Update Streamlit app

Dockerfile

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+FROM python:3.11.7-slim
+
+# Set the working directory inside the container to /app
+WORKDIR /app
+
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
+
+# Install Python dependencies listed in requirements
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+#Define the command to run the Flask application on port 7860 and make it accessible externally
+# - '-w 4' : Uses 4 worker processes to handle requests.
+# - '-b 0.0.0.0:7860': Binds the Flask app to the specified host and port.
+# - 'app:sales_prediction_api': Specifies the entry point of the Flask app.
+CMD ["gunicorn", "w", "4", "-b", "0.0.0.0:7860" "app:sales_prediction_api"]

app.py

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+# app.py
+import numpy as np
+from flask import Flask, request, jsonify
+import joblib
+import pandas as pd
+
+# Inititialize Flask app with name
+sales_prediction_api = Flask("Sales Predictor")
+
+# Load the trained model predictor model
+dt_model = joblib.load("decision_tree_model.pkl")
+xgb_model = joblib.load("xgboost_model.pkl")
+
+# Define a route for the home page
+@sales_prediction_api.route('/')
+def home():
+    return "Sales Prediction API"
+
+# Define an endpoint to predict sales
+@sales_prediction_api.post('/predict')
+def predict():
+    # Get the data from the request
+    data = request.get_json()
+
+    # Extract relevant features from the input data
+    sample = {
+        'Product_Weight': data['Product_Weight'],
+        'Product_Sugar_Content': data['Product_Sugar_Content'],
+        'Product_Allocated_Area':  data['Product_Allocated_Area'],
+        'Product_Type': data['Product_Type'],
+        'Product_MRP': data['Product_MRP'],
+        'Store_Size': data['Store_Size'],
+        'Store_Location_City_Type': data['Store_Location_City_Type'],
+        'Store_Type': data['Store_Type'],
+        'Store_Age': data['Store_Age']
+    }
+
+    #convert the extracted data into a dataframe
+    sample_df = pd.DataFrame(sample, index=[0])
+
+    # --------------------------------
+    # Model selection logic (FIXED)
+    # --------------------------------
+    model_choice = data.get("model", "dt")
+
+    if model_choice == "dt":
+        prediction = dt_model.predict(sample_df)[0]
+
+    else :
+        prediction = xgb_model.predict(sample_df)[0]
+
+
+    # --------------------------------
+    # Response
+    # --------------------------------
+    return jsonify({
+        "model_used": model_choice,
+        "prediction": float(prediction)
+    })
+
+if __name__ == '__main__':
+    sales_prediction_api.run(debug=True)

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+seaborn==0.13.2
+joblib==1.4.2
+xgboost==2.1.4
+joblib==1.4.2
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
+requests==2.32.3
+uvicorn[standard]
+streamlit==1.43.2