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import streamlit as st |
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import tensorflow as tf |
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import numpy as np |
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from PIL import Image |
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import requests |
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from io import BytesIO |
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from bs4 import BeautifulSoup |
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import pandas as pd |
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import os |
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def download_model(model_url, model_path): |
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if not os.path.exists(model_path): |
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response = requests.get(model_url) |
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with open(model_path, 'wb') as f: |
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f.write(response.content) |
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def load_model(model_path): |
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interpreter = tf.lite.Interpreter(model_path=model_path) |
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interpreter.allocate_tensors() |
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return interpreter |
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def preprocess_image(image, input_size): |
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image = image.convert('RGB') |
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image = image.resize((input_size, input_size)) |
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image_np = np.array(image, dtype=np.float32) |
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image_np = np.expand_dims(image_np, axis=0) |
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image_np = image_np / 255.0 |
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return image_np |
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def run_inference(interpreter, input_data): |
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input_details = interpreter.get_input_details() |
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output_details = interpreter.get_output_details() |
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interpreter.set_tensor(input_details[0]['index'], input_data) |
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interpreter.invoke() |
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output_data_shopping_intent = interpreter.get_tensor(output_details[0]['index']) |
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return output_data_shopping_intent |
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def fetch_images_from_url(url): |
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response = requests.get(url) |
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soup = BeautifulSoup(response.content, 'html.parser') |
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img_tags = soup.find_all('img') |
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img_urls = [img['src'] for img in img_tags if 'src' in img.attrs] |
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return img_urls |
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def render_intent_bars(labels, percentages): |
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for label, percentage in zip(labels, percentages): |
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bar_html = f""" |
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<div style='display: flex; align-items: center;'> |
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<div style='width: 30%; text-align: right; padding-right: 10px;'>{label}</div> |
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<div style='width: 70%; display: flex; align-items: center;'> |
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<div style='background-color: #007BFF; height: 10px; width: {percentage}%;'></div> |
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<div style='padding-left: 10px;'>{percentage:.2f}%</div> |
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</div> |
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</div> |
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""" |
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st.markdown(bar_html, unsafe_allow_html=True) |
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def main(): |
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st.set_page_config(layout="wide") |
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st.title("Shopping Intent Classification - SEO by DEJAN") |
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st.markdown(""" |
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Multi-label image classification model [extracted from Chrome](https://dejanmarketing.com/product-image-optimisation-with-chromes-convolutional-neural-network/). The model can be deployed in an automated pipeline capable of classifying product images in bulk. Javascript-based website scraping currently unsupported. |
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""") |
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st.write("Enter a URL to fetch and classify all images on the page:") |
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model_url = "https://huggingface.co/dejanseo/shopping-intent/resolve/main/model.tflite" |
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model_path = "model.tflite" |
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download_model(model_url, model_path) |
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url = st.text_input("Enter URL") |
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if url: |
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img_urls = fetch_images_from_url(url) |
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if img_urls: |
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st.write(f"Found {len(img_urls)} images") |
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interpreter = load_model(model_path) |
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input_details = interpreter.get_input_details() |
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input_shape = input_details[0]['shape'] |
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input_size = input_shape[1] |
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categories = [ |
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"No Shopping Intent", |
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"Apparel", |
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"Home Decor", |
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"Other" |
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] |
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for img_url in img_urls: |
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try: |
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response = requests.get(img_url) |
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image = Image.open(BytesIO(response.content)) |
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input_data = preprocess_image(image, input_size) |
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output_data_shopping_intent = run_inference(interpreter, input_data) |
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shopping_intent_percentages = (output_data_shopping_intent.flatten() * 100).tolist() |
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col1, col2 = st.columns([1, 3]) |
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with col1: |
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st.image(image.resize((224, 224)), width=224) |
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with col2: |
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st.write(f"[URL]({img_url})") |
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render_intent_bars(categories, shopping_intent_percentages) |
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st.write("---") |
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except Exception as e: |
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st.write(f"Could not process image {img_url}: {e}") |
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st.markdown(""" |
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Interested in using this in an automated pipeline for bulk image classification? |
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Please [book an appointment](https://dejanmarketing.com/conference/) to discuss your needs. |
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""") |
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if __name__ == "__main__": |
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main() |
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