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| import gradio as gr | |
| import pandas as pd | |
| from prophet import Prophet | |
| import plotly.graph_objs as go | |
| import numpy as np | |
| # Function to replace null values with average | |
| def replace_null_with_avg_values(df, column_name): | |
| df[column_name] = pd.to_numeric(df[column_name], errors='coerce') | |
| avg_value = round(df[column_name].mean(), 1) | |
| df[column_name].fillna(avg_value, inplace=True) | |
| # Load and process the data | |
| broiler_data = pd.read_csv('Broiler market price.csv') | |
| replace_null_with_avg_values(broiler_data, 'DOC') | |
| replace_null_with_avg_values(broiler_data, 'Farm Rate') | |
| replace_null_with_avg_values(broiler_data, 'Open') | |
| replace_null_with_avg_values(broiler_data, 'Close') | |
| broiler_data['Date'] = pd.to_datetime(broiler_data['Date']) | |
| # Prepare dataframes for Prophet models | |
| farm_rate_df = broiler_data[['Date', 'Farm Rate', 'DOC']].rename(columns={'Date': 'ds', 'Farm Rate': 'y', 'DOC': 'DOC'}) | |
| open_rate_df = broiler_data[['Date', 'Open', 'DOC']].rename(columns={'Date': 'ds', 'Open': 'y', 'DOC': 'DOC'}) | |
| close_rate_df = broiler_data[['Date', 'Close', 'DOC']].rename(columns={'Date': 'ds', 'Close': 'y', 'DOC': 'DOC'}) | |
| # Initialize Prophet models | |
| farm_rate_model = Prophet(growth='linear', yearly_seasonality=True, daily_seasonality=True) | |
| open_rate_model = Prophet(growth='linear', yearly_seasonality=True, daily_seasonality=True) | |
| close_rate_model = Prophet(growth='linear', yearly_seasonality=True, daily_seasonality=True) | |
| # Add DOC regressor and holidays | |
| farm_rate_model.add_regressor('DOC') | |
| open_rate_model.add_regressor('DOC') | |
| close_rate_model.add_regressor('DOC') | |
| farm_rate_model.add_country_holidays(country_name='PAK') | |
| open_rate_model.add_country_holidays(country_name='PAK') | |
| close_rate_model.add_country_holidays(country_name='PAK') | |
| # Fit the models | |
| farm_rate_model.fit(farm_rate_df) | |
| open_rate_model.fit(open_rate_df) | |
| close_rate_model.fit(close_rate_df) | |
| # Function to generate predictions based on DOC range and selected days | |
| def predict_values(start_doc, end_doc, days_option): | |
| # Generate future dataframes | |
| farm_future_rate = farm_rate_model.make_future_dataframe(periods=days_option, freq='D') | |
| open_future_rate = open_rate_model.make_future_dataframe(periods=days_option, freq='D') | |
| close_future_rate = close_rate_model.make_future_dataframe(periods=days_option, freq='D') | |
| # Generate DOC values within the specified range | |
| doc_values = np.linspace(start_doc, end_doc, num=days_option) # Adjusted line | |
| # Assign the generated DOC values to the future dataframes | |
| farm_future_rate['DOC'] = doc_values | |
| open_future_rate['DOC'] = doc_values | |
| close_future_rate['DOC'] = doc_values | |
| # Make predictions | |
| farm_forecast_rate = farm_rate_model.predict(farm_future_rate) | |
| open_forecast_rate = open_rate_model.predict(open_future_rate) | |
| close_forecast_rate = close_rate_model.predict(close_future_rate) | |
| # Filter to get only the future predictions (last `days_option` days) | |
| farm_output = farm_forecast_rate[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(days_option).copy() | |
| farm_output['DOC'] = farm_future_rate['DOC'].tail(days_option) | |
| open_output = open_forecast_rate[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(days_option).copy() | |
| open_output['DOC'] = open_future_rate['DOC'].tail(days_option) | |
| close_output = close_forecast_rate[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(days_option).copy() | |
| close_output['DOC'] = close_future_rate['DOC'].tail(days_option) | |
| # Round float values to two decimal places | |
| farm_output[['yhat', 'yhat_lower', 'yhat_upper']] = farm_output[['yhat', 'yhat_lower', 'yhat_upper']].round(2) | |
| open_output[['yhat', 'yhat_lower', 'yhat_upper']] = open_output[['yhat', 'yhat_lower', 'yhat_upper']].round(2) | |
| close_output[['yhat', 'yhat_lower', 'yhat_upper']] = close_output[['yhat', 'yhat_lower', 'yhat_upper']].round(2) | |
| # Create Plotly graphs | |
| fig_farm = go.Figure() | |
| fig_farm.add_trace(go.Scatter(x=farm_forecast_rate['ds'], y=farm_forecast_rate['yhat'], mode='lines+markers', name='Farm Rate Prediction')) | |
| fig_farm.update_layout(title='Farm Rate Predictions over Time', xaxis_title='Date', yaxis_title='Predicted Farm Rate') | |
| fig_open = go.Figure() | |
| fig_open.add_trace(go.Scatter(x=open_forecast_rate['ds'], y=open_forecast_rate['yhat'], mode='lines+markers', name='Open Rate Prediction')) | |
| fig_open.update_layout(title='Open Rate Predictions over Time', xaxis_title='Date', yaxis_title='Predicted Open Rate') | |
| fig_close = go.Figure() | |
| fig_close.add_trace(go.Scatter(x=close_forecast_rate['ds'], y=close_forecast_rate['yhat'], mode='lines+markers', name='Close Rate Prediction')) | |
| fig_close.update_layout(title='Close Rate Predictions over Time', xaxis_title='Date', yaxis_title='Predicted Close Rate') | |
| return farm_output, open_output, close_output, fig_farm, fig_open, fig_close | |
| # Define Gradio interface | |
| def interface(start_doc, end_doc, days_option): | |
| # Check if a valid selection is made | |
| if days_option is None: | |
| return "Please select a valid option for days.", None, None, None, None, None | |
| days_map = {'7 days': 7, '10 days': 10, '15 days': 15,'40 days':40} | |
| days_selected = days_map.get(days_option, 7) # Default to 7 days if no valid option is provided | |
| results_farm, results_open, results_close, plot_farm, plot_open, plot_close = predict_values(start_doc, end_doc, days_selected) | |
| return results_farm, results_open, results_close, plot_farm, plot_open, plot_close | |
| # Create Gradio inputs and outputs | |
| start_doc_input = gr.components.Number(label="Start DOC Value") | |
| end_doc_input = gr.components.Number(label="End DOC Value") | |
| days_dropdown = gr.components.Dropdown(choices=['7 days', '10 days', '15 days','40 days'], label="Select Number of Days",value='7 days') | |
| # Define output components | |
| output_table_farm = gr.components.Dataframe(label="Predicted Farm Rate Values") | |
| output_table_open = gr.components.Dataframe(label="Predicted Open Rate Values") | |
| output_table_close = gr.components.Dataframe(label="Predicted Close Rate Values") | |
| output_plot_farm = gr.components.Plot(label="Farm Rate Predictions") | |
| output_plot_open = gr.components.Plot(label="Open Rate Predictions") | |
| output_plot_close = gr.components.Plot(label="Close Rate Predictions") | |
| # Set up Gradio interface | |
| gr.Interface( | |
| fn=interface, | |
| inputs=[start_doc_input, end_doc_input, days_dropdown], | |
| outputs=[output_table_farm, output_table_open, output_table_close, output_plot_farm, output_plot_open, output_plot_close], | |
| title="Farm Rate Prediction Tool", | |
| description="Enter DOC range and select the number of days to generate predictions and plot." | |
| ).launch(debug=True) | |