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| import gradio as gr | |
| from hashlib import md5 | |
| 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") | |
| # Zero-shot classification | |
| classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli") | |
| DEFAULT_LABELS = "anger, calm, disgust, excited, confused, proud, regretful, hopeful" | |
| def analyze(image, labels_text): | |
| if image is None: | |
| return "<p class='empty'>Upload an image to classify its emotions with your own labels.</p>" | |
| labels = [] | |
| seen = set() | |
| for raw_label in labels_text.split(","): | |
| label = raw_label.strip() | |
| if label and label.lower() not in seen: | |
| seen.add(label.lower()) | |
| labels.append(label) | |
| if not labels: | |
| return "<p class='empty'>Enter at least one emotion label.</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) | |
| # Zero-shot classification | |
| result = classifier( | |
| caption, | |
| candidate_labels=labels, | |
| multi_label=True, | |
| hypothesis_template="This image conveys {}." | |
| ) | |
| bars = [] | |
| for label, score in zip(result["labels"], result["scores"]): | |
| pct = score * 100 | |
| hue = int(md5(label.encode("utf-8")).hexdigest()[:8], 16) % 360 | |
| color = f"hsl({hue}, 65%, 55%)" | |
| safe_label = escape(label) | |
| 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>""") | |
| top = result["labels"][0] | |
| top_score = result["scores"][0] | |
| return f""" | |
| <div class="caption-box"> | |
| <div class="caption-label">BLIP sees:</div> | |
| <div class="caption-text">"{safe_caption}"</div> | |
| </div> | |
| <div class="verdict"> | |
| Best match: <strong>{escape(top)}</strong> ({top_score*100:.1f}%) | |
| </div> | |
| <div class="bars">{"".join(bars)}</div> | |
| """ | |
| with gr.Blocks(title="Image Zero-Shot Emotions") as demo: | |
| gr.Markdown("## Image Zero-Shot Emotions\nUpload an image and define your own emotion labels. BLIP describes the image, then a zero-shot model scores each label.") | |
| labels_input = gr.Textbox( | |
| label="Emotion labels (comma-separated)", | |
| value=DEFAULT_LABELS, | |
| placeholder="e.g. joy, sadness, anger, fear, love" | |
| ) | |
| with gr.Row(): | |
| img_input = gr.Image(type="pil", label="Upload an image") | |
| result = gr.HTML( | |
| value="<p class='empty'>Your zero-shot 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-size: 1.15em; padding: 10px; | |
| background: #fafafa; border-radius: 8px; border: 1px solid #eee; | |
| margin-bottom: 14px; | |
| } | |
| .bars { display: flex; flex-direction: column; gap: 8px; } | |
| .bar-row { display: flex; align-items: center; gap: 10px; } | |
| .bar-label { width: 90px; font-weight: 600; font-size: 0.85em; text-align: right; text-transform: capitalize; } | |
| .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, labels_input], outputs=result) | |
| labels_input.change(fn=analyze, inputs=[img_input, labels_input], outputs=result) | |
| demo.launch() | |