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import gradio as gr
import torch
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
def load_model():
# Load model and processor
model_id = "fathindifa/food-caption-blip2"
processor = Blip2Processor.from_pretrained(model_id)
model = Blip2ForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float32,
device_map="auto" # Will use GPU if available, otherwise CPU
)
model.eval()
return processor, model
processor, model = load_model()
def generate_caption(image, prompt="", max_length=32, temperature=0.7):
"""Generate caption for the input image."""
if image is None:
return "Please upload an image."
# Ensure image is in RGB
image = Image.fromarray(image).convert("RGB")
# Process image
inputs = processor(images=image, text=prompt, return_tensors="pt")
# Move inputs to same device as model
inputs = {k: v.to(model.device) for k, v in inputs.items()}
# Generate caption
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=max_length,
num_beams=5,
temperature=temperature
)
# Decode and return caption
caption = processor.batch_decode(outputs, skip_special_tokens=True)[0]
return caption
# Create Gradio interface
iface = gr.Interface(
fn=generate_caption,
inputs=[
gr.Image(label="Upload Food Image"),
gr.Textbox(label="Optional Prompt", placeholder="Enter prompt to guide the caption (optional)", value=""),
gr.Slider(minimum=10, maximum=50, value=32, step=1, label="Maximum Caption Length"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature")
],
outputs=gr.Textbox(label="Generated Caption"),
title="🍜 Food Image Captioning",
description="Upload a food image and get an automatically generated caption! This demo uses a fine-tuned BLIP2 model specifically trained for food image captioning.",
article="""
### Tips:
- Try different prompts to guide the caption generation
- Adjust temperature for more/less creative captions
- Increase max length for longer descriptions
""",
flagging_mode="never",
cache_examples=False
)
# Launch the interface
if __name__ == "__main__":
iface.launch() |