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Local AI Vision Models for Alt Text Generation (100% FREE)
Uses Hugging Face transformers to run models locally - no API costs!
Supported models:
- BLIP: Good balance of speed and quality
- GIT: More detailed descriptions
- LLAVA: Most advanced (requires more resources)
"""
import os
from typing import Optional
from pathlib import Path
import io
try:
from PIL import Image
PIL_AVAILABLE = True
except ImportError:
PIL_AVAILABLE = False
print("⚠️ Pillow not installed. Run: pip install pillow")
try:
from transformers import BlipProcessor, BlipForConditionalGeneration
from transformers import AutoProcessor, AutoModelForCausalLM
import torch
TRANSFORMERS_AVAILABLE = True
except ImportError:
TRANSFORMERS_AVAILABLE = False
print("⚠️ Transformers not installed. Run: pip install transformers torch")
class LocalVisionModel:
"""
Local AI model for generating image descriptions
Runs on your computer - 100% FREE with no API limits!
"""
def __init__(self, model_name: str = "blip-base"):
"""
Initialize local vision model
Args:
model_name: Model to use
- "blip-base" (default): Fast, good quality, ~1GB
- "blip-large": Better quality, slower, ~2GB
- "git-base": Alternative model, ~1.5GB
"""
self.model_name = model_name
self.enabled = False
self.model = None
self.processor = None
self.device = "cuda" if torch.cuda.is_available() else "cpu"
if not TRANSFORMERS_AVAILABLE:
print("❌ Transformers library not available")
print(" Install with: pip install transformers torch")
return
if not PIL_AVAILABLE:
print("❌ Pillow not available")
print(" Install with: pip install pillow")
return
# Load model
try:
print(f"📥 Loading {model_name} model... (this may take a minute on first run)")
if "blip" in model_name.lower():
self._load_blip_model(model_name)
elif "git" in model_name.lower():
self._load_git_model()
else:
print(f"⚠️ Unknown model: {model_name}, defaulting to BLIP")
self._load_blip_model("blip-base")
self.enabled = True
print(f"✅ {model_name} model loaded successfully on {self.device}")
except Exception as e:
print(f"❌ Failed to load model: {e}")
self.enabled = False
def _load_blip_model(self, model_name: str):
"""Load BLIP model (recommended for most use cases)"""
if "large" in model_name:
model_id = "Salesforce/blip-image-captioning-large"
else:
model_id = "Salesforce/blip-image-captioning-base"
self.processor = BlipProcessor.from_pretrained(model_id)
self.model = BlipForConditionalGeneration.from_pretrained(model_id)
self.model.to(self.device)
self.model_type = "blip"
def _load_git_model(self):
"""Load GIT model (alternative to BLIP)"""
model_id = "microsoft/git-base"
self.processor = AutoProcessor.from_pretrained(model_id)
self.model = AutoModelForCausalLM.from_pretrained(model_id)
self.model.to(self.device)
self.model_type = "git"
def is_enabled(self) -> bool:
"""Check if model is loaded and ready"""
return self.enabled and self.model is not None
def generate_alt_text(
self,
image_data: bytes,
shape_name: str = "",
slide_number: int = 0,
max_length: int = 250
) -> Optional[str]:
"""
Generate alt text for an image using local AI
Args:
image_data: Raw image bytes
shape_name: Shape name (for context)
slide_number: Slide number (for context)
max_length: Maximum alt text length
Returns:
Generated alt text or None if failed
"""
if not self.is_enabled():
return None
try:
# Convert bytes to PIL Image
image = Image.open(io.BytesIO(image_data)).convert("RGB")
# Check if image looks decorative (very small, likely a logo/icon)
if image.size[0] < 100 and image.size[1] < 100:
# Small image - likely decorative
if any(hint in shape_name.lower() for hint in ["logo", "icon", "background", "border"]):
return "decorative"
# Generate description
if self.model_type == "blip":
alt_text = self._generate_blip(image)
elif self.model_type == "git":
alt_text = self._generate_git(image)
else:
return None
# Clean up the text
alt_text = self._clean_alt_text(alt_text, max_length)
return alt_text
except Exception as e:
print(f"Error generating alt text: {e}")
return None
def _generate_blip(self, image: Image.Image) -> str:
"""Generate caption using BLIP model"""
# Process image
inputs = self.processor(image, return_tensors="pt").to(self.device)
# Generate caption
with torch.no_grad():
out = self.model.generate(
**inputs,
max_length=50,
num_beams=5, # Better quality with beam search
early_stopping=True
)
caption = self.processor.decode(out[0], skip_special_tokens=True)
return caption
def _generate_git(self, image: Image.Image) -> str:
"""Generate caption using GIT model"""
# Process image
inputs = self.processor(images=image, return_tensors="pt").to(self.device)
# Generate caption
with torch.no_grad():
generated_ids = self.model.generate(
pixel_values=inputs.pixel_values,
max_length=50
)
caption = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return caption
def _clean_alt_text(self, alt_text: str, max_length: int) -> str:
"""Clean and format generated alt text"""
# Remove common prefixes that BLIP adds
prefixes_to_remove = [
"a picture of ",
"an image of ",
"a photo of ",
"there is ",
"arafed ", # Common BLIP artifact
]
alt_text_lower = alt_text.lower()
for prefix in prefixes_to_remove:
if alt_text_lower.startswith(prefix):
alt_text = alt_text[len(prefix):]
break
# Capitalize first letter
if alt_text:
alt_text = alt_text[0].upper() + alt_text[1:]
# Truncate if needed
if len(alt_text) > max_length:
alt_text = alt_text[:max_length-3] + "..."
return alt_text.strip()
class HuggingFaceInferenceAPI:
"""
Hugging Face Inference API (FREE tier available)
Falls back to this if local models don't work
"""
def __init__(self, api_token: Optional[str] = None):
"""
Initialize Hugging Face Inference API
Args:
api_token: HF token (if None, reads from HF_TOKEN env var)
Get free token at: https://huggingface.co/settings/tokens
"""
self.api_token = api_token or os.getenv("HF_TOKEN")
self.enabled = False
if not self.api_token:
print("⚠️ No Hugging Face token found. Set HF_TOKEN environment variable.")
print(" Get free token at: https://huggingface.co/settings/tokens")
return
try:
import requests
self.requests = requests
self.enabled = True
self.api_url = "https://api-inference.huggingface.co/models/Salesforce/blip-image-captioning-base"
print("✅ Hugging Face Inference API initialized")
except ImportError:
print("❌ 'requests' library not available. Run: pip install requests")
def is_enabled(self) -> bool:
"""Check if API is ready"""
return self.enabled and self.api_token is not None
def generate_alt_text(
self,
image_data: bytes,
shape_name: str = "",
slide_number: int = 0,
max_length: int = 250
) -> Optional[str]:
"""
Generate alt text using Hugging Face Inference API
Args:
image_data: Raw image bytes
shape_name: Shape name
slide_number: Slide number
max_length: Maximum length
Returns:
Generated alt text or None
"""
if not self.is_enabled():
return None
try:
headers = {"Authorization": f"Bearer {self.api_token}"}
response = self.requests.post(
self.api_url,
headers=headers,
data=image_data,
timeout=30
)
if response.status_code == 200:
result = response.json()
if isinstance(result, list) and len(result) > 0:
caption = result[0].get("generated_text", "")
return self._clean_alt_text(caption, max_length)
else:
print(f"HF API error: {response.status_code}")
return None
except Exception as e:
print(f"HF API request failed: {e}")
return None
def _clean_alt_text(self, alt_text: str, max_length: int) -> str:
"""Clean generated text"""
# Remove common prefixes
prefixes = ["a picture of ", "an image of ", "a photo of "]
alt_text_lower = alt_text.lower()
for prefix in prefixes:
if alt_text_lower.startswith(prefix):
alt_text = alt_text[len(prefix):]
break
# Capitalize first letter
if alt_text:
alt_text = alt_text[0].upper() + alt_text[1:]
# Truncate if needed
if len(alt_text) > max_length:
alt_text = alt_text[:max_length-3] + "..."
return alt_text.strip()
# Singleton instances
_local_model: Optional[LocalVisionModel] = None
_hf_api: Optional[HuggingFaceInferenceAPI] = None
def get_vision_model() -> Optional[LocalVisionModel]:
"""Get or create local vision model singleton"""
global _local_model
if _local_model is None:
model_name = os.getenv("LOCAL_VISION_MODEL", "blip-base")
_local_model = LocalVisionModel(model_name)
return _local_model
def get_hf_api() -> Optional[HuggingFaceInferenceAPI]:
"""Get or create Hugging Face API singleton"""
global _hf_api
if _hf_api is None:
_hf_api = HuggingFaceInferenceAPI()
return _hf_api
def generate_alt_text_free(
image_data: bytes,
shape_name: str = "",
slide_number: int = 0,
max_length: int = 250
) -> Optional[str]:
"""
Generate alt text using FREE methods (tries local first, then HF API)
Priority:
1. Local AI model (completely free, unlimited)
2. Hugging Face Inference API (free tier)
3. None (fallback to placeholder in main code)
Args:
image_data: Raw image bytes
shape_name: Shape name
slide_number: Slide number
max_length: Maximum length
Returns:
Generated alt text or None
"""
# Try local model first (best option - free and unlimited)
local_model = get_vision_model()
if local_model and local_model.is_enabled():
result = local_model.generate_alt_text(image_data, shape_name, slide_number, max_length)
if result:
return result
# Fallback to Hugging Face API (free tier)
hf_api = get_hf_api()
if hf_api and hf_api.is_enabled():
result = hf_api.generate_alt_text(image_data, shape_name, slide_number, max_length)
if result:
return result
# If both fail, return None (main code will use placeholder)
return None
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