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fe7930f | 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 | 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")
# Binary sentiment
sentiment = pipeline("sentiment-analysis", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
def analyze(image):
if image is None:
return "<p class='empty'>Upload an image to analyze its emotional sentiment.</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 sentiment
result = sentiment(caption)[0]
label = result["label"]
score = result["score"]
other_label = "NEGATIVE" if label == "POSITIVE" else "POSITIVE"
other_score = 1 - score
pos = score if label == "POSITIVE" else other_score
neg = score if label == "NEGATIVE" else other_score
pos_color = f"rgba(34,197,94,{0.2 + pos * 0.8})"
neg_color = f"rgba(239,68,68,{0.2 + neg * 0.8})"
return f"""
<div class="caption-box">
<div class="caption-label">BLIP sees:</div>
<div class="caption-text">"{safe_caption}"</div>
</div>
<div class="result-box">
<div class="bar-row">
<span class="bar-label">POSITIVE</span>
<div class="bar-track">
<div class="bar-fill" style="width:{pos*100:.1f}%;background:{pos_color}"></div>
</div>
<span class="bar-pct">{pos*100:.1f}%</span>
</div>
<div class="bar-row">
<span class="bar-label">NEGATIVE</span>
<div class="bar-track">
<div class="bar-fill" style="width:{neg*100:.1f}%;background:{neg_color}"></div>
</div>
<span class="bar-pct">{neg*100:.1f}%</span>
</div>
<div class="verdict {'pos' if label == 'POSITIVE' else 'neg'}">
{label} ({score*100:.1f}%)
</div>
</div>
"""
with gr.Blocks(title="Image Binary Sentiment") as demo:
gr.Markdown("## Image Binary Sentiment\nUpload an image. BLIP describes it, then a sentiment model classifies the description as positive or negative.")
with gr.Row():
img_input = gr.Image(type="pil", label="Upload an image")
result = gr.HTML(
value="<p class='empty'>Your sentiment 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; }
.result-box { display: flex; flex-direction: column; gap: 10px; }
.bar-row { display: flex; align-items: center; gap: 10px; }
.bar-label { width: 80px; font-weight: 600; font-size: 0.85em; text-align: right; }
.bar-track {
flex: 1; height: 24px; background: #f0f0f0; border-radius: 6px; overflow: hidden;
}
.bar-fill { height: 100%; border-radius: 6px; transition: width 0.3s; }
.bar-pct { width: 55px; font-family: monospace; font-size: 0.85em; color: #666; }
.verdict {
text-align: center; font-weight: 700; font-size: 1.3em;
margin-top: 10px; padding: 10px; border-radius: 8px;
}
.verdict.pos { background: rgba(34,197,94,0.12); color: #16a34a; }
.verdict.neg { background: rgba(239,68,68,0.12); color: #dc2626; }
.empty { color: #999; text-align: center; padding: 40px 20px; }
"""
)
img_input.change(fn=analyze, inputs=img_input, outputs=result)
demo.launch()
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