first commit

Dockerfile

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+FROM continuumio/miniconda3
+
+RUN apt-get update -y 
+RUN apt-get install nano unzip curl -y
+
+# THIS IS SPECIFIC TO HUGGINFACE
+# We create a new user named "user" with ID of 1000
+RUN useradd -m -u 1000 user
+# We switch from "root" (default user when creating an image) to "user" 
+USER user
+# We set two environmnet variables 
+# so that we can give ownership to all files in there afterwards
+# we also add /home/user/.local/bin in the $PATH environment variable 
+# PATH environment variable sets paths to look for installed binaries
+# We update it so that Linux knows where to look for binaries if we were to install them with "user".
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# We set working directory to $HOME/app (<=> /home/user/app)
+WORKDIR $HOME/app
+
+# Install basic dependencies
+RUN pip install boto3 pandas gunicorn streamlit scikit-learn matplotlib seaborn plotly
+
+# Copy all local files to /home/user/app with "user" as owner of these files
+# Always use --chown=user when using HUGGINGFACE to avoid permission errors
+COPY --chown=user . $HOME/app
+
+COPY requirements.txt /dependencies/requirements.txt
+RUN pip install -r /dependencies/requirements.txt
+
+COPY . $HOME/app
+
+CMD fastapi run app.py --port $PORT

README.md

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+---
+title: Test Api 2
+emoji: 📈
+colorFrom: gray
+colorTo: green
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import mlflow 
+import uvicorn
+import pandas as pd 
+from pydantic import BaseModel
+from typing import Literal, List, Union
+from fastapi import FastAPI, File, UploadFile
+import joblib
+
+# Log model from mlflow 
+logged_model = 'runs:/e7b51184619c45f9b2fbb017dfe0a49f/model'
+
+# Load model as a PyFuncModel.
+loaded_model = mlflow.pyfunc.load_model(logged_model)
+
+tags_metadata = [
+    {
+        "name": "Machine Learning",
+        "description": "Prediction Endpoint."
+    }
+]
+
+app = FastAPI(
+    title="Demo Iris API",
+    openapi_tags=tags_metadata
+)
+
+class PredictionFeatures(BaseModel):
+    sepal_length: float
+    sepal_width: float
+    petal_length: float
+    petal_width: float
+
+@app.get("/", tags=["Introduction Endpoints"])
+async def index():
+    """
+    Simply returns a welcome message!
+    """
+    message = "Hello world! This `/` is the most simple and default endpoint. If you want to learn more, check out documentation of the api at `/docs`"
+    return message
+
+
+@app.post("/predict", tags=["Machine Learning"])
+async def predict(predictionFeatures: PredictionFeatures):
+    # Read data 
+    input_data = pd.DataFrame({
+            "sepal length (cm)": [predictionFeatures.sepal_length],
+            "sepal width (cm)": [predictionFeatures.sepal_width],
+            "petal length (cm)": [predictionFeatures.petal_length],
+            "petal width (cm)": [predictionFeatures.petal_width]
+        })
+
+    prediction = loaded_model.predict(input_data)
+
+    # Format response
+    response = {"prediction": prediction.tolist()[0]}
+    return response

requirements.txt

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+fastapi[standard]
+pydantic 
+typing 
+pandas 
+openpyxl 
+mlflow 
+boto3 
+scikit-learn
+python-multipart
+fsspec
+s3fs
+streamlit
+matplotlib
+seaborn
+plotly