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Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.9-slim
+
+# Set the working directory in the container
+WORKDIR /code
+
+# Copy the requirements file into the container at /code
+COPY ./requirements.txt /code/requirements.txt
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Copy the rest of the application's code from the current directory to /code
+COPY . /code/
+
+# Expose the port the app runs on (Hugging Face uses 7860)
+EXPOSE 7860
+
+# Command to run the app using uvicorn
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel, Field, computed_field
+import numpy as np
+from typing import Literal, Annotated
+import pickle 
+import math
+import pandas as pd
+
+# import the ML model 
+with open('delivery_time_model.pkl','rb') as f:
+    model = pickle.load(f)
+    
+app = FastAPI()
+
+
+# pydantic model build to validate the input data
+
+class UserInput(BaseModel):
+    age : Annotated[int,Field(...,ge = 18, lt = 120,description = 'Age of the delivery person')]
+    rating : Annotated[float,Field(...,ge = 1, le = 6 ,description = 'Delivery person Ratings')]
+    distance : Annotated[int,Field(...,gt = 0,description = 'Total Distance to be covered')]
+    
+    
+@app.post('/predict')
+def predict_time(data: UserInput):
+    features = np.array([[data.age, data.rating, data.distance]])
+    prediction = model.predict(features)
+
+    prediction_value = math.ceil(float(prediction[0]))
+
+    return JSONResponse(
+        status_code=200,
+        content={"Predicted Delivery Time in Minutes": prediction_value}
+    )

delivery_time_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:951d09ab2ad27f612dcb8846718ce182f9412e597b4fd98d8cf345640a1432d4
+size 1449096

requirements.txt

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+# Libraries for FastAPI Backend
+fastapi
+uvicorn
+pydantic
+numpy
+scikit-learn
+pandas
+tensorflow
+keras
+# Libraries for Streamlit Frontend
+streamlit
+requests