Upload 3 files

app.py

@@ -0,0 +1,56 @@
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Load the dataset
+data = pd.read_csv("ProductInfo.csv")  # Replace with your actual dataset
+
+# Title and description
+st.title("Demand Forecasting")
+st.write("Demand Overview for Top 10 Products")
+
+# Sample dropdown for stock codes or product codes
+stock_codes = data['StockCode'].unique()
+selected_stock = st.selectbox("Select a Stock Code:", stock_codes)
+
+# Filter data for selected stock code
+filtered_data = data[data['StockCode'] == selected_stock]
+
+# Generate dummy data for the forecast and actual values
+# Replace this with your actual forecasting model's output
+dates = pd.date_range(start='2023-01-01', periods=len(filtered_data), freq='W')
+actual_demand = np.random.randint(50, 150, size=len(filtered_data))  # Replace with actual demand data
+predicted_demand_train = actual_demand + np.random.normal(0, 10, size=len(filtered_data))  # Replace with your model's predictions
+predicted_demand_test = actual_demand + np.random.normal(0, 15, size=len(filtered_data))  # Replace with your test predictions
+
+# Line chart: Actual vs Predicted Demand
+st.write(f"Demand Overview for {selected_stock}")
+fig, ax = plt.subplots(figsize=(10, 6))
+ax.plot(dates, actual_demand, label='Actual Demand', color='blue', marker='o')
+ax.plot(dates, predicted_demand_train, label='Train Predicted Demand', color='green', linestyle='--', marker='x')
+ax.plot(dates, predicted_demand_test, label='Test Predicted Demand', color='red', linestyle='--', marker='x')
+ax.legend()
+ax.set_xlabel("Date")
+ax.set_ylabel("Demand")
+st.pyplot(fig)
+
+# Histograms for error distributions
+train_error = actual_demand - predicted_demand_train
+test_error = actual_demand - predicted_demand_test
+
+# Training Error Distribution
+st.write("Training Error Distribution")
+fig, ax = plt.subplots(figsize=(6, 4))
+ax.hist(train_error, bins=20, color='green', alpha=0.7)
+ax.set_xlabel("Error")
+ax.set_ylabel("Frequency")
+st.pyplot(fig)
+
+# Testing Error Distribution
+st.write("Testing Error Distribution")
+fig, ax = plt.subplots(figsize=(6, 4))
+ax.hist(test_error, bins=20, color='red', alpha=0.7)
+ax.set_xlabel("Error")
+ax.set_ylabel("Frequency")
+st.pyplot(fig)

requirements.txt

@@ -0,0 +1,118 @@
+<<<<<<< HEAD
+absl-py==2.1.0
+altair==5.4.1
+asttokens==2.4.1
+astunparse==1.6.3
+attrs==24.2.0
+blinker==1.8.2
+cachetools==5.5.0
+certifi==2024.8.30
+charset-normalizer==3.3.2
+click==8.1.7
+cmdstanpy==1.2.4
+colorama==0.4.6
+comm==0.2.1
+contourpy==1.3.0
+cycler==0.12.1
+debugpy==1.8.1
+decorator==5.1.1
+executing==2.0.1
+flatbuffers==24.3.25
+fonttools==4.54.1
+gast==0.6.0
+gitdb==4.0.11
+GitPython==3.1.43
+google-pasta==0.2.0
+greenlet==3.1.1
+grpcio==1.66.2
+h5py==3.12.1
+holidays==0.57
+idna==3.10
+importlib_resources==6.4.5
+ipykernel==6.29.2
+ipython==8.21.0
+jedi==0.19.1
+Jinja2==3.1.4
+joblib==1.4.2
+jsonschema==4.23.0
+jsonschema-specifications==2023.12.1
+jupyter_client==8.6.0
+jupyter_core==5.7.1
+keras==3.6.0
+kiwisolver==1.4.7
+libclang==18.1.1
+Markdown==3.7
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.9.2
+matplotlib-inline==0.1.6
+mdurl==0.1.2
+ml-dtypes==0.4.1
+namex==0.0.8
+narwhals==1.9.0
+nest-asyncio==1.6.0
+numpy==1.26.4
+opt_einsum==3.4.0
+optree==0.13.0
+packaging==23.2
+pandas==2.2.3
+parso==0.8.3
+patsy==0.5.6
+pillow==10.4.0
+platformdirs==4.2.0
+plotly==5.24.1
+prompt-toolkit==3.0.43
+prophet==1.1.6
+protobuf==4.25.5
+psutil==5.9.8
+pure-eval==0.2.2
+pyarrow==17.0.0
+pydeck==0.9.1
+Pygments==2.17.2
+pyparsing==3.1.4
+python-dateutil==2.9.0.post0
+pytz==2024.2
+pywin32==306
+pyzmq==25.1.2
+referencing==0.35.1
+requests==2.32.3
+rich==13.9.1
+rpds-py==0.20.0
+scikit-learn==1.5.2
+scipy==1.14.1
+seaborn==0.13.2
+setuptools==75.1.0
+six==1.16.0
+smmap==5.0.1
+SQLAlchemy==2.0.35
+stack-data==0.6.3
+stanio==0.5.1
+statsmodels==0.14.4
+streamlit==1.39.0
+tenacity==9.0.0
+tensorboard==2.17.1
+tensorboard-data-server==0.7.2
+tensorflow==2.17.0
+tensorflow-intel==2.17.0
+termcolor==2.4.0
+threadpoolctl==3.5.0
+toml==0.10.2
+tornado==6.4
+tqdm==4.66.5
+traitlets==5.14.1
+typing_extensions==4.12.2
+tzdata==2024.2
+urllib3==2.2.3
+watchdog==5.0.3
+wcwidth==0.2.13
+Werkzeug==3.0.4
+wheel==0.44.0
+wrapt==1.16.0
+xgboost==2.1.1
+=======
+streamlit
+pandas
+numpy
+matplotlib
+# Add any other necessary packages
+>>>>>>> d44e10b2923e9d56ecc218922b09c19d73c02d18