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()