Create app.py

app.py

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+import pathlib
+
+import gradio as gr
+import open_clip
+import torch
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+model, _, transform = open_clip.create_model_and_transforms(
+    "coca_ViT-L-14",
+    pretrained="mscoco_finetuned_laion2B-s13B-b90k"
+)
+model.to(device)
+
+title="""<h1 align="center">CoCa: Contrastive Captioners</h1>"""
+description=(
+    """<br> An open source implementation of <strong>CoCa: Contrastive Captioners are Image-Text Foundation Models</strong> <a href=https://arxiv.org/abs/2205.01917>https://arxiv.org/abs/2205.01917.</a>
+    <br> Built using <a href=https://github.com/mlfoundations/open_clip>open_clip</a> with an effort from <a href=https://laion.ai/>LAION</a>. 
+    <br> For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.<a href="https://huggingface.co/spaces/laion/CoCa?duplicate=true"> <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>"""
+)
+
+def output_generate(image):
+    im = transform(image).unsqueeze(0).to(device)
+    with torch.no_grad(), torch.cuda.amp.autocast():
+        generated = model.generate(im, seq_len=20)
+    return open_clip.decode(generated[0].detach()).split("<end_of_text>")[0].replace("<start_of_text>", "")
+
+def inference_caption(image, decoding_method="Beam search", rep_penalty=1.2, top_p=0.5, min_seq_len=5, seq_len=20):
+    im = transform(image).unsqueeze(0).to(device)
+    generation_type = "beam_search" if decoding_method == "Beam search" else "top_p"
+    with torch.no_grad(), torch.cuda.amp.autocast():
+        generated = model.generate(
+            im, 
+            generation_type=generation_type,
+            top_p=float(top_p), 
+            min_seq_len=min_seq_len, 
+            seq_len=seq_len, 
+            repetition_penalty=float(rep_penalty)
+        )
+    return open_clip.decode(generated[0].detach()).split("<end_of_text>")[0].replace("<start_of_text>", "")
+
+paths = sorted(pathlib.Path("images").glob("*.jpg"))
+with gr.Blocks() as iface:
+    state = gr.State([])
+
+    gr.Markdown(title)
+    gr.Markdown(description)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(type="pil")
+
+            # with gr.Row():
+            sampling = gr.Radio(
+                choices=["Beam search", "Nucleus sampling"],
+                value="Beam search",
+                label="Text Decoding Method",
+                interactive=True,
+            )
+
+            rep_penalty = gr.Slider(
+                minimum=1.0,
+                maximum=5.0,
+                value=1.0,
+                step=0.5,
+                interactive=True,
+                label="Repeat Penalty (larger value prevents repetition)",
+            )
+            
+            top_p = gr.Slider(
+                minimum=0.0,
+                maximum=1.0,
+                value=0.5,
+                step=0.1,
+                interactive=True,
+                label="Top p (used with nucleus sampling)",
+            )
+            
+            min_seq_len = gr.Number(
+                value=5, label="Minimum Sequence Length", precision=0, interactive=True
+            )
+            
+            seq_len = gr.Number(
+                value=20, label="Maximum Sequence Length (has to higher than Minimum)", precision=0, interactive=True
+            )
+
+        with gr.Column(scale=1):
+
+            with gr.Column():
+                caption_output = gr.Textbox(lines=1, label="Caption Output")
+                caption_button = gr.Button(
+                    value="Caption it!", interactive=True, variant="primary"
+                )
+                caption_button.click(
+                    inference_caption,
+                    [
+                        image_input,
+                        sampling,
+                        rep_penalty,
+                        top_p,
+                        min_seq_len,
+                        seq_len
+                    ],
+                    [caption_output],
+                )
+
+    examples = gr.Examples(
+        examples=[path.as_posix() for path in paths],
+        inputs=[image_input],
+    )
+iface.launch()