Update app.py

app.py

@@ -1,125 +1,110 @@
-import streamlit as st
-import ee
-import geemap.foliumap as geemap
-import pandas as pd
-import datetime
-import matplotlib.pyplot as plt
-import geopandas as gpd
-import zipfile
-import os
-import requests
+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)
-
-# خواندن فایل CSV مزارع
-farms_data = pd.read_csv('Farm_Details_Export.csv')
-
-# تعریف ناحیه مورد مطالعه با مختصات جدید
-region = ee.Geometry.Polygon(
-    [[[48.681879, 31.417603], [48.721447, 31.413209], [48.724279, 31.420826], [48.726768, 31.427418],
-      [48.728228, 31.435694], [48.736382, 31.42837], [48.739557, 31.435657], [48.742261, 31.441772],
-      [48.752303, 31.452243], [48.75226, 31.459784], [48.759127, 31.473657], [48.766809, 31.472413],
-      [48.773203, 31.491188], [48.77758, 31.534579], [48.785563, 31.540797], [48.792601, 31.59696],
-      [48.694668, 31.60756], [48.691921, 31.603466], [48.697586, 31.534067], [48.69381, 31.507727],
-      [48.685226, 31.468496], [48.681879, 31.417603]]]
-)
-
-# تابع برای دانلود و استخراج شیپ‌فایل
-def download_and_extract_shapefile(zip_url, extract_to='.'):
-    zip_path = os.path.join(extract_to, 'shapefile.zip')
-    
-    # دانلود فایل زیپ
-    with requests.get(zip_url, stream=True) as r:
-        r.raise_for_status()
-        with open(zip_path, 'wb') as f:
-            for chunk in r.iter_content(chunk_size=8192):
-                f.write(chunk)
-    
-    # استخراج فایل زیپ
-    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
-        zip_ref.extractall(extract_to)
-
-    # یافتن فایل .shp
-    for file in os.listdir(extract_to):
-        if file.endswith('.shp'):
-            return os.path.join(extract_to, file)
-
-    return None
-
-# تابع برای خواندن و نمایش شیپ‌فایل
-def load_and_display_shapefile(shapefile_path):
-    gdf = gpd.read_file(shapefile_path)
-    geojson = gdf.to_json()
-    
-    map_ = geemap.Map(center=[31.5, 48.7], zoom=10)
-    map_.add_geojson(geojson, layer_name="Shapefile Layer")
-    map_.to_streamlit(height=600)
-
-# آدرس شیپ‌فایل آپلود شده در هاگینگ فیس (لینک مستقیم فایل زیپ)
-zip_url = 'https://huggingface.co/spaces/Esmaeilkiani/AppSugarcane/resolve/main/Dehkhodaa.rar'
-
-# دانلود و نمایش شیپ‌فایل
-shapefile_path = download_and_extract_shapefile(zip_url)
-if shapefile_path:
-    st.write("شیپ‌فایل با موفقیت بارگذاری شد!")
-    load_and_display_shapefile(shapefile_path)
-else:
-    st.write("خطا در بارگذاری شیپ‌فایل.")
-
-# انتخاب بازه زمانی با کلیدهای یکتا
-start_date = st.date_input("تاریخ شروع", datetime.date(2023, 1, 1), key="start_date")
-end_date = st.date_input("تاریخ پایان", datetime.date(2023, 12, 31), key="end_date")
-
-# انتخاب شاخص
-index_option = st.selectbox("شاخص مورد نظر را انتخاب کنید:", ["NDVI", "NDMI", "NDRE"])
-
-# انتخاب مزرعه از فایل CSV
-farm_name = st.sidebar.selectbox("نام مزرعه را انتخاب کنید:", farms_data['farm_name'].unique())
-
-# پیدا کردن مختصات مزرعه انتخاب شده
-selected_farm = farms_data[farms_data['farm_name'] == farm_name]
-latitude = selected_farm['latitude'].values[0]
-longitude = selected_farm['longitude'].values[0]
-farm_age = selected_farm['age'].values[0]
-farm_variety = selected_farm['variety'].values[0]
-
-# دکمه برای نمایش نقشه
-if st.button("نمایش نقشه"):
-    if index_option == "NDVI":
-        index_map, vis_params = get_ndvi_map(start_date.isoformat(), end_date.isoformat())
-    elif index_option == "NDMI":
-        index_map, vis_params = get_ndmi_map(start_date.isoformat(), end_date.isoformat())
-    else:
-        index_map, vis_params = get_ndre_map(start_date.isoformat(), end_date.isoformat())
-
-    # ایجاد نقشه با Geemap
-    map_ = geemap.Map(center=[latitude, longitude], zoom=14)
-    map_.addLayer(index_map.clip(region), vis_params, index_option)
-    map_.add_colorbar(vis_params, label=index_option)
-
-    # افزودن مزرعه به نقشه
-    map_.add_marker([latitude, longitude], popup=f"نام: {farm_name}<br>سن: {farm_age}<br>واریته: {farm_variety}")
-    map_.add_ee_layer(region, {'color': 'FF0000'}, 'منطقه مورد مطالعه')
-
-    # نمایش نقشه در Streamlit
-    map_.to_streamlit(height=600)
-
-    # محاسبه NDRE میانگین
-    ndre_image = get_ndre_map(start_date.isoformat(), end_date.isoformat())[0]
-    mean_ndre = ndre_image.reduceRegion(reducer=ee.Reducer.mean(), geometry=region, scale=30).get('NDRE').getInfo()
-
-    # نمودار تصمیم‌گیری برای برداشت
-    fig, ax = plt.subplots()
-    ax.bar(['NDRE', 'مناسب برای برداشت'], [mean_ndre, 0.5], color=['green', 'orange'])
-    ax.set_ylim(0, 1)
-    ax.set_title(f"شاخص NDRE برای مزرعه {farm_name}")
-    st.pyplot(fig)
-
-    # نمایش وضعیت برداشت
-    if mean_ndre > 0.5:
-        st.write(f"مزرعه {farm_name} به حد قابل قبول برای برداشت رسیده است (NDRE = {mean_ndre:.2f}).")
-    else:
-        st.write(f"مزرعه {farm_name} هنوز آماده برداشت نیست (NDRE = {mean_ndre:.2f}).")
+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]}")
+
+
+