| import gradio as gr |
| from html import escape |
| from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration |
| import torch |
|
|
| |
| blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") |
| blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") |
|
|
| |
| classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=None) |
|
|
| EMOTION_COLORS = { |
| "anger": "#ef4444", |
| "disgust": "#a3e635", |
| "fear": "#a855f7", |
| "joy": "#facc15", |
| "sadness": "#3b82f6", |
| "surprise": "#fb923c", |
| "neutral": "#94a3b8", |
| } |
|
|
| def analyze(image): |
| if image is None: |
| return "<p class='empty'>Upload an image to detect its basic emotions.</p>" |
|
|
| |
| 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) |
|
|
| |
| results = classifier(caption)[0] |
| results.sort(key=lambda x: x["score"], reverse=True) |
|
|
| top = results[0] |
| top_color = EMOTION_COLORS.get(top["label"], "#666") |
|
|
| bars = [] |
| for r in results: |
| color = EMOTION_COLORS.get(r["label"], "#666") |
| pct = r["score"] * 100 |
| safe_label = escape(r["label"].upper()) |
| bars.append(f""" |
| <div class="bar-row"> |
| <span class="bar-label">{safe_label}</span> |
| <div class="bar-track"> |
| <div class="bar-fill" style="width:{pct:.1f}%;background:{color}"></div> |
| </div> |
| <span class="bar-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="verdict" style="background:{top_color}22;color:{top_color};border:1px solid {top_color}44"> |
| {escape(top['label'].upper())} ({top['score']*100:.1f}%) |
| </div> |
| <div class="bars">{"".join(bars)}</div> |
| """ |
|
|
| with gr.Blocks(title="Image Basic Emotions (Ekman 6)") as demo: |
| gr.Markdown("## Image Basic Emotions (Ekman 6)\nUpload an image. BLIP describes it, then a model detects 6 basic emotions + neutral.") |
|
|
| with gr.Row(): |
| img_input = gr.Image(type="pil", label="Upload an image") |
| result = gr.HTML( |
| value="<p class='empty'>Your 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; } |
| .verdict { |
| text-align: center; font-weight: 700; font-size: 1.3em; |
| padding: 10px; border-radius: 8px; margin-bottom: 14px; |
| } |
| .bars { display: flex; flex-direction: column; gap: 8px; } |
| .bar-row { display: flex; align-items: center; gap: 10px; } |
| .bar-label { width: 80px; font-weight: 600; font-size: 0.8em; text-align: right; } |
| .bar-track { |
| flex: 1; height: 22px; background: #f0f0f0; border-radius: 6px; overflow: hidden; |
| } |
| .bar-fill { height: 100%; border-radius: 6px; } |
| .bar-pct { width: 55px; font-family: monospace; font-size: 0.85em; color: #666; } |
| .empty { color: #999; text-align: center; padding: 40px 20px; } |
| """ |
| ) |
|
|
| img_input.change(fn=analyze, inputs=img_input, outputs=result) |
|
|
| demo.launch() |
|
|
