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DockerFile

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+# Use the official Python image from the Docker Hub
+FROM python:3.9
+
+# Set the working directory in the container
+WORKDIR /code
+
+# Copy the requirements.txt file into the container
+COPY ./requirements.txt /code/requirements.txt
+
+# Install the dependencies
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Set up a new user named "user"
+RUN useradd user
+
+# Switch to the "user" user
+USER user
+
+# Set home to the user's home directory
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# Set working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the rest of the application into the container at $HOME/app
+COPY --chown=user . $Home/app
+
+# Set the command to run the FastAPI app with Uvicorn at 7860
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import re
+import string
+import joblib
+from sklearn.feature_extraction.text import TfidfVectorizer
+from fastapi import FastAPI, Request, Query
+from pydantic import BaseModel
+
+# Load the logistic regression model
+model = joblib.load('models/logistic_regression_model.pkl')
+
+# Load the TfidfVectorizer
+vectorization = joblib.load('models/tfidf_vectorizer.pkl')
+
+# Define the wordopt function
+def wordopt(text):
+    text = text.lower()
+    text = re.sub('\[.*?\]', '', text)
+    text = re.sub("\\W", " ", text)
+    text = re.sub('https?://\S+|www\.\S+', '', text)
+    text = re.sub('<.*?>+', '', text)
+    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
+    text = re.sub('\n', '', text)
+    text = re.sub('\w*\d\w*', '', text)
+    return text
+
+# Define the prediction function
+def predict_news(text):
+    text = wordopt(text)
+    text_vector = vectorization.transform([text])
+    prediction = model.predict(text_vector)
+    return "Fake" if prediction[0] == 1 else "Real"
+
+# FastAPI app
+app = FastAPI()
+
+@app.get("/")
+def home():
+    return {"message": "Hello World"}
+
+@app.get("/predict")
+def predict(text: str):
+    result = predict_news(text)
+    return {"result": result}
+
+# def predict(text: str = Query(..., description="Text to classify as fake or real news")):

models/logistic_regression_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:34898207a65a3c8ecd391c24297d6d5aa76def1814db94dcdf5b1e90996c8833
+size 759023

models/tfidf_vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:025e9ba8145dba27be38bb70954699b92a2eaef4af9d1fc122e16ae7dd6c9412
+size 2892814

requirements.txt

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+fastapi
+uvicorn
+scikit-learn
+joblib
+transformers
+datasets