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import gradio as gr |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline |
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from torch.nn.functional import sigmoid |
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import torch |
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model_name = "SamLowe/roberta-base-go_emotions" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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image_emotion_pipeline = pipeline("image-classification", model="nateraw/ferplus-emo-resnet34") |
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emotion_icons = { |
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"admiration": "😍", |
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"amusement": "😅", |
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"anger": "😡", |
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"annoyance": "😑", |
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"approval": "👍", |
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"caring": "💗", |
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"confusion": "🤔", |
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"curiosity": "😮", |
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"desire": "🤤", |
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"disappointment": "😞", |
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"disapproval": "👎", |
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"disgust": "🤮", |
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"embarrassment": "😳", |
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"excitement": "🎉", |
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"fear": "😱", |
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"gratitude": "🙏", |
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"grief": "😭", |
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"joy": "😃", |
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"love": "❤️", |
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"nervousness": "🤧", |
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"optimism": "😊", |
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"pride": "😎", |
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"realization": "🤯", |
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"relief": "😌", |
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"remorse": "😔", |
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"sadness": "😢", |
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"surprise": "😲", |
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"neutral": "😐" |
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} |
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def get_emotions(text, threshold): |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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probs = sigmoid(logits)[0] |
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labels = [model.config.id2label[i] for i, p in enumerate(probs) if p > threshold] |
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icons = [emotion_icons.get(label, '') + ' ' + label.capitalize() + f" ({probs[i]:.2f})" for i, label in enumerate(labels)] |
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return ", ".join(icons) if icons else "No strong emotion detected." |
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def analyze_image_emotion(image): |
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if image is None: |
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return "No image provided." |
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results = image_emotion_pipeline(image) |
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top = results[0] |
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return f"{top['label']} ({top['score']:.2f})" |
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def analyze_combined(text, threshold, image): |
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text_result = get_emotions(text, threshold) |
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image_result = analyze_image_emotion(image) |
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return text_result, image_result |
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custom_css = """ |
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body { |
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background: linear-gradient(to right, #f9f9f9, #d4ecff); |
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font-family: 'Segoe UI', sans-serif; |
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} |
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.gr-button { |
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background-color: #007BFF !important; |
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color: white !important; |
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border-radius: 8px !important; |
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font-weight: bold; |
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} |
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.gr-button:hover { |
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background-color: #0056b3 !important; |
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} |
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.gr-textbox { |
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border-radius: 8px !important; |
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border: 1px solid #ccc !important; |
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padding: 10px !important; |
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} |
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.output-textbox { |
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font-size: 1.5rem; |
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font-weight: bold; |
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color: #333; |
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background-color: #f1f9ff; |
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border-radius: 8px; |
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padding: 10px; |
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border: 1px solid #007BFF; |
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} |
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""" |
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demo = gr.Interface( |
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fn=analyze_combined, |
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inputs=[ |
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gr.Textbox(lines=5, placeholder="Write a sentence or a full paragraph...", label="Your Text"), |
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gr.Slider(minimum=0.1, maximum=0.9, value=0.3, step=0.05, label="Threshold"), |
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gr.Image(type="filepath", label="Upload Face Photo") |
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], |
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outputs=[ |
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gr.Textbox(label="Detected Text Emotions", elem_classes=["output-textbox"]), |
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gr.Textbox(label="Detected Photo Emotion", elem_classes=["output-textbox"]) |
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], |
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title="🥰 Multi-Modal Emotion Detector", |
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description="Analyze emotion from both text and a facial photo. Adjust the threshold for text emotion sensitivity.", |
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theme="default", |
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css=custom_css |
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) |
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demo.launch() |
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