bugfix

app.py

@@ -13,7 +13,7 @@ model = BlipForConditionalGeneration.from_pretrained(
     _MODEL_PATH, use_auth_token=HF_TOKEN).half().eval().to(device)
 
 
-def inference(raw_image, model_n, question, strategy):
+def inference(raw_image, model_n, strategy):
     if model_n == 'Image Captioning':
         input = processor(raw_image).to(device, torch.float16)
         with torch.no_grad():
@@ -38,8 +38,7 @@ def inference(raw_image, model_n, question, strategy):
             return 'caption: '+caption
 
 
-inputs = [gr.inputs.Image(type='pil'), gr.inputs.Radio(choices=['Image Captioning'], type="value", default="Image Captioning", label="Task"), gr.inputs.Textbox(
-    lines=2, label="Question"), gr.inputs.Radio(choices=['Beam search', 'Nucleus sampling'], type="value", default="Nucleus sampling", label="Caption Decoding Strategy")]
+inputs = [gr.inputs.Image(type='pil'), gr.inputs.Radio(choices=['Image Captioning'], type="value", default="Image Captioning", label="Task"), gr.inputs.Radio(choices=['Beam search', 'Nucleus sampling'], type="value", default="Nucleus sampling", label="Caption Decoding Strategy")]
 outputs = gr.outputs.Textbox(label="Output")
 
 title = "BLIP"
@@ -50,4 +49,4 @@ article = "<p style='text-align: center'><a href='https://github.com/IDEA-CCNL/F
 
 
 gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=[
-             ['demo.jpg', "Image Captioning", "None", "Nucleus sampling"]]).launch(enable_queue=True)
+             ['demo.jpg', "Image Captioning", "Nucleus sampling"]]).launch(enable_queue=True)