Update app.py

app.py

@@ -1,71 +1,37 @@
-from PIL import Image
-from difflib import SequenceMatcher
-from skimage.metrics import structural_similarity as ssim
 import gradio as gr
-import pytesseract
+import cv2
+import numpy as np
+from skimage.metrics import structural_similarity as ssim
+from PIL import Image
 import io
 
-
 # Function to calculate SSIM between two images
 def calculate_ssim(img1, img2):
-    img1_gray = Image.open(img1).convert("L")
-    img2_gray = Image.open(img2).convert("L")
-    return ssim(img1_gray, img2_gray)
-
+    img1_gray = Image.open(io.BytesIO(img1)).convert("L")
+    img2_gray = Image.open(io.BytesIO(img2)).convert("L")
+    return ssim(np.array(img1_gray), np.array(img2_gray))
 
-# Function to extract text from an image using OCR
-def extract_text(image):
-    image = Image.open(image)
-    text = pytesseract.image_to_string(image).lower()
-    return text
-
-# Function to compare trademarks based on text similarity
-def compare_text(trademark1, trademark2):
-    text1 = extract_text(trademark1)
-    text2 = extract_text(trademark2)
-    similarity_ratio = SequenceMatcher(None, text1, text2).ratio()
-    return similarity_ratio
-
-# Function to compare trademarks based on color similarity
-def compare_colors(trademark1, trademark2):
-    trademark1 = trademark1.convert("RGB")
-    trademark2 = trademark2.convert("RGB")
-    colors1 = trademark1.getcolors(trademark1.size[0] * trademark1.size[1])
-    colors2 = trademark2.getcolors(trademark2.size[0] * trademark2.size[1])
-    color_vector1 = sum([(count * np.array(color)) for count, color in colors1]) / (trademark1.size[0] * trademark1.size[1])
-    color_vector2 = sum([(count * np.array(color)) for count, color in colors2]) / (trademark2.size[0] * trademark2.size[1])
-    color_similarity = 1 - np.linalg.norm(color_vector1 - color_vector2)
-    return color_similarity
-
-# Function to compare trademarks based on multiple aspects
+# Function to compare two trademarks
 def compare_trademarks(trademark1, trademark2):
     ssim_score = calculate_ssim(trademark1, trademark2)
-    text_similarity = compare_text(trademark1, trademark2)
-    color_similarity = compare_colors(trademark1, trademark2)
-
-    # Adjust weights based on the importance of each aspect
-    ssim_weight = 0.6
-    text_weight = 0.2
-    color_weight = 0.2
-
-    overall_similarity = (ssim_weight * ssim_score) + (text_weight * text_similarity) + (color_weight * color_similarity)
-    return overall_similarity
+    return ssim_score
 
-# Function to perform trademark conflict prevention
+# Function to handle the Gradio interface
 def prevent_trademark_conflict(trademark1, trademark2):
     similarity_score = compare_trademarks(trademark1, trademark2)
-    result = f"Trademark Similarity Score: {similarity_score:.4f}"
-    return result
+    return similarity_score
 
+# Prepare Gradio interface
 iface = gr.Interface(
     fn=prevent_trademark_conflict,
     inputs=[
-        gr.inputs.File(label="Trademark Image 1"),
-        gr.inputs.File(label="Trademark Image 2")
+        gr.inputs.Image(type="file", label="Trademark Image 1"),
+        gr.inputs.Image(type="file", label="Trademark Image 2")
     ],
     outputs="text",
-    title="Trademark Conflict Prevention",
-    description="Upload two trademark images. Get the trademark similarity score."
+    title="Trademark Similarity",
+    description="Compare two trademarks to check for similarity."
 )
 
+# Launch the interface
 iface.launch()