Create app.py

app.py

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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
+
+# Interface using Gradio for Hugging Face deployment
+# Simple UI that takes an image upload
+iface = gr.Interface(
+    fn=upload_and_analyze,
+    inputs=gr.inputs.Image(type="file", label="Upload Advertisement Image"),
+    outputs=gr.outputs.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()