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| import openai | |
| import os | |
| import gradio as gr | |
| from dotenv import load_dotenv | |
| import io | |
| from PIL import Image | |
| # Load environment variables (where your OpenAI key will be stored) | |
| load_dotenv() | |
| # Load the OpenAI API key from environment variables and strip any trailing newlines or spaces | |
| openai.api_key = os.getenv("OPENAI_API_KEY").strip() | |
| # Function to analyze the ad image using GPT-4-turbo's multimodal capabilities | |
| def analyze_ad(image): | |
| # Convert the PIL image to bytes | |
| image_bytes = io.BytesIO() | |
| image.save(image_bytes, format='PNG') | |
| image_bytes = image_bytes.getvalue() | |
| # Placeholder for ad text extracted from the image (since GPT-4-turbo doesn't support image-to-text natively) | |
| # In a real scenario, you could integrate OCR to extract text or pass the image to a vision model. | |
| ad_text = "This is a placeholder for text extracted from the image." | |
| # Prompt for the marketing persona and scoring rubric | |
| prompt = f""" | |
| Analyze this advertisement and generate a marketing persona. The text in the ad says: | |
| '{ad_text}' | |
| Then, provide a score (out of 10) for each of the following: | |
| 1. Relevance to Target Audience: Is the ad appealing to the intended demographic? | |
| 2. Emotional Engagement: Does the ad evoke the right emotional response? | |
| 3. Brand Consistency: Does the ad align with the brand’s voice and values? | |
| 4. Creativity: How unique or innovative is the ad's design and text approach? | |
| 5. Persuasiveness: Does the ad motivate action, such as clicking or purchasing? | |
| Provide the persona description and the scores in table form with a final score. | |
| """ | |
| # Send the prompt to GPT-4-turbo for analysis | |
| response = openai.ChatCompletion.create( | |
| model="gpt-4-turbo", # Use GPT-4 turbo model | |
| messages=[ | |
| {"role": "system", "content": "You are a marketing expert analyzing an advertisement."}, | |
| {"role": "user", "content": prompt} | |
| ], | |
| temperature=0.7, | |
| max_tokens=500 | |
| ) | |
| # Extract the response text from the API output | |
| result = response['choices'][0]['message']['content'].strip() | |
| # Return the result for display | |
| return result | |
| # Function to process the image and run the analysis | |
| def upload_and_analyze(image): | |
| # Pass the uploaded image to the analyze_ad function | |
| result = analyze_ad(image) | |
| return result | |
| # Gradio interface for Hugging Face deployment | |
| iface = gr.Interface( | |
| fn=upload_and_analyze, | |
| inputs=gr.Image(type="pil", label="Upload Advertisement Image"), # Use type="pil" for the image input | |
| outputs=gr.Textbox(label="Marketing Persona and Ad Analysis"), | |
| title="Advertisement Persona and Scoring Analyzer", | |
| description="Upload an advertisement image, and the app will generate marketing personas and evaluate the ad based on Relevance, Emotional Engagement, Brand Consistency, Creativity, and Persuasiveness." | |
| ) | |
| # Launch the app | |
| if __name__ == "__main__": | |
| iface.launch() | |