Spaces:
Running
Running
File size: 3,930 Bytes
951d546 8113166 951d546 8113166 951d546 11370de 951d546 11370de 951d546 0370355 951d546 2484b4f 951d546 c4a6bfb 951d546 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
import streamlit as st
from transformers import pipeline
from diffusers import StableDiffusionPipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if device == "cuda" else torch.float32
# Sentiment Analysis Model
@st.cache_resource
def load_sentiment_analyzer():
return pipeline("text-classification", model="SamLowe/roberta-base-go_emotions", top_k=5, device=device)
# Text-to-image model
@st.cache_resource
def load_text2img():
model = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch_dtype, safety_checker = None, requires_safety_checker = False)
model = model.to(device)
return model
sentiment_analyzer = load_sentiment_analyzer()
text2img = load_text2img()
def analyze_sentiment(text):
results = sentiment_analyzer(text)
emotions = [(result['label'], result['score']) for result in results[0]]
return emotions
def generate_prompt(emotions):
era_styles = {
"joy": ("Impressionist", "Claude Monet"),
"sadness": ("Romantic", "Caspar David Friedrich"),
"anger": ("Expressionist", "Edvard Munch"),
"fear": ("Surrealist", "Salvador Dali"),
"disgust": ("Abstract", "Jackson Pollock"),
"surprise": ("Pop Art", "Andy Warhol"),
"neutral": ("Minimalist", "Piet Mondrian"),
"love": ("Renaissance", "Sandro Botticelli"),
"admiration": ("Baroque", "Rembrandt"),
"approval": ("Neoclassical", "Jacques-Louis David"),
"caring": ("Pre-Raphaelite", "John Everett Millais"),
"excitement": ("Fauvism", "Henri Matisse"),
"gratitude": ("Rococo", "Jean-Honoré Fragonard"),
"pride": ("Art Nouveau", "Gustav Klimt"),
"relief": ("Pointillism", "Georges Seurat"),
"remorse": ("Symbolism", "Odilon Redon"),
"confusion": ("Cubism", "Pablo Picasso"),
"curiosity": ("Futurism", "Umberto Boccioni"),
"desire": ("Art Deco", "Tamara de Lempicka"),
"disapproval": ("Dada", "Marcel Duchamp"),
"embarrassment": ("Naive Art", "Henri Rousseau"),
"nervousness": ("Constructivism", "Vladimir Tatlin"),
"optimism": ("De Stijl", "Theo van Doesburg"),
"realization": ("Bauhaus", "Wassily Kandinsky"),
"amusement": ("Suprematism", "Kazimir Malevich"),
"annoyed": ("Social Realism", "Diego Rivera"),
"disappointment": ("Color Field", "Mark Rothko"),
"grief": ("Neo-Expressionism", "Jean-Michel Basquiat")
}
prompt_parts = []
for emotion, _ in emotions:
style, artist = era_styles.get(emotion, ("Contemporary", "Various"))
prompt_parts.append(f"a {style} painting in the style of {artist} depicting {emotion}")
return "Create a multi-style artwork combining " + ", ".join(prompt_parts)
def generate_image(prompt):
image = text2img(prompt, num_inference_steps=5).images[0]
return image
def main():
st.title("MoodAlbum - Paintings derived from emotions.")
user_text = st.text_area("How are you feeling? Describe your emotions:")
if st.button("Generate Painting"):
if user_text:
with st.spinner("Analyzing emotions..."):
emotions = analyze_sentiment(user_text)
st.write("Detected emotions:")
for emotion, score in emotions:
st.write(f"- {emotion}")
prompt = generate_prompt(emotions)
#st.write("Generated prompt:")
#st.write(prompt)
with st.spinner("Generating painting... This may take 3.5-4 minutes."):
image = generate_image(prompt)
st.image(image, caption="Your feelings coming to life!", use_column_width=True)
else:
st.warning("Please enter some text describing your emotions.")
if __name__ == "__main__":
main() |