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import base64
import requests
import gradio as gr
from PIL import Image
import numpy as np
from datetime import datetime
import os
# OpenAI API Key
api_key = os.getenv("OPENAI_API_KEY")
# Function to encode the image
def encode_image(image_array):
# Convert numpy array to an image file and encode it in base64
img = Image.fromarray(np.uint8(image_array))
img_buffer = os.path.join(
"/tmp", f"temp_image_{datetime.now().strftime('%Y%m%d_%H%M%S')}.jpg"
)
img.save(img_buffer, format="JPEG")
with open(img_buffer, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
# Function to generate product description using OpenAI API
def generate_product_description(image, description_type, custom_instruction=None):
# Encode the uploaded image
base64_image = encode_image(image)
headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}
# Set the description type or custom instruction
description_prompts = {
"Short Formal π": "Based on the image, craft a concise and compelling product description that highlights key features and benefits in a formal tone.",
"Bullet Points π": "From the image, provide a detailed list of bullet points describing the product's features, benefits, and unique selling points.",
"Amazon Optimized π": "Create an Amazon-style product description based on the image, including key features, benefits, relevant keywords for SEO, and a persuasive call to action.",
"Fashion π": "Generate a stylish and trendy product description for a fashion item shown in the image, emphasizing its design, materials, and how it fits into current fashion trends.",
"Sport π": "Using the image, develop an energetic and engaging product description for a sports-related item, highlighting its performance features and benefits for athletic activities.",
"Technical Specifications βοΈ": "Extract and present the product's technical specifications from the image in a clear and concise manner, suitable for tech-savvy customers.",
"SEO Optimized π": "Write an SEO-friendly product description based on the image, incorporating relevant keywords and phrases to enhance search engine visibility.",
"Social Media Style π±": "Create a catchy and engaging product description suitable for social media platforms, using the image as inspiration.",
"Luxury π": "Craft an elegant and sophisticated product description for the luxury item shown in the image, emphasizing exclusivity, premium quality, and craftsmanship.",
"Kid-Friendly π§Έ": "Generate a fun and appealing product description for a children's product based on the image, using language that resonates with both kids and parents.",
"Health and Beauty π": "Develop a compelling product description for a health or beauty item shown in the image, highlighting its benefits, ingredients, and usage tips.",
"Electronic Gadgets π±": "Write a tech-focused product description for the electronic gadget in the image, focusing on its innovative features, specifications, and user advantages.",
"Eco-Friendly π±": "Create an eco-conscious product description for the environmentally friendly product shown in the image, emphasizing sustainability and green benefits.",
"Personalized Gifts π": "Generate a heartfelt product description for the personalized gift in the image, highlighting customization options and sentimental value.",
"Seasonal Promotion π": "Craft a seasonal promotional product description based on the image, incorporating festive themes and limited-time offers to encourage immediate purchase.",
"Clearance Sale π·οΈ": "Write an urgent and enticing product description for the item in the image, emphasizing discounted prices and limited stock availability.",
"Cross-Selling π": "Develop a product description that not only highlights the item in the image but also suggests complementary products, encouraging additional purchases.",
"Up-Selling β¬οΈ": "Create a persuasive product description that highlights premium features of the item in the image, encouraging customers to consider higher-end versions.",
"Multi-Language Support π": "Provide a product description based on the image in multiple languages to cater to a diverse customer base.",
"User Testimonials β": "Incorporate fictional user testimonials or reviews into the product description based on the image, adding credibility and social proof.",
"Instructional π": "Write a product description that includes usage instructions or assembly steps for the item shown in the image.",
"Bundle Offer π¦": "Craft a product description that promotes the item in the image as part of a bundle deal, highlighting the added value.",
"Gift Guide Entry π": "Generate a product description suitable for inclusion in a gift guide, emphasizing why the item in the image makes a great gift.",
"Limited Edition π": "Create an exclusive product description for the limited-edition item shown in the image, highlighting its uniqueness and scarcity.",
"Subscription Model π": "Write a product description that promotes the item in the image as part of a subscription service, detailing recurring benefits.",
"B2B Focused π’": "Develop a professional product description suitable for business-to-business contexts, emphasizing features relevant to corporate clients.",
}
if description_type == "Other" and custom_instruction:
instruction = custom_instruction
else:
instruction = description_prompts.get(
description_type, "Create a product description based on the image."
)
# Payload with base64 encoded image as a Data URL
payload = {
"model": "gpt-4o-mini",
"messages": [
{
"role": "user",
"content": [
{"type": "text", "text": instruction},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{base64_image}"},
},
],
}
],
"max_tokens": 300,
}
response = requests.post(
"https://api.openai.com/v1/chat/completions", headers=headers, json=payload
)
response_data = response.json()
# Handle errors
if response.status_code != 200:
raise ValueError(
f"OpenAI API Error: {response_data.get('error', {}).get('message', 'Unknown Error')}"
)
# Extract and return only the generated message content
return response_data["choices"][0]["message"]["content"]
css = """
#output {
height: 500px;
overflow: auto;
border: 1px solid #ccc;
}
"""
with gr.Blocks(css=css) as demo:
gr.Markdown("WordLift Product Description Generation - [FREE]")
with gr.Tab(label="WordLift Product Description Generation"):
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input Picture")
description_type = gr.Dropdown(
label="Select Description Type",
choices=[
"Short Formal π",
"Bullet Points π",
"Amazon Optimized π",
"Fashion π",
"Sport π",
"Technical Specifications βοΈ",
"SEO Optimized π",
"Social Media Style π±",
"Luxury π",
"Kid-Friendly π§Έ",
"Health and Beauty π",
"Electronic Gadgets π±",
"Eco-Friendly π±",
"Personalized Gifts π",
"Seasonal Promotion π",
"Clearance Sale π·οΈ",
"Cross-Selling π",
"Up-Selling β¬οΈ",
"Multi-Language Support π",
"User Testimonials β",
"Instructional π",
"Bundle Offer π¦",
"Gift Guide Entry π",
"Limited Edition π",
"Subscription Model π",
"B2B Focused π’",
"Other",
],
value="Short Formal π",
)
custom_instruction = gr.Textbox(
label="Custom Instruction (Only for 'Other')", visible=False
)
submit_btn = gr.Button(value="Submit")
with gr.Column():
output_text = gr.Textbox(label="Output Text")
# Toggle visibility of custom instruction based on selected type
def toggle_custom_instruction(type_selection):
return gr.update(visible=(type_selection == "Other"))
description_type.change(
toggle_custom_instruction,
inputs=[description_type],
outputs=[custom_instruction],
)
submit_btn.click(
generate_product_description,
[input_img, description_type, custom_instruction],
[output_text],
)
# Launch Gradio app
demo.queue(api_open=False)
demo.launch(debug=True)
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