Create app.py

app.py

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+import gradio as gr 
+from lavis.models import load_model_and_preprocess
+import torch
+
+device = torch.device("cuda") if torch.cuda.is_available() else "cpu"
+
+
+model_name = "blip2_t5_instruct"
+model_type = "flant5xl"
+model, vis_processors, _ = load_model_and_preprocess(
+    name=args.model_name,
+    model_type=args.model_type,
+    is_eval=True,
+    device=device,
+)
+
+def infer(image, prompt, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, decoding_method):
+        use_nucleus_sampling = decoding_method == "Nucleus sampling"
+        print(image, prompt, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, use_nucleus_sampling)
+        image = vis_processors["eval"](image).unsqueeze(0).to(device)
+
+        samples = {
+            "image": image,
+            "prompt": prompt,
+        }
+
+        output = model.generate(
+            samples,
+            length_penalty=float(len_penalty),
+            repetition_penalty=float(repetition_penalty),
+            num_beams=beam_size,
+            max_length=max_len,
+            min_length=min_len,
+            top_p=top_p,
+            use_nucleus_sampling=use_nucleus_sampling,
+        )
+
+        return output[0]
+    
+theme = gr.themes.Monochrome(
+    primary_hue="indigo",
+    secondary_hue="blue",
+    neutral_hue="slate",
+    radius_size=gr.themes.sizes.radius_sm,
+    font=[gr.themes.GoogleFont("Open Sans"), "ui-sans-serif", "system-ui", "sans-serif"],
+)
+css = ".generating {visibility: hidden}"
+
+with gr.Blocks(theme=theme, analytics_enabled=False,css=css) as demo:
+    with gr.Column(scale=3):
+        image_input = gr.Image(type="pil")
+        prompt_textbox = gr.Textbox(label="Prompt:", placeholder="prompt", lines=2)
+        output = gr.Textbox(label="Output")
+        submit = gr.Button("Run", variant="primary")
+
+    with gr.Column(scale=1):
+        min_len = gr.Slider(
+            minimum=1,
+            maximum=50,
+            value=1,
+            step=1,
+            interactive=True,
+            label="Min Length",
+        )
+    
+        max_len = gr.Slider(
+            minimum=10,
+            maximum=500,
+            value=250,
+            step=5,
+            interactive=True,
+            label="Max Length",
+        )
+    
+        sampling = gr.Radio(
+            choices=["Beam search", "Nucleus sampling"],
+            value="Beam search",
+            label="Text Decoding Method",
+            interactive=True,
+        )
+    
+        top_p = gr.Slider(
+            minimum=0.5,
+            maximum=1.0,
+            value=0.9,
+            step=0.1,
+            interactive=True,
+            label="Top p",
+        )
+    
+        beam_size = gr.Slider(
+            minimum=1,
+            maximum=10,
+            value=5,
+            step=1,
+            interactive=True,
+            label="Beam Size",
+        )
+    
+        len_penalty = gr.Slider(
+            minimum=-1,
+            maximum=2,
+            value=1,
+            step=0.2,
+            interactive=True,
+            label="Length Penalty",
+        )
+    
+        repetition_penalty = gr.Slider(
+            minimum=-1,
+            maximum=3,
+            value=1,
+            step=0.2,
+            interactive=True,
+            label="Repetition Penalty",
+        )
+    
+    submit.click(infer, inputs=[image_input, prompt_textbox, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, sampling], outputs=[output])
+
+demo.queue(concurrency_count=16).launch(debug=True)