Upload 4 files
Browse files- Dockerfile +31 -0
- main.py +68 -0
- requirements.txt +9 -0
- use_api_in_python.png +0 -0
Dockerfile
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# Use the official Python 3.9 image
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FROM python:3.9
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RUN apt-get update && apt-get install -y \
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tesseract-ocr-all \
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&& rm -rf /var/lib/apt/lists/*
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# Set the working directory to /code
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WORKDIR /code
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# Copy the current directory contents into the container at /code
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COPY ./requirements.txt /code/requirements.txt
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# Install requirements.txt
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RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
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# Set up a new user named "user" with user ID 1000
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RUN useradd -m -u 1000 user
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# Switch to the "user" user
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USER user
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# Set home to the user's home directory
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH
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# Set the working directory to the user's home directory
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WORKDIR $HOME/app
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# Copy the current directory contents into the container at $HOME/app setting the owner to the user
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COPY --chown=user . $HOME/app
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]
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main.py
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from fastapi import FastAPI, UploadFile, File
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from fastapi.responses import HTMLResponse
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import pandas as pd
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import io
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import requests
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import time
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app = FastAPI()
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@app.get("/", response_class=HTMLResponse)
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async def analyze_logs():
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return """
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<html>
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<body>
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<form action="/upload/" enctype="multipart/form-data" method="post">
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<input name="file" type="file">
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<input type="submit">
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</form>
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</body>
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</html>
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"""
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@app.post("/upload/")
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async def upload_file(file: UploadFile = File(...)):
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contents = await file.read()
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logs_df = pd.read_parquet(io.BytesIO(contents))
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processing_message = "\n\n Processing files...\n\n"
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time.sleep(3) # Simulate processing time (3 seconds)
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logs_df['datetime'] = pd.to_datetime(logs_df['datetime'], format='%d/%m/%Y:%H:%M:%S')
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logs_df['day'] = logs_df['datetime'].apply(lambda x: x.day)
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logs_df['hour'] = logs_df['datetime'].apply(lambda x: x.hour)
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logs_df['minute'] = logs_df['datetime'].apply(lambda x: x.minute)
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ip_address_count_df = (
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logs_df.groupby(['method', 'client'], as_index=False)
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.size()
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.rename(columns={'size': 'count'})
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.sort_values('count', ascending=False)
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)
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ip_address_count_df = ip_address_count_df.assign(
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perc=ip_address_count_df['count'].div(ip_address_count_df['count'].sum()),
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cum_perc=lambda df: df['perc'].cumsum(),
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)
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result = (
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"<h1>Redundant IP Requests....</h1>"
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"<p>The Total API Requests from the sample logs are : {total_requests}</p>"
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"<p>The Redundant API Requests from the sample logs are : {redundant_requests}</p>"
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"<p>The percentage of Redundant API Requests from the sample logs is : {redundant_percentage:.2f}%</p>"
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"{dataframe_html}"
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).format(
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total_requests=logs_df.shape[0],
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redundant_requests=ip_address_count_df.shape[0],
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redundant_percentage=(ip_address_count_df.shape[0] / logs_df.shape[0]) * 100,
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dataframe_html=ip_address_count_df.head(1000)
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.style.background_gradient(subset=['count', 'perc', 'cum_perc'], cmap='cividis')
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.format({'count': '{:,}', 'perc': '{:.1%}', 'cum_perc': '{:.1%}'})
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.render(),
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)
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# Save result in a new HTML file
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with open("result.html", "w") as f:
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f.write(result)
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return "Result saved in 'result.html'"
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requirements.txt
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fastapi==0.74.*
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requests==2.27.*
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uvicorn[standard]==0.17.*
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sentencepiece==0.1.*
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torch==1.11.*
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transformers[vision]==4.*
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pytesseract==0.3.10
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python-multipart==0.0.6
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pandas
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use_api_in_python.png
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