init repo

Dockerfile

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+FROM python:3.10
+
+WORKDIR /home/app
+
+RUN apt-get update -y 
+RUN apt-get install nano unzip -y
+RUN apt install curl -y
+
+RUN curl -fsSL https://get.deta.dev/cli.sh | sh
+
+COPY requirements.txt /dependencies/requirements.txt
+RUN pip install -r /dependencies/requirements.txt
+
+COPY . /home/app
+
+CMD gunicorn app:app  --bind 0.0.0.0:$PORT --worker-class uvicorn.workers.UvicornWorker 

app.py

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+import os
+import uvicorn
+import pandas as pd
+from pydantic import BaseModel
+from fastapi import FastAPI, File, UploadFile
+import mlflow
+import os
+from dotenv import load_dotenv
+
+description = """
+
+# Climate Fake News Detector(https://github.com/Olivier-52/Fake_news_detector.git)
+
+This API allows you to use a Machine Learning model to detect fake news related to climate change.
+
+## Machine-Learning 
+
+Where you can:
+* `/predict` : prediction for a single value
+
+Check out documentation for more information on each endpoint. 
+"""
+
+tags_metadata = [
+    {
+        "name": "Predictions",
+        "description": "Endpoints that uses our Machine Learning model",
+    },
+]
+
+load_dotenv()
+MLFLOW_TRACKING_URI = os.environ["MLFLOW_TRACKING_APP_URI"]
+AWS_ACCESS_KEY_ID = os.environ["AWS_ACCESS_KEY_ID"]
+AWS_SECRET_ACCESS_KEY = os.environ["AWS_SECRET_ACCESS_KEY"]
+MODEL_URI = "models:/climate-fake-news-detector-model-XGBoost-v1@production"
+mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)
+model_uri = MODEL_URI 
+model = mlflow.sklearn.load_model(model_uri)
+
+app = FastAPI(
+    title="API for Climate Fake News Detector",
+    description=description,
+    version="1.0",
+    contact={
+        "name": "Olivier",
+        "url": "https://github.com/Olivier-52/Fake_news_detector",
+    },
+    openapi_tags=tags_metadata,)
+
+@app.get("/")
+def index():
+    """Return a message to the user.
+
+    This endpoint does not take any parameters and returns a message
+    to the user. It is used to test the API.
+
+    Returns:
+        str: A message to the user.
+    """
+    return "Hello world! Go to /docs to try the API."
+
+
+class PredictionFeatures(BaseModel):
+    text: str
+
+@app.post("/predict", tags=["Predictions"])
+def predict(features: PredictionFeatures):
+    """Predict Climate Fake News.
+
+    This endpoint takes a text as input and returns the predicted class : fake, real, or biased.
+
+    Args:
+        features (PredictionFeatures): A PredictionFeatures object
+            containing the text to analyze.
+
+    Returns:
+        dict: A dictionary containing the predicted class.
+    """
+    df = pd.DataFrame({
+        "text": [features.text],
+    })
+    
+    prediction = model.predict(df)[0]
+    return {"prediction": float(prediction)}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="localhost", port=8000)

requirements.txt

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+mlflow==2.21.3
+scikit-learn==1.4.2
+requests>=2.31.0,<3
+fastapi 
+uvicorn[standard]
+pydantic 
+typing 
+pandas 
+gunicorn 
+openpyxl 
+boto3
+python-multipart
+dotenv