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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 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 load and display the image
def upload_and_analyze(image):
# Open the image and display
ad_image = Image.open(image)
# Analyze the ad
result = analyze_ad(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()
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