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a745a5e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import streamlit as st
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
BlipForConditionalGeneration,
BlipProcessor,
)
@st.cache_resource
def load_caption_model():
device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
model = BlipForConditionalGeneration.from_pretrained("saved_model_phase2")
processor = BlipProcessor.from_pretrained("saved_model_phase2")
model.to(device)
model.eval()
return model, processor, device
@st.cache_resource
def load_toxicity_model():
device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained("unitary/toxic-bert")
model = AutoModelForSequenceClassification.from_pretrained("unitary/toxic-bert")
model.to(device)
model.eval()
return model, tokenizer, device
caption_model, caption_processor, device = load_caption_model()
tox_model, tox_tokenizer, tox_device = load_toxicity_model()
st.title("🖼️ Advanced Image Captioning Demo")
st.write("Fine-tuned BLIP with Beam Search + Toxicity Filtering")
st.sidebar.header("⚙️ Generation Settings")
num_beams = st.sidebar.slider("Beam Size", 1, 10, 5)
max_length = st.sidebar.slider("Max Length", 10, 50, 20)
length_penalty = st.sidebar.slider("Length Penalty", 0.5, 2.0, 1.0, step=0.1)
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
if uploaded_file:
image = Image.open(uploaded_file).convert("RGB")
st.image(image, caption="Uploaded Image", width="stretch")
if st.button("Generate Caption"):
# Generate caption
with st.spinner("Generating caption..."):
inputs = caption_processor(
images=image,
return_tensors="pt",
).to(device)
with torch.no_grad():
output_ids = caption_model.generate(
**inputs,
num_beams=num_beams,
max_length=max_length,
length_penalty=length_penalty,
)
caption = caption_processor.decode(
output_ids[0],
skip_special_tokens=True,
)
# Confidence score (stable)
with torch.no_grad():
loss_inputs = caption_processor(
images=image,
text=caption,
return_tensors="pt",
).to(device)
outputs = caption_model(
pixel_values=loss_inputs["pixel_values"],
input_ids=loss_inputs["input_ids"],
attention_mask=loss_inputs["attention_mask"],
labels=loss_inputs["input_ids"],
)
loss = outputs.loss
confidence = torch.exp(-loss).item() if loss is not None else 0.0
# Toxicity check
tox_inputs = tox_tokenizer(
caption,
return_tensors="pt",
truncation=True,
).to(tox_device)
with torch.no_grad():
tox_outputs = tox_model(**tox_inputs)
probs = F.softmax(tox_outputs.logits, dim=-1)
toxic_score = probs[0][1].item()
# Display caption
if toxic_score > 0.6:
st.error("⚠️ Generated caption flagged as potentially toxic.")
st.markdown("### 🚫 Caption Blocked")
else:
st.success("Caption Generated")
st.markdown(f"### 📝 {caption}")
st.caption(f"Toxicity Score: {toxic_score:.2f}")
st.caption(f"Confidence Score: {confidence:.2f}")
# Vision attention heatmap
with torch.no_grad():
vision_outputs = caption_model.vision_model(
inputs["pixel_values"],
output_attentions=True,
return_dict=True,
)
attentions = vision_outputs.attentions[-1]
attn = attentions[0].mean(0)
cls_attn = attn[0, 1:]
attn_map = cls_attn.cpu().numpy()
attn_map = attn_map / attn_map.max()
size = int(np.sqrt(len(attn_map)))
fig, ax = plt.subplots()
ax.imshow(attn_map.reshape(size, size), cmap="viridis")
ax.set_title("Vision Attention Heatmap")
ax.axis("off")
st.pyplot(fig)
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