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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") | |
| # 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() | |