Duplicate from taesiri/BLIP-2

Co-authored-by: taesiri <taesiri@users.noreply.huggingface.co>

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README.md

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+---
+title: BLIP-2
+emoji: 👁
+colorFrom: purple
+colorTo: gray
+sdk: gradio
+sdk_version: 3.17.0
+app_file: app.py
+pinned: false
+license: other
+duplicated_from: taesiri/BLIP-2
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+
+import gradio as gr
+import numpy as np
+import torch
+from lavis.models import load_model_and_preprocess
+from PIL import Image
+
+device = torch.device("cuda") if torch.cuda.is_available() else "cpu"
+
+model, vis_processors, _ = load_model_and_preprocess(
+    name="blip2_opt", model_type="pretrain_opt2.7b", is_eval=True, device=device
+)
+
+
+def generate_caption(image, caption_type):
+    image = vis_processors["eval"](image).unsqueeze(0).to(device)
+
+    if caption_type == "Beam Search":
+        caption = model.generate({"image": image})
+    else:
+        caption = model.generate(
+            {"image": image}, use_nucleus_sampling=True, num_captions=3
+        )
+
+    caption = "\n".join(caption)
+
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
+    return caption
+
+
+def chat(input_image, question, history):
+    history = history or []
+    question = question.lower()
+
+    image = vis_processors["eval"](input_image).unsqueeze(0).to(device)
+
+    clean = lambda x: x.replace("<p>", "").replace("</p>", "").replace("\n", "")
+    clean_h = lambda x: (clean(x[0]), clean(x[1]))
+    context = list(map(clean_h, history))
+    template = "Question: {} Answer: {}."
+    prompt = (
+        " ".join(
+            [template.format(context[i][0], context[i][1]) for i in range(len(context))]
+        )
+        + " Question: "
+        + question
+        + " Answer:"
+    )
+
+    response = model.generate({"image": image, "prompt": prompt})
+    history.append((question, response[0]))
+
+    return history, history
+
+
+def clear_chat(history):
+    return [], []
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        "### BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models"
+    )
+    gr.Markdown(
+        "This demo uses the `pretrain_opt2.7b` weights. For more information please visit [Github](https://github.com/salesforce/LAVIS/tree/main/projects/blip2) or [Paper](https://arxiv.org/abs/2301.12597)."
+    )
+
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(label="Image", type="pil")
+            caption_type = gr.Radio(
+                ["Beam Search", "Nucleus Sampling"],
+                label="Caption Decoding Strategy",
+                value="Beam Search",
+            )
+            btn_caption = gr.Button("Generate Caption")
+            output_text = gr.Textbox(label="Answer", lines=5)
+
+        with gr.Column():
+            chatbot = gr.Chatbot().style(color_map=("green", "pink"))
+            chat_state = gr.State()
+
+            question_txt = gr.Textbox(label="Question", lines=1)
+            btn_answer = gr.Button("Generate Answer")
+            btn_clear = gr.Button("Clear Chat")
+
+    btn_caption.click(
+        generate_caption, inputs=[input_image, caption_type], outputs=[output_text]
+    )
+
+    btn_answer.click(
+        chat,
+        inputs=[input_image, question_txt, chat_state],
+        outputs=[chatbot, chat_state],
+    )
+
+    btn_clear.click(clear_chat, inputs=[chat_state], outputs=[chatbot, chat_state])
+
+    gr.Examples(
+        [
+            ["./merlion.png", "Beam Search", "which city is this?"],
+            [
+                "./Blue_Jay_0044_62759.jpg",
+                "Beam Search",
+                "what is the name of this bird?",
+            ],
+            ["./5kstbz-0001.png", "Beam Search", "where is the man standing?"],
+            [
+                "ILSVRC2012_val_00000008.JPEG",
+                "Beam Search",
+                "Name the colors of macarons you see in the image.",
+            ],
+        ],
+        inputs=[input_image, caption_type, question_txt],
+    )
+
+    gr.Markdown(
+        "Sample images are taken from [ImageNet](https://paperswithcode.com/sota/image-classification-on-imagenet), [CUB](https://paperswithcode.com/dataset/cub-200-2011) and [GamePhysics](https://asgaardlab.github.io/CLIPxGamePhysics/) datasets."
+    )
+
+demo.launch()

requirements.txt

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+torch
+torchvision
+salesforce-lavis