Update Streamlit app

Dockerfile

@@ -1,20 +1,16 @@
-FROM python:3.13.5-slim
+FROM python:3.11.7-slim
 
+# Set the working directory inside the container to /app
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
 
+# Install Python dependencies listed in requirements
 RUN pip3 install -r requirements.txt
 
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+#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

@@ -0,0 +1,66 @@
+# 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]
+
+    elif model_choice == "xgb":
+        prediction = xgb_model.predict(sample_df)[0]
+
+    else:
+        return jsonify({"error": "Invalid model specified. Use 'dt' or 'xgb'"}), 400
+
+
+
+    # --------------------------------
+    # Response
+    # --------------------------------
+    return jsonify({
+        "model_used": model_choice,
+        "prediction": float(prediction)
+    })
+
+if __name__ == '__main__':
+    sales_prediction_api.run(host="0.0.0.0", port=7860,debug=True)

requirements.txt

@@ -1,3 +1,9 @@
-altair
-pandas
-streamlit
+pandas==2.1.4
+flask==3.0.1
+joblib==1.3.2
+numpy==1.26.4
+scikit-learn==1.4.0
+xgboost==2.0.3
+streamlit==1.30.0
+requests==2.31.0
+gunicorn==21.2.0