app.py

import gradio as gr
import cv2
import numpy as np
from skimage.metrics import structural_similarity as ssim
from PIL import Image
import pytesseract
import matplotlib.pyplot as plt

# Function to calculate SSIM between two images
def calculate_ssim(img1, img2):
    img1_gray = img1.convert("L")
    img2_gray = img2.convert("L")
    img1_resized = img1_gray.resize((256, 256))
    img2_resized = img2_gray.resize((256, 256))
    return ssim(np.array(img1_resized), np.array(img2_resized))

# Function to extract text from an image
def extract_text(image):
    text = pytesseract.image_to_string(image)
    return text

# Function to calculate color difference between two images
def calculate_color_difference(img1, img2):
    hist1 = cv2.calcHist([np.array(img1)], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
    hist2 = cv2.calcHist([np.array(img2)], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
    return cv2.compareHist(hist1, hist2, cv2.HISTCMP_CHISQR)

# Function to compare two trademarks
def compare_trademarks(trademark1, trademark2):
    ssim_score = calculate_ssim(trademark1, trademark2)
    text1 = extract_text(trademark1)
    text2 = extract_text(trademark2)
    text_similarity = calculate_text_similarity(text1, text2)
    color_difference = calculate_color_difference(trademark1, trademark2)
    return ssim_score, text_similarity, color_difference

# Function to calculate text similarity ratio
def calculate_text_similarity(text1, text2):
    if text1 == "" or text2 == "":
        return 0.0
    text1 = text1.lower()
    text2 = text2.lower()
    intersection = len(set(text1.split()) & set(text2.split()))
    union = len(set(text1.split()) | set(text2.split()))
    return intersection / union

# Function to handle the Gradio interface
def prevent_trademark_conflict(trademark1, trademark2):
    ssim_score, text_similarity, color_difference = compare_trademarks(trademark1, trademark2)
    result = f"SSIM Score: {ssim_score:.2f}\nText Similarity: {text_similarity:.2f}\nColor Difference: {color_difference:.2f}"
    return result

# Prepare Gradio interface
iface = gr.Interface(
    fn=prevent_trademark_conflict,
    inputs=[
        gr.inputs.Image(type="pil", label="Trademark Image 1"),
        gr.inputs.Image(type="pil", label="Trademark Image 2")
    ],
    outputs="text",
    title="Trademark Similarity",
    description="Compare two trademarks to check for similarity."
)

# Launch the interface
iface.launch()