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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()
# Prompt for the marketing persona and scoring rubric
prompt = """
Analyze this advertisement image. Extract any text present in the image and generate a marketing persona.
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 image and prompt to GPT-4-turbo for analysis
response = openai.ChatCompletion.create(
model="gpt-4-turbo-vision", # Use the multimodal GPT-4-turbo model
messages=[
{"role": "system", "content": "You are a marketing expert analyzing an advertisement."},
{"role": "user", "content": prompt}
],
functions=[
{
"name": "analyze_image",
"description": "Analyze an image and generate marketing insights",
"parameters": {
"type": "image",
"properties": {
"image": {
"type": "string",
"description": "The input advertisement image for analysis"
}
},
"required": ["image"]
}
}
],
function_call={"name": "analyze_image", "arguments": {"image": image_bytes}}, # Sending the image as input
temperature=0.7,
max_tokens=500
)
# Extract the response text from the API output
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):
# 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()
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