update

demand_forecast/Dockerfile

@@ -7,11 +7,14 @@ WORKDIR /app
 # Copy the current directory contents into the container at /app
 COPY . /app
 
-# Install any necessary dependencies
+# Upgrade pip to the latest version before installing dependencies
+RUN pip install --upgrade pip
+
+# Install necessary dependencies
 RUN pip install --no-cache-dir -r requirements.txt
 
 # Expose port 5000 for Flask to run on
 EXPOSE 5000
 
-# Run the application
+# Set the default command to run the application
 CMD ["python", "app.py"]

demand_forecast/app.py

@@ -1,31 +1,63 @@
 from flask import Flask, request, jsonify, send_file
+from flask_cors import CORS
+from pathlib import Path
 from forecasting import predict_demand, check_stock_and_alert
 from bokeh_forecast import create_bokeh_plots
 from utils import load_data
 import os
+import logging
 
 app = Flask(__name__)
 
-# Directory to store uploaded files and plots
-UPLOAD_FOLDER = 'uploads'
-os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+# Enable CORS
+CORS(app, resources={r"/*": {"origins": "*"}})
+
+# Configure upload folder and file size limit
+UPLOAD_FOLDER = Path(os.getenv('UPLOAD_FOLDER', 'uploads'))
+UPLOAD_FOLDER.mkdir(parents=True, exist_ok=True)
+app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024  # 16 MB
+
+# Logging setup
+app.logger.setLevel(logging.INFO)
+
+# Allowed file extensions
+ALLOWED_EXTENSIONS = {'csv', 'xlsx'}
 data_file_path = None
 
 
+def allowed_file(filename):
+    """Check if the uploaded file has an allowed extension."""
+    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+
 @app.route('/upload', methods=['POST'])
 def upload_file():
     global data_file_path
+
     if 'file' not in request.files:
         return jsonify({"error": "No file part in the request"}), 400
 
     file = request.files['file']
     if file.filename == '':
         return jsonify({"error": "No file selected"}), 400
 
-    # Save the uploaded file
-    data_file_path = os.path.join(UPLOAD_FOLDER, file.filename)
-    file.save(data_file_path)
-    return jsonify({"message": "File uploaded successfully", "file_path": data_file_path})
+    if not allowed_file(file.filename):
+        return jsonify({"error": "Only .csv and .xlsx files are allowed"}), 400
+
+    try:
+        # Save the new file and overwrite the existing one
+        data_file_path = UPLOAD_FOLDER / file.filename
+        file.save(data_file_path)
+
+        # Cleanup old plots
+        for plot in UPLOAD_FOLDER.glob("*.html"):
+            plot.unlink()
+
+        app.logger.info(f"File uploaded successfully: {data_file_path}")
+        return jsonify({"message": "File uploaded successfully", "file_path": str(data_file_path)})
+    except Exception as e:
+        app.logger.error(f"File upload error: {e}")
+        return jsonify({"error": "Failed to upload file"}), 500
 
 
 @app.route('/forecast', methods=['POST'])
@@ -37,50 +69,53 @@ def forecast():
     data = request.get_json()
     item_id = data.get('item_id')
 
-    # Validate item_id
     if item_id is None:
         return jsonify({"error": "Item ID is required."}), 400
 
-    # Load data
-    df = load_data(data_file_path)
+    try:
+        # Load data
+        df = load_data(data_file_path)
 
-    # Check if item_id exists in the dataset
-    if item_id not in df['item_id'].values:
-        print(f"Item ID {item_id} not found in the dataset")
-        return jsonify({"error": f"Item ID {item_id} not found in the dataset."}), 404
+        # Validate item ID
+        if item_id not in df['item_id'].values:
+            app.logger.error(f"Item ID {item_id} not found in the dataset")
+            return jsonify({"error": f"Item ID {item_id} not found in the dataset."}), 404
 
-    # Make predictions
-    future_months, predicted_demand = predict_demand(df, item_id)
+        # Generate predictions
+        future_months, predicted_demand = predict_demand(df, item_id)
 
-    if predicted_demand is not None:
-        alerts = check_stock_and_alert(df, item_id, predicted_demand, future_months)
+        if predicted_demand is None:
+            return jsonify({"error": f"Could not generate forecasts for item ID {item_id}."}), 500
 
-        # Create and save Bokeh plots
+        # Generate alerts and plots
+        alerts = check_stock_and_alert(df, item_id, predicted_demand, future_months)
         plot_path = create_bokeh_plots(df, item_id, future_months, predicted_demand)
 
-        # Convert arrays to lists for JSON serialization
+        # Safely convert variables for JSON serialization
         response = {
-            "future_months": future_months.tolist() if hasattr(future_months, 'tolist') else future_months,
-            "predicted_demand": predicted_demand.tolist() if hasattr(predicted_demand, 'tolist') else predicted_demand,
+            "future_months": future_months if isinstance(future_months, list) else future_months.tolist(),
+            "predicted_demand": predicted_demand if isinstance(predicted_demand, list) else predicted_demand.tolist(),
             "alerts": alerts,
-            "plot_url": f"/plot/{item_id}"  # URL to view the plot
-        }
-    else:
-        response = {
-            "error": f"Could not generate forecasts for item ID {item_id}."
+            "plot_url": f"/plot/{item_id}"
         }
 
-    return jsonify(response)
+        app.logger.info(f"Forecast successfully generated for item ID {item_id}")
+        return jsonify(response)
+
+    except Exception as e:
+        app.logger.error(f"Error during forecasting: {e}")
+        return jsonify({"error": "An error occurred during forecasting. Please try again."}), 500
+
 
 
 @app.route('/plot/<item_id>')
 def plot(item_id):
-    plot_path = f"uploads/demand_forecast_{item_id}.html"
-    if os.path.exists(plot_path):
+    plot_path = UPLOAD_FOLDER / f"demand_forecast_{item_id}.html"
+    if plot_path.exists():
         return send_file(plot_path)
     else:
         return jsonify({"error": "Plot not found"}), 404
 
 
 if __name__ == "__main__":
-    app.run(host='0.0.0.0', port=5000, debug=True)
+    app.run(host='0.0.0.0', port=5000, debug=True)

demand_forecast/requirements.txt

@@ -1,9 +1,9 @@
 flask==2.3.2
 Werkzeug==2.3.3
-pandas==2.1.3
-numpy>=2.1.0
+pandas>=2.1,<2.2
+numpy>=1.21,<2.1
 statsmodels==0.14.0
 tensorflow>=2.14.0,<2.18.0
-scikit-learn==1.2.2
 bokeh==3.1.1
-
+flask-cors==3.0.10
+scikit-learn>=0.24.0,<1.3.0