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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)
# Initialize the model
from modeling import clip_lora_model
model = clip_lora_model().to(device)
# Load pretrained weights
model_path = hf_hub_download(repo_id="PerceptCLIP/PerceptCLIP_Emotions", filename="perceptCLIP_Emotions.pth")
model.load_state_dict(torch.load(model_path, map_location=device))
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):
image = Image.open(image).convert("RGB")
image = emo_preprocess(image)
with torch.no_grad():
outputs = model(image)
predicted = outputs.argmax(1).item()
emotion = idx2label[predicted]
emoji = emotion_emoji.get(emotion, "❓") # Default to "?" if no emoji found
return f"{emotion} {emoji}"
# Create Gradio interface
iface = gr.Interface(
fn=predict_emotion,
inputs=gr.inputs.Image(type="pil", label="Upload an Image"),
outputs=gr.outputs.Textbox(label="Emotion + Emoji"),
title="PerceptCLIP-Emotions",
description="This model predicts the emotion evoked by an image and returns the corresponding emoji along with the emotion name."
)
if __name__ == "__main__":
iface.launch()
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