Update app.py

app.py

@@ -1,125 +1,125 @@
-import gradio as gr
-import numpy as np
-import tensorflow as tf
-import logging
-from PIL import Image
-from tensorflow.keras.preprocessing import image as keras_image
-from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input as resnet_preprocess
-from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input as vgg_preprocess
-import scipy.fftpack
-import time
-import clip
-import torch
-
-# Set up logging
-logging.basicConfig(level=logging.INFO)
-
-# Load models
-resnet_model = ResNet50(weights='imagenet', include_top=False, pooling='avg')
-vgg_model = VGG16(weights='imagenet', include_top=False, pooling='avg')
-clip_model, preprocess_clip = clip.load("ViT-B/32", device="cpu")
-
-# Preprocess function
-def preprocess_img(img_path, target_size=(224, 224), preprocess_func=resnet_preprocess):
-    start_time = time.time()
-    img = keras_image.load_img(img_path, target_size=target_size)
-    img_array = keras_image.img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0)
-    img_array = preprocess_func(img_array)
-    logging.info(f"Image preprocessed in {time.time() - start_time:.4f} seconds")
-    return img_array
-
-# Feature extraction function
-def extract_features(img_path, model, preprocess_func):
-    img_array = preprocess_img(img_path, preprocess_func=preprocess_func)
-    start_time = time.time()
-    features = model.predict(img_array)
-    logging.info(f"Features extracted in {time.time() - start_time:.4f} seconds")
-    return features.flatten()
-
-# Calculate cosine similarity
-def cosine_similarity(vec1, vec2):
-    return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
-
-# pHash related functions
-def phashstr(image, hash_size=8, highfreq_factor=4):
-    img_size = hash_size * highfreq_factor
-    image = image.convert('L').resize((img_size, img_size), Image.Resampling.LANCZOS)
-    pixels = np.asarray(image)
-    dct = scipy.fftpack.dct(scipy.fftpack.dct(pixels, axis=0), axis=1)
-    dctlowfreq = dct[:hash_size, :hash_size]
-    med = np.median(dctlowfreq)
-    diff = dctlowfreq > med
-    return _binary_array_to_hex(diff.flatten())
-
-def _binary_array_to_hex(arr):
-    h = 0
-    s = []
-    for i, v in enumerate(arr):
-        if v:
-            h += 2**(i % 8)
-        if (i % 8) == 7:
-            s.append(hex(h)[2:].rjust(2, '0'))
-            h = 0
-    return ''.join(s)
-
-def hamming_distance(hash1, hash2):
-    if len(hash1) != len(hash2):
-        raise ValueError("Hashes must be of the same length")
-    return sum(c1 != c2 for c1, c2 in zip(hash1, hash2))
-
-def hamming_to_similarity(distance, hash_length):
-    return (1 - distance / hash_length) * 100
-
-# CLIP related functions
-def extract_clip_features(image_path, model, preprocess):
-    image = preprocess(Image.open(image_path)).unsqueeze(0).to("cpu")
-    with torch.no_grad():
-        features = model.encode_image(image)
-    return features.cpu().numpy().flatten()
-
-# Main function
-def compare_images(image1, image2, method):
-    similarity = None
-    start_time = time.time()
-    if method == 'pHash':
-        img1 = Image.open(image1)
-        img2 = Image.open(image2)
-        hash1 = phashstr(img1)
-        hash2 = phashstr(img2)
-        distance = hamming_distance(hash1, hash2)
-        similarity = hamming_to_similarity(distance, len(hash1) * 4)
-    elif method == 'ResNet50':
-        features1 = extract_features(image1, resnet_model, resnet_preprocess)
-        features2 = extract_features(image2, resnet_model, resnet_preprocess)
-        similarity = cosine_similarity(features1, features2)
-    elif method == 'VGG16':
-        features1 = extract_features(image1, vgg_model, vgg_preprocess)
-        features2 = extract_features(image2, vgg_model, vgg_preprocess)
-        similarity = cosine_similarity(features1, features2)
-    elif method == 'CLIP':
-        features1 = extract_clip_features(image1, clip_model, preprocess_clip)
-        features2 = extract_clip_features(image2, clip_model, preprocess_clip)
-        similarity = cosine_similarity(features1, features2)
-    
-    logging.info(f"Image comparison using {method} completed in {time.time() - start_time:.4f} seconds")
-    return similarity
-
-# Gradio interface
-demo = gr.Interface(
-    fn=compare_images,
-    inputs=[
-        gr.Image(type="filepath", label="Upload First Image"),
-        gr.Image(type="filepath", label="Upload Second Image"),
-        gr.Radio(["pHash", "ResNet50", "VGG16", "CLIP"], label="Select Comparison Method")
-    ],
-    outputs=gr.Textbox(label="Similarity"),
-    title="Image Similarity Comparison",
-    description="Upload two images and select the comparison method.",
-    examples=[
-        ["Snipaste_2024-05-31_16-18-31.jpg", "Snipaste_2024-05-31_16-18-52.jpg"],
-        ["example1.png", "example2.png"]
-    ]
-)
-
-demo.launch()
+import gradio as gr
+import numpy as np
+import tensorflow as tf
+import logging
+from PIL import Image
+from tensorflow.keras.preprocessing import image as keras_image
+from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input as resnet_preprocess
+from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input as vgg_preprocess
+import scipy.fftpack
+import time
+import clip
+import torch
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+
+# Load models
+resnet_model = ResNet50(weights='imagenet', include_top=False, pooling='avg')
+vgg_model = VGG16(weights='imagenet', include_top=False, pooling='avg')
+clip_model, preprocess_clip = clip.load("ViT-B/32", device="cpu")
+
+# Preprocess function
+def preprocess_img(img_path, target_size=(224, 224), preprocess_func=resnet_preprocess):
+    start_time = time.time()
+    img = keras_image.load_img(img_path, target_size=target_size)
+    img_array = keras_image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = preprocess_func(img_array)
+    logging.info(f"Image preprocessed in {time.time() - start_time:.4f} seconds")
+    return img_array
+
+# Feature extraction function
+def extract_features(img_path, model, preprocess_func):
+    img_array = preprocess_img(img_path, preprocess_func=preprocess_func)
+    start_time = time.time()
+    features = model.predict(img_array)
+    logging.info(f"Features extracted in {time.time() - start_time:.4f} seconds")
+    return features.flatten()
+
+# Calculate cosine similarity
+def cosine_similarity(vec1, vec2):
+    return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
+
+# pHash related functions
+def phashstr(image, hash_size=8, highfreq_factor=4):
+    img_size = hash_size * highfreq_factor
+    image = image.convert('L').resize((img_size, img_size), Image.Resampling.LANCZOS)
+    pixels = np.asarray(image)
+    dct = scipy.fftpack.dct(scipy.fftpack.dct(pixels, axis=0), axis=1)
+    dctlowfreq = dct[:hash_size, :hash_size]
+    med = np.median(dctlowfreq)
+    diff = dctlowfreq > med
+    return _binary_array_to_hex(diff.flatten())
+
+def _binary_array_to_hex(arr):
+    h = 0
+    s = []
+    for i, v in enumerate(arr):
+        if v:
+            h += 2**(i % 8)
+        if (i % 8) == 7:
+            s.append(hex(h)[2:].rjust(2, '0'))
+            h = 0
+    return ''.join(s)
+
+def hamming_distance(hash1, hash2):
+    if len(hash1) != len(hash2):
+        raise ValueError("Hashes must be of the same length")
+    return sum(c1 != c2 for c1, c2 in zip(hash1, hash2))
+
+def hamming_to_similarity(distance, hash_length):
+    return (1 - distance / hash_length) * 100
+
+# CLIP related functions
+def extract_clip_features(image_path, model, preprocess):
+    image = preprocess(Image.open(image_path)).unsqueeze(0).to("cpu")
+    with torch.no_grad():
+        features = model.encode_image(image)
+    return features.cpu().numpy().flatten()
+
+# Main function
+def compare_images(image1, image2, method):
+    similarity = None
+    start_time = time.time()
+    if method == 'pHash':
+        img1 = Image.open(image1)
+        img2 = Image.open(image2)
+        hash1 = phashstr(img1)
+        hash2 = phashstr(img2)
+        distance = hamming_distance(hash1, hash2)
+        similarity = hamming_to_similarity(distance, len(hash1) * 4)
+    elif method == 'ResNet50':
+        features1 = extract_features(image1, resnet_model, resnet_preprocess)
+        features2 = extract_features(image2, resnet_model, resnet_preprocess)
+        similarity = cosine_similarity(features1, features2)
+    elif method == 'VGG16':
+        features1 = extract_features(image1, vgg_model, vgg_preprocess)
+        features2 = extract_features(image2, vgg_model, vgg_preprocess)
+        similarity = cosine_similarity(features1, features2)
+    elif method == 'CLIP':
+        features1 = extract_clip_features(image1, clip_model, preprocess_clip)
+        features2 = extract_clip_features(image2, clip_model, preprocess_clip)
+        similarity = cosine_similarity(features1, features2)
+    
+    logging.info(f"AI based Supporting Documents comparison using {method} completed in {time.time() - start_time:.4f} seconds")
+    return similarity
+
+# Gradio interface
+demo = gr.Interface(
+    fn=compare_images,
+    inputs=[
+        gr.Image(type="filepath", label="Upload First Image"),
+        gr.Image(type="filepath", label="Upload Second Image"),
+        gr.Radio(["pHash", "ResNet50", "VGG16", "CLIP"], label="Select Comparison Method")
+    ],
+    outputs=gr.Textbox(label="Similarity"),
+    title="AI based Customs Supporting Documents comparison",
+    description="Upload two images of Suppporting documents and select the comparison method.",
+    examples=[
+        ["Snipaste_2024-05-31_16-18-31.jpg", "Snipaste_2024-05-31_16-18-52.jpg"],
+        ["example1.png", "example2.png"]
+    ]
+)
+
+demo.launch()