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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): | |
| # If the image is passed as a PIL Image, you can directly use it | |
| if isinstance(image, Image.Image): | |
| img = image.convert("RGB") | |
| else: | |
| img = Image.open(image).convert("RGB") # In case the input is a path or something else | |
| img = emo_preprocess(img) | |
| with torch.no_grad(): | |
| outputs = model(img) | |
| predicted = outputs.argmax(1).item() | |
| emotion = idx2label[predicted] | |
| emoji = emotion_emoji.get(emotion, "❓") # Default to "?" if no emoji found | |
| return f"{emotion} {emoji}" | |
| # Example images (provide local file paths or URLs) | |
| 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"), | |
| title="PerceptCLIP-Emotions", | |
| description="This model predicts the emotion evoked by an image and returns the corresponding emoji along with the emotion name.", | |
| examples=example_images # Add example images here | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |