Task6: FastAPI + Streamlit deployment

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import joblib
+import numpy as np
+
+# Load your trained model
+model = joblib.load("model.joblib")
+
+# Initialize FastAPI app
+app = FastAPI(title="Supermarket Sales Forecast API")
+
+# Define input schema (replace with actual model features)
+class SalesInput(BaseModel):
+    feature1: float
+    feature2: float
+    feature3: float
+
+@app.post("/predict")
+def predict(data: SalesInput):
+    # Convert input to array for prediction
+    input_data = np.array([[data.feature1, data.feature2, data.feature3]])
+    prediction = model.predict(input_data)
+    return {"prediction": prediction.tolist()}

dockerfile

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+# Use official Python base image
+FROM python:3.12-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy project files
+COPY requirements.txt .
+COPY app.py .
+COPY model.joblib .
+COPY streamlit_app.py .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose FastAPI port
+EXPOSE 8000
+
+# Command to run FastAPI
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

requirements.txt

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+fastapi
+uvicorn
+pydantic
+joblib
+numpy
+requests
+streamlit

streamlit_app.py

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+import streamlit as st
+import requests
+
+st.title("Supermarket Sales Forecast")
+
+# Input sliders or number inputs
+feature1 = st.number_input("Feature 1")
+feature2 = st.number_input("Feature 2")
+feature3 = st.number_input("Feature 3")
+
+if st.button("Predict"):
+    data = {
+        "feature1": feature1,
+        "feature2": feature2,
+        "feature3": feature3
+    }
+    response = requests.post("https://YOUR_DEPLOYED_API_URL/predict", json=data)
+    st.write("Prediction:", response.json()["prediction"])