Update app.py

app.py

@@ -1,19 +1,46 @@
-from PIL import Image
-import requests
 import gradio as gr
+import torch
 
-from transformers import BlipProcessor, BlipForConditionalGeneration
+from transformers import BlipForConditionalGeneration, BlipProcessor
 
-model_id = "Salesforce/blip-image-captioning-base"
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-model = BlipForConditionalGeneration.from_pretrained(model_id)
-processor = BlipProcessor.from_pretrained(model_id)
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+model_image_captioning = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to(device)
 
-def launch(input):
-    image = Image.open(requests.get(input, stream=True).raw).convert('RGB')
-    inputs = processor(image, return_tensors="pt")
-    out = model.generate(**inputs)
-    return processor.decode(out[0], skip_special_tokens=True)
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-iface = gr.Interface(launch, inputs="text", outputs="text")
-iface.launch()
+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)