Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Thu Jun  1 13:52:42 2023
+
+@author: ME
+"""
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import streamlit as st
+import seaborn as sns
+import pickle
+import base64
+import joblib
+from src.preprocess import preprocess_data
+import mplcursors
+
+
+#load json file
+json_path = r"C:/Users/ME/Desktop/Blessing_AI/Weather_Prediction/Artifacts/feature_dict.joblib"
+loaded_data = joblib.load(json_path)
+
+#load model
+model_path = r"C:/Users/ME/Desktop/Blessing_AI/Weather_Prediction/Artifacts/trained_prophet_model.pkl"
+with open(model_path, 'rb') as file_:
+    model = pickle.load(file_)
+    
+
+st.title("Weather Forecast: Average Daily Temperature Projections for Nigerian Airports")
+
+col1,col2 = st.columns(2)
+
+start_d = col1.date_input("Select start date")
+end_d =  col2.date_input("Select end date")
+#get number of days
+delta = end_d - start_d
+days = delta.days
+
+#split start date into year ,month and date 
+y1 = start_d.year
+m1 = start_d.month
+d1  = start_d.day
+
+#split end date into year ,month and date
+y2 = end_d.year
+m2 = end_d.month
+d2  = end_d.day
+
+start_date = (y1,m1,d1)
+end_date = (y2,m2,d2)
+
+if days > 10 :
+    st.write("Select a lower number of days range (10 and below)")
+    
+else:
+    
+    name = st.selectbox("Select airport of interest",options=tuple(loaded_data["Airport_name"].keys()))
+
+    data = preprocess_data(
+        start_d=start_date,
+        end_d= end_date,
+       airport_name = name
+        )
+    
+    forecast_bn = st.button("Forecast")
+    
+    if forecast_bn:
+        pred_df = model.predict(data)
+        pred_df = pred_df[["ds","yhat"]]
+        pred_df.columns = ["Date","Prediction"]
+        
+        #Display only year,month and date
+        pred_df["Date"]= pred_df['Date'].dt.strftime('%Y-%m-%d')
+        
+        #Convert column to 2 dp
+        pred_df["Prediction"] = pred_df["Prediction"].round(2)
+        
+        tab1,tab2 = st.tabs(["Predictions","Check plot"])
+        tab1.write(pred_df)
+        
+        # Add a download button
+        csv = pred_df.to_csv(index=False)
+        b64 = base64.b64encode(csv.encode()).decode()  # Encode the DataFrame as a base64 string
+        href = f'<a href="data:file/csv;base64,{b64}" download="data.csv">Download CSV File</a>'
+        st.markdown(href, unsafe_allow_html=True)
+                
+        
+        # Create the figure and axis objects
+        fig, ax = plt.subplots()
+        
+        # Plot the time series
+        x = pred_df["Date"]
+        y = pred_df["Prediction"]
+        ax.plot(x, y, marker='o', linestyle='--')
+        
+        # Set x-axis label
+        ax.set_xlabel('Date')
+        
+        # Set y-axis label
+        ax.set_ylabel('Average temperature')
+        
+        # Style the plot
+        sns.set_style('whitegrid')
+        
+        # Set the limits of the x-axis and y-axis
+        ax.set_xlim(min(x), max(x))
+        ax.set_ylim(min(y), max(y))
+        
+        # Format the x-axis date ticks
+        fig.autofmt_xdate()
+        
+        # Add cursor interaction
+        cursor = mplcursors.cursor(hover=True)
+        # Define the annotation format
+        @cursor.connect("add")
+        def on_add(sel):
+            x = sel.target[0]
+            y = sel.target[1]
+            date_str = x.strftime("%Y-%m-%d")
+            sel.annotation.set_text(f'Date: {date_str}\nValue: {y:.2f}')
+        
+        # Show the plot
+        tab2.pyplot(fig)
+    
+    
+
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