app.py

import gradio as gr
from transformers import CLIPProcessor, CLIPModel
from PIL import Image
import torch

# Load a pre-trained CLIP model and processor from Hugging Face
model_name = "openai/clip-vit-base-patch32"
model = CLIPModel.from_pretrained(model_name)
processor = CLIPProcessor.from_pretrained(model_name)

# Load the image (Gradio handles this as part of the interface)
def process_image(image):
    """Prepares the image for the model and extracts features."""
    inputs = processor(images=image, return_tensors="pt")
    with torch.no_grad():
        features = model.get_image_features(**inputs)
    return features

# Generate a basic persona based on the features
def generate_persona(image):
    """Generates a persona based on the extracted features."""
    features = process_image(image)

    # Example: Generating hardcoded persona traits for now, could be improved with actual inference logic
    persona = {
        "Age": "25-35 years",
        "Gender": "Likely Female",
        "Interests": ["Fashion", "Modern Lifestyle", "Technology"],
        "Income Level": "Medium to High",
        "Psychographics": ["Trend-conscious", "Tech-savvy", "Health-aware"],
        "Behavioral Traits": ["Likes social media", "Follows influencers", "Shops online frequently"]
    }

    return persona

# Function to format the persona output as a string
def format_persona(persona):
    """Formats the persona for display in Gradio."""
    result = "\n".join([f"{key}: {value}" for key, value in persona.items()])
    return result

# Gradio interface for image input and persona output
def persona_analysis(image):
    """Takes an image and returns the inferred persona."""
    persona = generate_persona(image)
    return format_persona(persona)

# Build the Gradio interface
iface = gr.Interface(
    fn=persona_analysis, 
    inputs=gr.inputs.Image(type="pil"),  # Accept image input in PIL format
    outputs="text",  # Display persona as text output
    title="Marketing Persona Generator",
    description="Upload an image to generate a marketing persona."
)

# Launch the Gradio interface
iface.launch()