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| import gradio as gr | |
| from html import escape | |
| from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration | |
| import torch | |
| # Image captioning | |
| blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
| blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") | |
| # GoEmotions 28 categories | |
| classifier = pipeline("text-classification", model="SamLowe/roberta-base-go_emotions", top_k=None) | |
| def analyze(image): | |
| if image is None: | |
| return "<p class='empty'>Upload an image to detect its emotions across 28 categories.</p>" | |
| # Generate caption | |
| image = image.convert("RGB") | |
| inputs = blip_processor(image, return_tensors="pt") | |
| with torch.no_grad(): | |
| caption_ids = blip_model.generate(**inputs, max_new_tokens=50) | |
| caption = blip_processor.decode(caption_ids[0], skip_special_tokens=True) | |
| safe_caption = escape(caption) | |
| # Classify emotions | |
| results = classifier(caption)[0] | |
| results.sort(key=lambda x: x["score"], reverse=True) | |
| # Top 5 emotions shown prominently | |
| top5 = results[:5] | |
| rest = results[5:] | |
| top_cards = [] | |
| for i, r in enumerate(top5): | |
| pct = r["score"] * 100 | |
| opacity = 0.3 + r["score"] * 0.7 | |
| size = 1.1 if i == 0 else 0.95 | |
| safe_label = escape(r["label"]) | |
| top_cards.append(f""" | |
| <div class="top-card" style="opacity:{opacity};font-size:{size}em"> | |
| <span class="emotion-name">{safe_label}</span> | |
| <span class="emotion-score">{pct:.1f}%</span> | |
| </div>""") | |
| rest_items = [] | |
| for r in rest: | |
| pct = r["score"] * 100 | |
| safe_label = escape(r["label"]) | |
| rest_items.append(f""" | |
| <div class="rest-row"> | |
| <span class="rest-label">{safe_label}</span> | |
| <div class="rest-track"> | |
| <div class="rest-fill" style="width:{pct:.1f}%"></div> | |
| </div> | |
| <span class="rest-pct">{pct:.1f}%</span> | |
| </div>""") | |
| return f""" | |
| <div class="caption-box"> | |
| <div class="caption-label">BLIP sees:</div> | |
| <div class="caption-text">"{safe_caption}"</div> | |
| </div> | |
| <div class="section-label">Top 5 Emotions</div> | |
| <div class="top-grid">{"".join(top_cards)}</div> | |
| <details class="rest-section"> | |
| <summary>All 28 emotions</summary> | |
| <div class="rest-list">{"".join(rest_items)}</div> | |
| </details> | |
| """ | |
| with gr.Blocks(title="Image 28 Emotions (GoEmotions)") as demo: | |
| gr.Markdown("## Image 28 Emotions (GoEmotions)\nUpload an image. BLIP describes it, then a model scores 28 fine-grained emotion categories.") | |
| with gr.Row(): | |
| img_input = gr.Image(type="pil", label="Upload an image") | |
| result = gr.HTML( | |
| value="<p class='empty'>Your 28-emotion analysis will appear here.</p>", | |
| css_template=""" | |
| .caption-box { | |
| background: #f0f4ff; border-radius: 10px; padding: 14px 18px; | |
| margin-bottom: 16px; border: 1px solid #d0d8f0; | |
| } | |
| .caption-label { font-size: 0.75em; color: #888; text-transform: uppercase; letter-spacing: 0.05em; } | |
| .caption-text { font-size: 1.1em; margin-top: 4px; color: #333; } | |
| .section-label { font-weight: 700; font-size: 0.85em; color: #555; margin-bottom: 8px; text-transform: uppercase; letter-spacing: 0.05em; } | |
| .top-grid { display: flex; flex-direction: column; gap: 6px; margin-bottom: 16px; } | |
| .top-card { | |
| display: flex; justify-content: space-between; align-items: center; | |
| padding: 10px 16px; background: #fafafa; border: 1px solid #eee; | |
| border-radius: 8px; | |
| } | |
| .emotion-name { font-weight: 600; text-transform: capitalize; } | |
| .emotion-score { font-family: monospace; color: #666; } | |
| .rest-section { margin-top: 4px; } | |
| .rest-section summary { | |
| cursor: pointer; font-size: 0.85em; color: #888; | |
| padding: 6px 0; user-select: none; | |
| } | |
| .rest-list { display: flex; flex-direction: column; gap: 4px; margin-top: 8px; } | |
| .rest-row { display: flex; align-items: center; gap: 8px; font-size: 0.82em; } | |
| .rest-label { width: 100px; text-align: right; color: #555; text-transform: capitalize; } | |
| .rest-track { flex: 1; height: 14px; background: #f0f0f0; border-radius: 4px; overflow: hidden; } | |
| .rest-fill { height: 100%; background: #a78bfa; border-radius: 4px; } | |
| .rest-pct { width: 50px; font-family: monospace; color: #999; font-size: 0.9em; } | |
| .empty { color: #999; text-align: center; padding: 40px 20px; } | |
| """ | |
| ) | |
| img_input.change(fn=analyze, inputs=img_input, outputs=result) | |
| demo.launch() | |