Create app.py

app.py

@@ -0,0 +1,90 @@
+import string
+import gradio as gr
+import requests
+import torch
+
+
+from transformers import BlipForQuestionAnswering, BlipProcessor
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-large")
+model_vqa = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-large").to(device)
+def inference_chat(input_image,input_text):
+    inputs = processor(images=input_image, text=input_text,return_tensors="pt")
+
+
+    inputs["max_length"] = 20
+    inputs["num_beams"] = 5
+
+    out = model_vqa.generate(**inputs)
+    return processor.batch_decode(out, skip_special_tokens=True)[0]
+    
+with gr.Blocks(
+    css="""
+    .message.svelte-w6rprc.svelte-w6rprc.svelte-w6rprc {font-size: 20px; margin-top: 20px}
+    #component-21 > div.wrap.svelte-w6rprc {height: 600px;}
+    """
+) as iface:
+    state = gr.State([])
+    #caption_output = None
+    #gr.Markdown(title)
+    #gr.Markdown(description)
+    #gr.Markdown(article)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(type="pil")
+
+            with gr.Row():
+                
+                with gr.Column(scale=1):
+                    caption_output = None
+                    chat_input = gr.Textbox(lines=1, label="VQA Input")
+                    chat_input.submit(
+                        inference_chat,
+                        [
+                            image_input,
+                            chat_input,
+                        ],
+                        [ caption_output],
+                    )
+                    
+                    with gr.Row():
+                        clear_button = gr.Button(value="Clear", interactive=True)
+                        clear_button.click(
+                            lambda: ("", [], []),
+                            [],
+                            [chat_input,  state],
+                            queue=False,
+                        )
+    
+                        submit_button = gr.Button(
+                            value="Submit", interactive=True, variant="primary"
+                        )
+                        submit_button.click(
+                            inference_chat,
+                            [
+                                image_input,
+                                chat_input,
+                            ],
+                            [caption_output],
+                        )
+                    caption_output = gr.Textbox(lines=1, label="VQA Output")
+            
+    
+            image_input.change(
+                lambda: ("", "", []),
+                [],
+                [ caption_output, state],
+                queue=False,
+            )
+        
+
+   # examples = gr.Examples(
+   #     examples=examples,
+   #     inputs=[image_input, chat_input],
+  #  )
+
+iface.queue(concurrency_count=1, api_open=False, max_size=10)
+iface.launch(enable_queue=True)