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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 | |
| openai.api_key = os.getenv("OPENAI_API_KEY") | |
| # Function to analyze the ad and generate marketing personas + scoring | |
| 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() | |
| # Simulate extracting creative copy from the image using OCR (optical character recognition) | |
| # In actual production, you'd integrate an OCR API here to extract text | |
| # For simplicity, we'll use placeholder text | |
| ad_copy = "Placeholder for ad copy extracted from the image." | |
| # Prompt for the marketing persona and scoring rubric | |
| prompt = f""" | |
| Analyze the following ad copy and generate a marketing persona. Then, provide a score (out of 10) for each of the following: | |
| 1. Relevance to Target Audience: Is the copy appealing to the intended demographic? | |
| 2. Emotional Engagement: Does the ad evoke the right emotional response? | |
| 3. Brand Consistency: Does the copy align with the brand’s voice and values? | |
| 4. Creativity: How unique or innovative is the language or approach? | |
| 5. Persuasiveness: Does the ad motivate action, such as clicking or purchasing? | |
| Ad Copy: {ad_copy} | |
| Provide the persona description and the scores in table form with a final score. | |
| """ | |
| # Use the OpenAI API to generate the persona and scores using gpt-4o-mini model | |
| response = openai.ChatCompletion.create( | |
| model="gpt-4o-mini", # Use the gpt-4o-mini model as requested | |
| messages=[ | |
| {"role": "system", "content": "You are a marketing expert."}, | |
| {"role": "user", "content": prompt} | |
| ], | |
| max_tokens=300, | |
| temperature=0.7, | |
| ) | |
| # Extract the response text | |
| result = response['choices'][0]['message']['content'] | |
| # Return the result for display | |
| return result | |
| # Function to process the image and run the analysis | |
| def upload_and_analyze(image): | |
| # No need to open the image, as it is already a PIL image | |
| result = analyze_ad(image) | |
| return result | |
| # Updated 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 copy based on Relevance, Emotional Engagement, Brand Consistency, Creativity, and Persuasiveness." | |
| ) | |
| # Launch the app | |
| if __name__ == "__main__": | |
| iface.launch() | |