Spaces:
Sleeping
Sleeping
| import os | |
| import openai | |
| import gradio as gr | |
| from bs4 import BeautifulSoup | |
| import requests | |
| openai.api_key = os.getenv("OPENAI_API_KEY") | |
| def extract_text_from_url(url): | |
| try: | |
| resp = requests.get(url, timeout=30, headers={ | |
| "User-Agent": "Mozilla/5.0 (compatible; Bot/1.0)" | |
| }) | |
| soup = BeautifulSoup(resp.content, "html.parser") | |
| candidates = soup.find_all(['h1','h2','h3','h4','p','span','li']) | |
| text = ' '.join([c.get_text(strip=True) for c in candidates]) | |
| text = text[:4000] | |
| if len(text) < 100: | |
| raise ValueError("Could not extract enough content (site may require JavaScript). Please enter keywords manually.") | |
| return text | |
| except Exception as e: | |
| raise ValueError(f"URL extraction error: {e}") | |
| def extract_keywords(text): | |
| prompt = f""" | |
| Extract up to 10 concise, relevant SEO keywords suitable for an automotive advertisement from the following content: | |
| {text} | |
| Keywords: | |
| """ | |
| response = openai.ChatCompletion.create( | |
| model="gpt-4", | |
| messages=[{"role": "user", "content": prompt}], | |
| temperature=0.6, | |
| max_tokens=100 | |
| ) | |
| output = response.choices[0].message.content.strip() | |
| if ',' in output: | |
| keywords = output.split(',') | |
| else: | |
| keywords = output.split('\n') | |
| return [kw.strip() for kw in keywords if kw.strip()] | |
| def generate_ad_copy(platform, keywords): | |
| prompt = f""" | |
| Create a compelling, SEO-optimized {platform} ad using these keywords: {', '.join(keywords)}. | |
| Include a clear and enticing call-to-action. | |
| """ | |
| response = openai.ChatCompletion.create( | |
| model="gpt-4", | |
| messages=[{"role": "user", "content": prompt}], | |
| temperature=0.7, | |
| max_tokens=300 | |
| ) | |
| return response.choices[0].message.content.strip() | |
| def generate_ad_image(keywords): | |
| kw_str = ", ".join(keywords) | |
| # Enhanced prompt for better visuals | |
| image_prompt = ( | |
| f"High-quality, photorealistic automotive ad photo of a luxury car. " | |
| f"Clean background, professional lighting, stylish dealership setting. " | |
| f"Keywords: {kw_str}. Room for text overlay, wide format, visually appealing." | |
| ) | |
| response = openai.Image.create( | |
| prompt=image_prompt, | |
| n=1, | |
| size="512x512" | |
| ) | |
| image_url = response["data"][0]["url"] | |
| img_data = requests.get(image_url).content | |
| img_file = "generated_ad_image.png" | |
| with open(img_file, "wb") as f: | |
| f.write(img_data) | |
| return img_file | |
| def platform_html(platform, ad_text): | |
| # Platform-specific color and icons | |
| if platform == "Facebook": | |
| color = "#1877F2" | |
| icon = "🌐" | |
| elif platform == "Instagram": | |
| # Instagram gradient | |
| color = "linear-gradient(90deg, #f58529 0%, #dd2a7b 50%, #8134af 100%)" | |
| icon = "📸" | |
| elif platform == "X (Twitter)": | |
| color = "#14171A" | |
| icon = "🐦" | |
| else: # Google Search | |
| color = "#4285F4" | |
| icon = "🔍" | |
| if platform == "Instagram": | |
| # Gradient needs to be on a child div (not background-color) | |
| content = f""" | |
| <div style="background: {color}; padding: 2px; border-radius: 12px; margin-bottom:16px;"> | |
| <div style="background: white; color: #333; padding: 18px 20px; border-radius: 10px;"> | |
| <span style="font-size: 1.5em;">{icon} <b>{platform}</b></span> | |
| <div style="margin-top: 12px; font-size: 1.1em; line-height:1.6;">{ad_text}</div> | |
| </div> | |
| </div> | |
| """ | |
| else: | |
| content = f""" | |
| <div style="background: {color}; color: white; padding: 18px 20px; border-radius: 12px; margin-bottom:16px; min-height: 120px;"> | |
| <span style="font-size: 1.5em;">{icon} <b>{platform}</b></span> | |
| <div style="margin-top: 12px; font-size: 1.1em; line-height:1.6;">{ad_text}</div> | |
| </div> | |
| """ | |
| return content | |
| def main_workflow(input_mode, url_or_keywords): | |
| error = None | |
| keywords = [] | |
| ad_copies = {} | |
| image_path = None | |
| if input_mode == "URL": | |
| try: | |
| text = extract_text_from_url(url_or_keywords) | |
| keywords = extract_keywords(text) | |
| except Exception as e: | |
| return None, None, None, f"{e}" | |
| else: | |
| keywords = [kw.strip() for kw in url_or_keywords.split(",") if kw.strip()] | |
| if not keywords: | |
| return None, None, None, "Please provide at least one keyword." | |
| # Generate ad copies | |
| platforms = ["Facebook", "Instagram", "X (Twitter)", "Google Search"] | |
| for platform in platforms: | |
| ad_copies[platform] = generate_ad_copy(platform, keywords) | |
| # Generate image | |
| try: | |
| image_path = generate_ad_image(keywords) | |
| except Exception as e: | |
| error = f"Image generation error: {e}" | |
| # Save ads to txt | |
| output_txt = "generated_ads.txt" | |
| with open(output_txt, "w", encoding="utf-8") as f: | |
| for platform, content in ad_copies.items(): | |
| f.write(f"--- {platform} Ad Copy ---\n{content}\n\n") | |
| return keywords, ad_copies, image_path, error | |
| def run_space(input_mode, url, keywords): | |
| url_or_keywords = url if input_mode == "URL" else keywords | |
| keywords, ad_copies, image_path, error = main_workflow(input_mode, url_or_keywords) | |
| ad_previews = "" | |
| if ad_copies: | |
| for platform, ad in ad_copies.items(): | |
| ad_previews += platform_html(platform, ad) | |
| return ( | |
| keywords, | |
| ad_previews, | |
| image_path, | |
| "generated_ads.txt" if ad_copies else None, | |
| error | |
| ) | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# 🚗 Auto Ad Generator\nPaste a car listing URL **or** enter your own keywords, then preview AI-generated ads for each social media platform, plus an auto-generated image!") | |
| input_mode = gr.Radio(["URL", "Keywords"], value="URL", label="Input Type") | |
| url_input = gr.Textbox(label="Listing URL", placeholder="https://www.cars.com/listing/...", visible=True) | |
| kw_input = gr.Textbox(label="Manual Keywords (comma separated)", placeholder="e.g. BMW, used car, sunroof", visible=False) | |
| submit_btn = gr.Button("Generate Ads") | |
| gr.Markdown("## Keywords") | |
| kw_out = gr.JSON(label="Extracted/Provided Keywords") | |
| gr.Markdown("## Ad Copy Previews") | |
| ad_out = gr.HTML(label="Ad Copy Preview") # Now HTML, not Markdown | |
| gr.Markdown("## Generated Ad Image") | |
| img_out = gr.Image(label="Generated Ad Image", type="filepath") | |
| gr.Markdown("## Download Ad Copies") | |
| file_out = gr.File(label="Download TXT") | |
| err_out = gr.Textbox(label="Errors", interactive=False) | |
| def show_hide_fields(choice): | |
| return ( | |
| gr.update(visible=choice == "URL"), | |
| gr.update(visible=choice == "Keywords"), | |
| ) | |
| input_mode.change(show_hide_fields, input_mode, [url_input, kw_input]) | |
| submit_btn.click( | |
| run_space, | |
| inputs=[input_mode, url_input, kw_input], | |
| outputs=[kw_out, ad_out, img_out, file_out, err_out] | |
| ) | |
| demo.launch() | |