Update app.py

app.py

@@ -1,68 +1,31 @@
-import streamlit as st  
-import pandas as pd  
-import matplotlib.pyplot as plt  
-from mpl_toolkits.mplot3d import Axes3D  
-  
-# Load CSV file  
-df = pd.read_csv('رشد و ارتفاع مزارع نماینده- هفته 20.csv')  
-  
-st.title("داشبورد اداره مزارع")  
-  
-# Department selection  
-department = st.selectbox("انتخاب اداره:", df['اداره'].unique())  
-  
-# Variety selection  
-variety = st.selectbox("انتخاب واریته:", df['واریته'].unique())  
-  
-# Filter farms based on selected department and variety  
-farms = df[(df['اداره'] == department) & (df['واریته'] == variety)]['مزرعه'].unique()  
-  
-# Farm selection  
-farm = st.selectbox("انتخاب مزرعه:", farms)  
-  
-# Display farm data  
-if farm:  
-   farm_data = df[df['مزرعه'] == farm]  
-   weekly_height = farm_data['ارتفاع مزرعه'].values  
-    
-   # Display table  
-   st.write("### ارتفاع هفتگی")  
-   st.table(pd.DataFrame({'هفته': range(1, len(weekly_height)+1), 'ارتفاع': weekly_height}))  
-    
-   # Display 3D histogram  
-   fig = plt.figure()  
-   ax = fig.add_subplot(111, projection='3d')  
-    
-   xpos = [i for i in range(1, len(weekly_height)+1)]  
-   ypos = [0] * len(weekly_height)  
-   zpos = [0] * len(weekly_height)  
-   dx = [1] * len(weekly_height)  
-   dy = [1] * len(weekly_height)  
-   dz = weekly_height  
-    
-   ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color='b', zsort='average')  
-    
-   ax.set_xlabel("هفته")  
-   ax.set_ylabel("Y")  
-   ax.set_zlabel("ارتفاع")  
-   st.pyplot(fig)  
-    
-   # Additional features  
-   st.write("### آمار مزرعه")  
-   st.write(f"میانگین ارتفاع: {weekly_height.mean():.2f}")  
-   st.write(f"بیشترین ارتفاع: {weekly_height.max():.2f}")  
-   st.write(f"کمترین ارتفاع: {weekly_height.min():.2f}")  
-    
-   st.write("### نمودار توزیع ارتفاع")  
-   fig, ax = plt.subplots()  
-   ax.hist(weekly_height, bins=10, color='b')  
-   ax.set_xlabel("ارتفاع")  
-   ax.set_ylabel("تعداد")  
-   st.pyplot(fig)  
-    
-   st.write("### نمودار توزیع ارتفاع بر حسب هفته")  
-   fig, ax = plt.subplots()  
-   ax.plot(range(1, len(weekly_height)+1), weekly_height, marker='o')  
-   ax.set_xlabel("هفته")  
-   ax.set_ylabel("ارتفاع")  
-   st.pyplot(fig)
+import ee
+import streamlit as st
+import pandas as pd
+
+# Authenticate 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) 
+
+# آپلود فایل مختصات
+uploaded_file = st.file_uploader("آپلود فایل مختصات مزارع", type="csv")
+if uploaded_file:
+    farms_df = pd.read_csv(uploaded_file)
+    
+    # پردازش اطلاعات هر مزرعه
+    for idx, row in farms_df.iterrows():
+        lat, lon = row['latitude'], row['longitude']
+        location = ee.Geometry.Point(lon, lat)
+        
+        # دریافت تصویر ماهواره‌ای و محاسبه NDVI
+        image = ee.ImageCollection('COPERNICUS/S2') \
+                  .filterBounds(location) \
+                  .filterDate('2023-01-01', '2023-12-31') \
+                  .mean()
+        ndvi = image.normalizedDifference(['B8', 'B4']).reduceRegion(
+            reducer=ee.Reducer.mean(),
+            geometry=location,
+            scale=30
+        )
+        
+        st.write(f"شاخص NDVI برای مزرعه {idx}: {ndvi.getInfo()['nd']}")