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9c79ff7 c787168 9c79ff7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | 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()
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