Create app.py

app.py

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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=len(farm_future_rate))  # 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)
+
+    # 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}
+    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'], 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)