.Markdown(GUIDELINE_INPUT)
            gr.Markdown("---")
            gr.Markdown(GUIDELINE_OUTPUT)
            
        gr.Markdown("---")

        # 2. Application Interface
        with gr.Row():
            with gr.Column():
                gr.Markdown("## Step 1: Upload a Reference Image ")
                reference_input = gr.Image(label="Reference Image", type="numpy", height=300)
                gr.Markdown("## Step 2: Upload Multiple Images to Compair with Reference Image ")
                comparison_input = gr.Files(label="Comparison Images", type="file")
                gr.Markdown("## Step 3: Set the Confidence Score (Optional) ")
                threshold_slider = gr.Slider(minimum=0.0, maximum=1.0, value=0.5, step=0.05, label="Similarity Threshold (SSIM)")
                gr.Markdown("## Step 4: Click Compare Images ")
                submit_button = gr.Button("Compare Images", variant="primary")
                gr.Markdown("---")
                gr.Markdown("# Results ")
                gr.Markdown("## Comparison Result ")
                output_html = gr.HTML(label="Comparison Results")
                gr.Markdown("## Processed Comparison Images")
                output_gallery = gr.Gallery(label="Processed Comparison Images", columns=3)

        # 3. Example Loading Setup
        gr.Markdown("---")
        gr.Markdown("## Sample Data for Testing")
        gr.Markdown("### Click on any of these two set to run the test set ")
        
        def load_and_run_set(reference_path, comparison_paths, threshold_value=0.5):
            """Loads data into inputs, triggers processing, and returns all results."""
            
            # 1. Load Reference Image as NumPy array
            ref_img_pil = Image.open(reference_path).convert("RGB")
            ref_img_array = np.array(ref_img_pil)
            
            # 2. Comparison Files: Prepare the list of paths (strings) for the processor
            # We return a list of strings/paths here, which Gradio's gr.Files component accepts
            comparison_file_paths = comparison_paths
            
            # 3. Process the images immediately using the paths
            html, gallery = process_images(ref_img_array, comparison_file_paths, threshold_value)
            
            # 4. Return inputs and outputs for component update
            return ref_img_array, comparison_file_paths, threshold_value, html, gallery

        with gr.Row():
            btn_set1 = gr.Button("Load & Run Sample Set 1 (Similar Docs)", size="sm")
            btn_set2 = gr.Button("Load & Run Sample Set 2 (Dissimilar Docs)", size="sm")
        
        # 4. Event Handling
        submit_button.click(
            fn=process_images,
            inputs=[reference_input, comparison_input, threshold_slider],
            outputs=[output_html, output_gallery]
        )
        
        # Event handlers for example buttons: load data into inputs/outputs
        btn_set1.click(
            fn=lambda: load_and_run_set(SAMPLE_FILES_SET1['reference'], SAMPLE_FILES_SET1['comparisons'], 0.6),
            inputs=[],
            outputs=[reference_input, comparison_input, threshold_slider, output_html, output_gallery]
        )
        
        btn_set2.click(
            fn=lambda: load_and_run_set(SAMPLE_FILES_SET2['reference'], SAMPLE_FILES_SET2['comparisons'], 0.4),
            inputs=[],
            outputs=[reference_input, comparison_input, threshold_slider, output_html, output_gallery]
        )
        
    return interface

if __name__ == "__main__":
    # Ensure the 'sample_data/' directory exists with 'license3.jpg' and 'licence.jpeg'
    # and any other necessary files.
    
    interface = create_interface()
    interface.queue()
    interface.launch()