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dc71762
1
Parent(s):
60eee60
all added
Browse files- Dockerfile +17 -0
- app/__pycache__/app.cpython-39.pyc +0 -0
- app/app.py +16 -0
- gs_clf.pickle +0 -0
- gs_clf.pickle:Zone.Identifier +3 -0
- requirements.txt +4 -0
- static/style.css +44 -0
- templates/index.html +20 -0
Dockerfile
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# Use the official Python image from the Docker Hub
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FROM python:3.9
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# Set the working directory to /code
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WORKDIR /code
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# Copy requirements.txt to the docker container
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COPY ./requirements.txt /code/requirements.txt
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# Install any dependencies
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RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
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# Copy the current directory contents into the container at /code
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COPY . .
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# Run the command to start Uvicorn
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CMD ["uvicorn", "app.app:app", "--host", "0.0.0.0", "--port", "7860"]
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app/__pycache__/app.cpython-39.pyc
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Binary file (760 Bytes). View file
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app/app.py
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from fastapi import FastAPI
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from pydantic import BaseModel
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from joblib import load
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import pickle
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app = FastAPI()
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with open('gs_clf.pickle', 'rb') as f:
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spam_classifier = pickle.load(f)
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class Item(BaseModel):
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text: str
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@app.post("/classify/")
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async def classify_text(item: Item):
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return {"result": spam_classifier.predict([item.text])[0]}
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gs_clf.pickle
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Binary file (942 kB). View file
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gs_clf.pickle:Zone.Identifier
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[ZoneTransfer]
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ZoneId=3
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HostUrl=about:internet
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requirements.txt
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fastapi
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uvicorn
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scikit-learn
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static/style.css
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body {
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font-family: Arial, sans-serif;
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color: #333;
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background-color: #f9f9f9;
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padding: 30px;
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}
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.container {
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display: flex;
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flex-direction: column;
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align-items: center;
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justify-content: center;
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height: 100vh;
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text-align: center;
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}
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textarea, button {
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width: 300px;
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margin-bottom: 20px;
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padding: 10px;
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font-size: 1em;
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border: none;
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border-radius: 5px;
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}
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textarea {
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height: 100px;
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resize: none;
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}
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button {
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color: #fff;
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background-color: #007BFF;
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cursor: pointer;
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transition: background-color 0.3s ease;
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}
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button:hover {
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background-color: #0056b3;
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}
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.result {
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font-size: 1.5em;
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}
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templates/index.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<title>Spam Classifier App</title>
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<link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
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</head>
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<body>
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<div class="container">
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<h1>Spam Classifier</h1>
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<form method="POST">
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<textarea name="message" required></textarea>
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<button type="submit">Classify</button>
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</form>
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{% if prediction %}
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<div class="result">Classification Result: {{ prediction }}</div>
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{% endif %}
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</div>
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</body>
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</html>
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