import openai
import os
import gradio as gr
from dotenv import load_dotenv
import io
from PIL import Image
import pytesseract  # Import Tesseract OCR

# 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 and generate marketing personas + scoring
def analyze_ad(image):
    # Extract text from the image using Tesseract OCR
    ad_copy = pytesseract.image_to_string(image)

    if not ad_copy.strip():  # Check if OCR extracted any text
        return "No text was detected in the image. Please upload a clearer ad 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 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?
    
    Ad Copy: {ad_copy}
    
    Provide the persona description and the scores in table form with a final score.
    """

    # Send the prompt to GPT-4o-mini for analysis
    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 analyzing an advertisement."},
            {"role": "user", "content": prompt}
        ],
        temperature=0.7,
        max_tokens=400
    )

    # 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()