Init commit

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 uvicorn
+import pandas as pd
+from fastapi import FastAPI
+
+description = """
+# Climate news generator
+
+Generate a random news about climate change from [readerbench/fakenews-climate-fr](https://huggingface.co/datasets/readerbench/fakenews-climate-fr) dataset.
+
+Use for testing data pipelines.
+
+Where you can:
+* `/get_news` : Get a random news
+
+Check out documentation for more information on each endpoint. 
+"""
+
+tags_metadata = [
+    {
+        "name": "Generator",
+        "description": "Endpoints that generate news about climate change",
+    },
+]
+
+data = pd.read_csv("hf://datasets/readerbench/fakenews-climate-fr/fake-fr.csv")
+
+app = FastAPI(
+    title="API for Climate news generator",
+    description=description,
+    version="1.0",
+    contact={
+        "name": "Olivier-52",
+        "url": "https://huggingface.co/Olivier-52",
+    },
+    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."
+
+@app.get("/get_news", tags=["Generator"])
+def get_news():
+    """Return a random news about climate change.
+
+    This endpoint does not take any parameters and returns a random news
+    about climate change in JSON format.
+
+    Returns:
+        dict: A JSON object containing a random news about climate change.
+    """
+    if data.empty:
+        return {"error": "No data available"}
+    return data.sample(1).to_json(orient="records")
+
+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