Create app.py

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+import torch
+
+from transformers import BlipForConditionalGeneration, BlipProcessor
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+model_image_captioning = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to(device)
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+def inference(raw_image, question, decoding_strategy): 
+ inputs = processor(images=raw_image, text=question, return_tensors="pt")
+
+ if decoding_strategy == "Beam search":
+ inputs["max_length"] = 20
+ inputs["num_beams"] = 5
+ elif decoding_strategy == "Nucleus sampling":
+ inputs["max_length"] = 20
+ inputs["num_beams"] = 1
+ inputs["do_sample"] = True
+ inputs["top_k"] = 50
+ inputs["top_p"] = 0.95
+ elif decoding_strategy == "Contrastive search":
+ inputs["penalty_alpha"] = 0.6
+ inputs["top_k"] = 4
+ inputs["max_length"] = 512
+
+ out = model_image_captioning.generate(**inputs)
+ return processor.batch_decode(out, skip_special_tokens=True)[0]
+
+inputs = [
+ gr.inputs.Image(type='pil'),
+ gr.inputs.Textbox(lines=2, label="Context (optional)"),
+ gr.inputs.Radio(choices=["Beam search","Nucleus sampling", "Contrastive search"], type="value", default="Nucleus sampling", label="Caption Decoding Strategy")
+ ]
+outputs = gr.outputs.Textbox(label="Output")
+
+title = "BLIP"
+
+description = "Gradio demo for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (Salesforce Research). To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2201.12086' target='_blank'>BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation</a> | <a href='https://github.com/salesforce/BLIP' target='_blank'>Github Repo</a></p>"
+
+gr.Interface(inference, inputs, outputs, title=title, description=description, article=article).launch(enable_queue=True)