add app, requirements and pre-commit

.pre-commit-config.yaml

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+default_language_version:
+    python: python3.7
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+    -   id: check-merge-conflict
+    -   id: check-case-conflict
+    -   id: check-yaml
+    -   id: detect-private-key
+    -   id: debug-statements
+    -   id: end-of-file-fixer
+    -   id: trailing-whitespace
+    -   id: double-quote-string-fixer
+    -   id: requirements-txt-fixer
+-   repo: https://github.com/asottile/reorder_python_imports
+    rev: v3.8.2
+    hooks:
+    -   id: reorder-python-imports
+-   repo: https://github.com/asottile/add-trailing-comma
+    rev: v2.2.3
+    hooks:
+    -   id: add-trailing-comma
+-   repo: https://github.com/asottile/pyupgrade
+    rev: v2.37.3
+    hooks:
+    -   id: pyupgrade
+        args: [--py37-plus]
+-   repo: https://github.com/pre-commit/mirrors-autopep8
+    rev: v1.7.0
+    hooks:
+    -   id: autopep8
+-   repo: https://github.com/pycqa/flake8
+    rev: 5.0.4
+    hooks:
+    -   id: flake8
+        additional_dependencies: [flake8-typing-imports==1.12.0]

app.py

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+import gradio as gr
+import numpy as np
+import torch
+from CCAgT_utils.types.mask import Mask
+from PIL import Image
+from torch import nn
+from transformers import SegformerFeatureExtractor
+from transformers import SegformerForSemanticSegmentation
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+model_hub_name = 'lapix/segformer-b3-finetuned-ccagt-400-300'
+
+model = SegformerForSemanticSegmentation.from_pretrained(
+    model_hub_name,
+).to(device)
+feature_extractor = SegformerFeatureExtractor.from_pretrained(
+    model_hub_name,
+)
+
+
+def query_image(image):
+    image = np.array(image)
+    img = Image.fromarray(image)
+
+    pixel_values = feature_extractor(
+        image,
+        return_tensors='pt',
+    ).to(device)
+
+    with torch.no_grad():
+        outputs = model(pixel_values=pixel_values)
+
+    logits = outputs.logits
+
+    upsampled_logits = nn.functional.interpolate(
+        logits,
+        size=img.size[::-1],  # (height, width)
+        mode='bilinear',
+        align_corners=False,
+    )
+
+    segmentation_mask = upsampled_logits.argmax(dim=1)[0]
+
+    results = Mask(segmentation_mask).colorized() / 255
+
+    return results
+
+
+title = 'SegFormer (b3) - CCAgT dataset'
+description = f"""
+This is demo for the SegFormer fine-tuned on sub-dataset from
+[CCAgT dataset](https://huggingface.co/datasets/lapix/CCAgT). This model
+was trained to segment cervical cells silver-stained (AgNOR technique)
+images with resolution of 400x300. The model was available at HF hub at
+[{model_hub_name}](https://huggingface.co/{model_hub_name}).
+"""
+examples = [
+    [f'https://hf.co/{model_hub_name}/resolve/main/sampleA.png'],
+    [f'https://hf.co/{model_hub_name}/resolve/main/sampleB.png'],
+]
+
+demo = gr.Interface(
+    query_image,
+    inputs=[gr.Image()],
+    outputs='image',
+    title=title,
+    description=description,
+    examples=examples,
+)
+
+demo.launch()

requirements.txt

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+CCAgT-utils
+numpy
+torch
+transformers