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2ce6e8c 0b8d5a4 2ce6e8c 0b8d5a4 2ce6e8c 0b8d5a4 2ce6e8c 0b8d5a4 2ce6e8c 0b8d5a4 2ce6e8c 0b8d5a4 2ce6e8c 0b8d5a4 | 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 | import gradio as gr
import torch
from transformers import BlipProcessor, BlipForConditionalGeneration
MODEL_NAME = "Salesforce/blip-image-captioning-base"
# --- مدل را بارگیری کن
try:
device = "cuda" if torch.cuda.is_available() else "cpu"
caption_processor = BlipProcessor.from_pretrained(MODEL_NAME)
caption_model = BlipForConditionalGeneration.from_pretrained(MODEL_NAME)
caption_model.to(device)
_load_error = None
except Exception as e:
caption_processor = None
caption_model = None
_load_error = str(e)
# --- تابع captioning
def caption_image(image):
if _load_error:
return f"❌ Model load error: {_load_error}"
if image is None:
return "⚠️ لطفاً یک تصویر آپلود کنید."
inputs = caption_processor(image, return_tensors="pt").to(device)
out = caption_model.generate(**inputs, max_new_tokens=30)
caption = caption_processor.decode(out[0], skip_special_tokens=True)
return caption
# --- رابط کاربری Gradio
status_text = (
"✅ Model loaded successfully"
if caption_model is not None and not _load_error
else f"❌ Error: {_load_error}"
if _load_error
else "⏳ Loading model..."
)
with gr.Blocks(title="Image Captioning App") as demo:
gr.Markdown("## 🖼️ Image Captioning with BLIP\nUpload an image and get an automatic caption.")
gr.Markdown(f"**Status:** {status_text}")
with gr.Row():
image_input = gr.Image(type="pil", label="Upload Image")
caption_output = gr.Textbox(label="Generated Caption", interactive=False)
generate_btn = gr.Button("Generate Caption")
generate_btn.click(fn=caption_image, inputs=image_input, outputs=caption_output)
demo.launch()
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