Create image_to_text_blip.py

image_to_text_blip.py

@@ -0,0 +1,32 @@
+from transformers import BlipProcessor, BlipForConditionalGeneration
+from PIL import Image
+import gradio as gr
+import torch
+
+# Load BLIP model and processor
+processor = BlipProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16)
+model.eval()
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+# Inference function
+def generate_caption(image):
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+    
+    inputs = processor(image, return_tensors="pt").to(device, torch.float16)
+    output = model.generate(**inputs, max_new_tokens=50)
+    caption = processor.decode(output[0], skip_special_tokens=True)
+    return caption
+
+# Gradio interface
+iface = gr.Interface(
+    fn=generate_caption,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Construction Site Image-to-Text Generator",
+    description="Upload a site photo. The model will detect and describe construction activities."
+)
+
+iface.launch()