LapStore
commited on
Commit
·
b1f46e5
1
Parent(s):
7126c79
try space
Browse files- Dockerfile +16 -0
- app.py +79 -0
- requirements.txt +7 -0
Dockerfile
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# Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
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# you will also find guides on how best to write your Dockerfile
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FROM python:3.9
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RUN useradd -m -u 1000 user
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USER user
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ENV PATH="/home/user/.local/bin:$PATH"
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WORKDIR /app
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COPY --chown=user ./requirements.txt requirements.txt
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RUN pip install --no-cache-dir --upgrade -r requirements.txt
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COPY --chown=user . /app
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CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
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app.py
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from fastapi import FastAPI ,Request ,Form, UploadFile, File
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from fastapi.responses import JSONResponse
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from fastapi.responses import HTMLResponse, FileResponse
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import os
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import io
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from PIL import ImageOps,Image ,ImageFilter
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#from transformers import pipeline
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import matplotlib.pyplot as plt
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import numpy as np
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import ast
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#http://localhost:8000
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app = FastAPI()
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# Root route
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@app.get('/')
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def hello_world():
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return "Hello World taha"
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def get_segment_image(raw_image):
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#pipe = pipeline("image-segmentation", model="Intel/dpt-large-ade")
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#output = pipe(raw_image, points_per_batch=32)
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#return output
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pass
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def get_supported_segmentation(output,supported_types):
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return [obj for obj in output if (obj['label'] in supported_types)]
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@app.post('/predict')
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async def predict(supported_types_str: str = Form(),age: str = Form() , file: UploadFile = File(...)):
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# Form(...) to accept input as web form ,may change when android /upload
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supported_types=ast.literal_eval(supported_types_str)
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contents = await file.read()
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image = Image.open(io.BytesIO(contents))
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# Process the image (example: convert to grayscale)
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processed_image = image.convert("L")
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# Save the processed image to a temporary file
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output_file_path = "tmp_processed_image.png"
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processed_image.save(output_file_path)
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# Return the processed image for download
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return FileResponse(output_file_path, media_type='image/png', filename="tmp_processed_image.png")
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@app.post('/predict2')
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async def predict2(supported_types_str: str = Form(...), age: str = Form(...), file: UploadFile = File(...)):
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contents = await file.read()
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image = Image.open(io.BytesIO(contents))
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# Process the image (example: convert to grayscale)
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processed_image = image.convert("L")
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# Save the processed image to a BytesIO object
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img_byte_arr = io.BytesIO()
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processed_image.save(img_byte_arr, format='PNG')
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img_byte_arr.seek(0) # Move to the beginning of the BytesIO buffer
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# Create an HTML response with the processed image
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html_content = f"""
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<html>
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<body>
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<h3>Processed Image:</h3>
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<img src="data:image/png;base64,{img_byte_arr.getvalue().decode('latin1')}" alt="Processed Image" style="max-width: 500px;"/>
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<br><br>
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<p>Your name: {supported_types_str}</p>
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<p>Your age: {age}</p>
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<p><a href="/download">Download Processed Image</a></p>
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</body>
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</html>
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"""
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return HTMLResponse(content=html_content)
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requirements.txt
ADDED
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@@ -0,0 +1,7 @@
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| 1 |
+
fastapi
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| 2 |
+
uvicorn[standard]
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+
Pillow
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| 4 |
+
transformers
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+
matplotlib
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numpy
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python-multipart
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