refine the gradio app

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1 @@
+venv

app.py

@@ -1,118 +1,628 @@
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+# 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 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 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):
-        # 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 = 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)
+        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)
+        return ssim(img1_gray, img2_gray, data_range=img1_gray.max() - img1_gray.min())
 
-def process_images(reference_image, comparison_images, similarity_threshold):
+def process_images(reference_image_array, comparison_files, similarity_threshold):
     try:
-        if reference_image is None:
-            return "Please upload a reference image.", []
-        if not comparison_images:
-            return "Please upload comparison images.", []
+        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)
 
-        # Convert reference image to NumPy array
-        ref_image = Image.fromarray(reference_image)
-        ref_features = classifier.extract_features(ref_image)
+        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_image in comparison_images:
+        for comp_file_item in comparison_files:
             try:
-                # Read image file as PIL Image
-                comp_pil = Image.open(comp_image)
-                comp_pil = comp_pil.convert("RGB")
+                # 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.")
 
-                # Convert to NumPy format for SSIM
-                comp_array = np.array(comp_pil)
-
-                # Calculate SSIM score
-                ssim_score = classifier.calculate_ssim(reference_image, comp_array)
+                with open(file_path, "rb") as f:
+                    comp_pil = Image.open(BytesIO(f.read())).convert("RGB")
 
-                # Extract features
+                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))
-                is_similar = max_feature_diff < 6.0
-
-                status_text = "SIMILAR" if is_similar else "NOT SIMILAR"
+                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};'>{comp_image.name}: {status_text}</p>"
+                html_output += f"<p style='color:{status_color};'>{os.path.basename(file_path)}: {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>"
+                # 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, results
+        return html_output, [r for r in results if r is not None]
 
     except Exception as e:
-        return f"<p style='color:red;'>Error: {str(e)}</p>", []
+        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() as interface:
-        gr.Markdown("# Image Similarity Classifier")
-        gr.Markdown("Upload a reference image and multiple comparison images.")
+    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():
-                reference_input = gr.Image(label="Reference Image", type="numpy")
-                comparison_input = gr.Files(label="Comparison Images", type="filepath")
-                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")
+                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)
 
-            with gr.Column():
-                output_html = gr.HTML(label="Results")
-                output_gallery = gr.Gallery(label="Processed 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.launch(share=True)
+    interface.queue()
+    interface.launch()

requirements.txt

@@ -1,5 +1,4 @@
 tensorflow==2.10.0
-tensorflow-gpu==2.10.0
 keras==2.10.0
 numpy==1.23.5
 opencv-python==4.7.0.72