app.py

import streamlit as st
from transformers import CLIPProcessor, CLIPModel
import torch
from PIL import Image
import requests
from io import BytesIO

st.title("Text to Image Generation with CLIP")

# Load pretrained models
clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16")
clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch16")

text = st.text_area("Enter a description:")
if st.button("Generate Image") and text:
    # Process text and get CLIP features for text
    text_features = clip_processor(text, return_tensors="pt", padding=True)

    # Load an example image from the web (replace this with your image loading logic)
    example_image_url = "https://example.com/your-image.jpg"
    example_image_response = requests.get(example_image_url)
    example_image = Image.open(BytesIO(example_image_response.content))

    # Process image and get CLIP features for image
    image_features = clip_processor(images=example_image, return_tensors="pt", padding=True)

    # Concatenate text and image features
    combined_features = {
        "pixel_values": torch.cat([text_features.pixel_values, image_features.pixel_values], dim=-1)
    }

    # Forward pass through CLIP
    image_representation = clip_model(**combined_features).last_hidden_state.mean(dim=1)

    # For visualization, you can convert the image representation back to an image
    image_array = image_representation.squeeze().cpu().numpy()
    image = Image.fromarray((image_array * 255).astype('uint8'))

    # Display the generated image
    st.image(image, caption="Generated Image", use_column_width=True)