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import torch
import gradio as gr
from PIL import Image
from huggingface_hub import hf_hub_download
import importlib.util
from torchvision import transforms
# Load model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Download model code
class_path = hf_hub_download(repo_id="PerceptCLIP/PerceptCLIP_Emotions", filename="modeling.py")
spec = importlib.util.spec_from_file_location("modeling", class_path)
modeling = importlib.util.module_from_spec(spec)
spec.loader.exec_module(modeling)
from modeling import clip_lora_model
# Emotions model
emotion_model = clip_lora_model().to(device)
emotion_model_path = hf_hub_download(repo_id="PerceptCLIP/PerceptCLIP_Emotions", filename="perceptCLIP_Emotions.pth")
emotion_model.load_state_dict(torch.load(emotion_model_path, map_location=device))
emotion_model.eval()
# Memorability model
mem_model = clip_lora_model(output_dim=1).to(device)
mem_model_path = hf_hub_download(repo_id="PerceptCLIP/PerceptCLIP_Memorability", filename="perceptCLIP_Memorability.pth")
mem_model.load_state_dict(torch.load(mem_model_path, map_location=device))
mem_model.eval()
# Emotion label mapping
idx2label = {
0: "amusement",
1: "awe",
2: "contentment",
3: "excitement",
4: "anger",
5: "disgust",
6: "fear",
7: "sadness"
}
# Emoji mapping
emotion_emoji = {
"amusement": "😂",
"awe": "😲",
"contentment": "😊",
"excitement": "😃",
"anger": "😠",
"disgust": "🤢",
"fear": "😱",
"sadness": "😞"
}
# Image preprocessing
def emo_preprocess(image):
transform = transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=(0.4814, 0.4578, 0.4082), std=(0.2686, 0.2613, 0.2758)),
])
return transform(image).unsqueeze(0).to(device)
# Inference function
def predict_emotion(image):
# If the image is passed as a PIL Image
if isinstance(image, Image.Image):
img = image.convert("RGB")
else:
img = Image.open(image).convert("RGB")
img = emo_preprocess(img)
with torch.no_grad():
mem_score = mem_model(img).item()
outputs = emotion_model(img)
predicted = outputs.argmax(1).item()
emotion = idx2label[predicted]
emoji = emotion_emoji.get(emotion, "❓")
return f"{emotion} {emoji}", f"{mem_score:.4f}"
# Example images
example_images = [
"https://img.freepik.com/free-photo/emotive-excited-female-with-dark-skin-crisp-hair-keeps-hands-clenched-fists-exclaims-with-positiveness-as-achieved-success-her-career-opens-mouth-widely-isolated-white-wall_273609-16443.jpg",
"https://t4.ftcdn.net/jpg/01/18/44/59/360_F_118445958_NtP7tIsD0CBPyG7Uad7Z2KxVWrsfCPjP.jpg",
"https://apnapestcontrol.ca/wp-content/uploads/2019/02/9.jpg",
"https://images.pexels.com/photos/1107717/pexels-photo-1107717.jpeg?cs=srgb&dl=pexels-fotios-photos-1107717.jpg&fm=jpg"
]
# Create Gradio interface
iface = gr.Interface(
fn=predict_emotion,
inputs=gr.Image(type="pil", label="Upload an Image"),
outputs=[gr.Textbox(label="Emotion"), gr.Textbox(label="Memorability Score")],
title="PerceptCLIP-Emotions",
description="This model predicts the emotion evoked by an image.",
examples=example_images
)
if __name__ == "__main__":
iface.launch()
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