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import streamlit as st
import pandas as pd
import joblib
import ee
import geemap
# Earth Engine Authentication (Replace with your actual authentication)
# اعتبار سنجی و اتصال به Google Earth Engine
service_account = 'earth-engine-service-account@ee-esmaeilkiani1387.iam.gserviceaccount.com'
credentials = ee.ServiceAccountCredentials(service_account, 'ee-esmaeilkiani1387-1b2c5e812a1d.json')
ee.Initialize(credentials)
# Load pre-trained model
model = joblib.load('updated_model.pkl') # Replace 'updated_model.pkl' with your actual model file
# Load farm data
farm_data = pd.read_csv('Farm_NDRE_TimeSeries.csv') # Replace 'Farm_NDRE_TimeSeries.csv' with your actual data file
farm_names = farm_data['Farm_Name'].tolist()
# Function to calculate NDRE
def calculate_ndre(coordinates, start_date, end_date):
try:
# Define the Earth Engine region of interest (ROI)
roi = ee.Geometry.Point(coordinates)
# Define the image collection (replace with your actual collection ID and bands)
imageCollection = ee.ImageCollection('COPERNICUS/S2_SR') \
.filterBounds(roi) \
.filterDate(start_date, end_date) \
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)) #Cloud filtering
# Function to compute NDRE
def ndre(image):
red_edge = image.select('B8A')
red = image.select('B4')
return image.addBands(red_edge.subtract(red).divide(red_edge.add(red)).rename('NDRE'))
#Apply NDRE to all images in collection, and reduce to median
ndre_image = imageCollection.map(ndre).median().select('NDRE')
# Get NDRE value at the point
ndre_value = ndre_image.reduceRegion(
reducer=ee.Reducer.first(),
geometry=roi,
scale=10
).getInfo()
ndre_value = ndre_value.get('NDRE') #Extract from dictionary
return ndre_value
except Exception as e:
st.error(f"Error calculating NDRE: {e}")
return None
# Streamlit UI
st.title("Farm Parameter Prediction App")
# User input
selected_farm = st.selectbox("Select Farm", farm_names)
farm_age = st.number_input("Farm Age (years)", min_value=0)
farm_variety = st.text_input("Farm Variety")
start_date = st.date_input("Start Date")
end_date = st.date_input("End Date")
#Find Coordinates based on Farm Name Selection
selected_farm_data = farm_data[farm_data['Farm'] == selected_farm]
coordinates = (selected_farm_data['longitude'].iloc[0], selected_farm_data['latitude'].iloc[0])
if st.button("Calculate NDRE and Show Map"):
ndre_value = calculate_ndre(coordinates, start_date.strftime("%Y-%m-%d"), end_date.strftime("%Y-%m-%d"))
if ndre_value is not None:
st.write(f"NDRE Value: {ndre_value}")
# Map Display (Replace with your actual map display logic)
Map = geemap.Map()
Map.centerObject(ee.Geometry.Point(coordinates), 12)
# Add NDRE layer
#This part requires more detailed specification of the image and display parameters.
#Example (Adapt according to your NDRE image and color scheme)
#vis_params = {'min': 0, 'max': 1, 'palette': ['blue', 'green', 'yellow', 'red']}
#Map.addLayer(ndre_image, vis_params, 'NDRE')
#Display Map
Map.to_streamlit(height=500)
if st.button("Predict"):
user_input = pd.DataFrame({
'Farm_Name': [selected_farm],
'Farm_Age': [farm_age],
'Farm_Variety': [farm_variety],
'NDRE': [ndre_value] if ndre_value is not None else [0] #Handle cases where ndre_value is None. Replace 0 with a more suitable default if needed.
})
# Feature Engineering might be needed here depending on your model's input features
prediction = model.predict(user_input)
st.write("Predictions:")
st.write(f"Brix: {prediction[0][0]}") #Assuming model outputs a list of lists
st.write(f"Pol: {prediction[0][1]}")
st.write(f"Purity: {prediction[0][2]}")
st.write(f"RS: {prediction[0][3]}")
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