Update app.py

app.py

@@ -1,183 +1,231 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
-import plotly.express as px
-import plotly.graph_objects as go
-from datetime import datetime, timedelta
+import folium
+from streamlit_folium import folium_static
+import ee
 import requests
 import json
-import ee
-import pydeck as pdk
-from geopy.geocoders import Nominatim
-import matplotlib.pyplot as plt
+from datetime import datetime, timedelta
+import plotly.express as px
+import plotly.graph_objects as go
+from PIL import Image
+import io
+import base64
 from reportlab.pdfgen import canvas
 from reportlab.lib.pagesizes import letter
-import io
+import os
 
+# Page config
 st.set_page_config(
-    page_title="Dehkoda Sugar Field Monitoring",
+    page_title="مانیتورینگ مزارع نیشکر دهخدا",
+    page_icon="🌾",
     layout="wide",
     initial_sidebar_state="expanded"
 )
 
-# Apply dark theme
-def set_dark_theme():
-    st.markdown("""
+# Apply custom CSS for dark theme
+st.markdown("""
     <style>
-    .stApp {
-        background-color: #1a1a1a;
-        color: #ffffff;
-    }
-    .css-1d391kg, .st-bb, .st-at, .st-ax {
-        background-color: #2d2d2d !important;
-    }
+        .reportview-container {
+            background: #0e1117;
+            color: #fafafa;
+        }
+        .sidebar .sidebar-content {
+            background: #262730;
+        }
+        .Widget>label {
+            color: #fafafa;
+        }
+        .stButton>button {
+            background-color: #4CAF50;
+            color: white;
+        }
+        .stProgress .st-bo {
+            background-color: #4CAF50;
+        }
     </style>
-    """, unsafe_allow_html=True)
-
-set_dark_theme()
-
-# Google Earth Engine Authentication
-service_account = "esmaeil-kiani1387-gmail-com@ee-esmaeilkiani13877.iam.gserviceaccount.com"
-credentials = ee.ServiceAccountCredentials(service_account, 'ee-esmaeilkiani13877-9a054809a4bb.json')
-ee.Initialize(credentials)
-
-# API Keys
-OPENWEATHER_API = "ed47316a45379e2221a75f813229fb46"
-WINDY_API = "ohZom14Dr9JPKOaeUoY8nBxKdiK2yURW"
+""", unsafe_allow_html=True)
+
+# Initialize Earth Engine
+def initialize_ee():
+    try:
+        ee.Initialize()
+    except:
+        credentials = ee.ServiceAccountCredentials(
+            'esmaeil-kiani1387-gmail-com@ee-esmaeilkiani13877.iam.gserviceaccount.com',
+            'ee-esmaeilkiani13877-9a054809a4bb.json'
+        )
+        ee.Initialize(credentials)
 
+# Load farm data
 @st.cache_data
 def load_farm_data():
-    return pd.read_csv("tableConvert.com_wftamx (1).csv")
-
-df_farms = load_farm_data()
-
-def get_weather(lat, lon):
-    url = f"https://api.openweathermap.org/data/2.5/weather?lat={lat}&lon={lon}&appid={OPENWEATHER_API}&units=metric"
-    response = requests.get(url).json()
-    return {
-        'temp': response['main']['temp'],
-        'humidity': response['main']['humidity'],
-        'wind_speed': response['wind']['speed'],
-        'wind_deg': response['wind']['deg'],
-        'weather': response['weather'][0]['main']
-    }
-
-def fetch_ndvi_data(field_geom, start_date, end_date):
-    collection = ee.ImageCollection('COPERNICUS/S2') \
-        .filterBounds(field_geom) \
-        .filterDate(start_date, end_date)
+    df = pd.read_csv('tableConvert.com_wftamx (1).csv')
+    return df
+
+# NDVI calculation function
+def calculate_ndvi(farm_geometry, start_date, end_date):
+    collection = ee.ImageCollection('COPERNICUS/S2_SR') \
+        .filterBounds(farm_geometry) \
+        .filterDate(start_date, end_date) \
+        .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))
     
-    def calculate_ndvi(image):
+    def addNDVI(image):
         ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI')
         return image.addBands(ndvi)
     
-    ndvi_collection = collection.map(calculate_ndvi)
-    return ndvi_collection.select('NDVI')
-
-with st.sidebar:
-    st.title("Field Explorer")
-    search_query = st.text_input("Search fields")
-    filtered_farms = df_farms[df_farms['name'].str.contains(search_query, case=False)]
-    selected_farm = st.selectbox("Select Farm", filtered_farms['name'])
-    selected_data = df_farms[df_farms['name'] == selected_farm].iloc[0]
+    ndvi_collection = collection.map(addNDVI)
+    return ndvi_collection
+
+# Weather data fetching
+def get_weather_data(lat, lon, api_key):
+    base_url = "http://api.openweathermap.org/data/2.5/weather"
+    params = {
+        "lat": lat,
+        "lon": lon,
+        "appid": api_key,
+        "units": "metric"
+    }
+    response = requests.get(base_url, params=params)
+    return response.json()
+
+# Windy.com API integration
+def get_windy_forecast(lat, lon, api_key):
+    base_url = "https://api.windy.com/api/point-forecast/v2"
+    headers = {
+        "Authorization": f"Bearer {api_key}"
+    }
+    params = {
+        "lat": lat,
+        "lon": lon,
+        "model": "gfs",
+        "parameters": ["wind", "temp", "humidity"]
+    }
+    response = requests.get(base_url, headers=headers, params=params)
+    return response.json()
 
-# Right Sidebar - Weather & Info
-with st.expander("Farm Details", expanded=True):
-    st.header(selected_data['name'])
-    st.subheader("Vital Statistics")
-    st.metric("Age", f"{selected_data['age']} years")
-    st.metric("Variety", selected_data['variety'])
+# Main function
+def main():
+    # Initialize Earth Engine
+    initialize_ee()
     
-    # Weather Data
-    weather = get_weather(selected_data['latitude'], selected_data['longitude'])
-    st.subheader("Current Weather")
-    cols = st.columns(2)
-    cols[0].metric("Temperature", f"{weather['temp']}°C")
-    cols[1].metric("Humidity", f"{weather['humidity']}%")
-    cols[0].metric("Wind Speed", f"{weather['wind_speed']} m/s")
-    cols[1].metric("Condition", weather['weather'])
-
-st.title(f"Field Monitoring Dashboard - {selected_data['name']}")
-
-# Temperature & NDVI Chart
-st.subheader("Temperature & NDVI Analysis")
-date_range = st.date_input("Select Date Range", 
-                          [datetime.today() - timedelta(days=7), datetime.today()])
-
-# Generate sample temperature data
-dates = pd.date_range(date_range[0], date_range[1])
-temps = np.random.uniform(low=15, high=35, size=len(dates))
-ndvi = np.random.uniform(low=0.2, high=0.8, size=len(dates))
-
-fig = go.Figure()
-fig.add_trace(go.Scatter(x=dates, y=temps, name='Temperature'))
-fig.add_trace(go.Scatter(x=dates, y=ndvi, name='NDVI', yaxis='y2'))
-fig.update_layout(
-    yaxis=dict(title="Temperature (°C)"),
-    yaxis2=dict(title="NDVI", overlaying='y', side='right'),
-    template="plotly_dark"
-)
-st.plotly_chart(fig, use_container_width=True)
-
-# NDVI Map
-st.subheader("Field Health Analysis")
-field_geom = ee.Geometry.Point([selected_data['longitude'], selected_data['latitude']]).buffer(500)
-ndvi_data = fetch_ndvi_data(field_geom, str(date_range[0]), str(date_range[1]))
-mean_ndvi = ndvi_data.mean().getInfo()['bands'][0]['stats']['mean']
-
-st.pydeck_chart(pdk.Deck(
-    map_style='mapbox://styles/mapbox/dark-v10',
-    initial_view_state=pdk.ViewState(
-        latitude=selected_data['latitude'],
-        longitude=selected_data['longitude'],
-        zoom=12,
-        pitch=50,
-    ),
-    layers=[
-        pdk.Layer(
-            'ScatterplotLayer',
-            data=pd.DataFrame({
-                'lat': [selected_data['latitude']],
-                'lon': [selected_data['longitude']],
-                'color': [[0, 255, 0] if mean_ndvi > 0.6 else [255, 255, 0] if mean_ndvi > 0.3 else [255, 0, 0]]
-            }),
-            get_position='[lon, lat]',
-            get_color='color',
-            get_radius=200,
+    # Load farm data
+    farms_df = load_farm_data()
+    
+    # Sidebar
+    st.sidebar.title("مدیریت مزارع")
+    
+    # Farm search
+    search_query = st.sidebar.text_input("جستجوی مزرعه", "")
+    filtered_farms = farms_df[farms_df['name'].str.contains(search_query, case=False, na=False)]
+    
+    # Farm selection
+    selected_farm = st.sidebar.selectbox(
+        "انتخاب مزرعه",
+        filtered_farms['name'].tolist()
+    )
+    
+    # Main content area
+    col1, col2 = st.columns([2, 1])
+    
+    with col1:
+        st.title(f"داشبورد مزرعه {selected_farm}")
+        
+        # Map
+        farm_data = farms_df[farms_df['name'] == selected_farm].iloc[0]
+        m = folium.Map(
+            location=[farm_data['latitude'], farm_data['longitude']],
+            zoom_start=15
         )
-    ]
-))
-
-# --------------------------
-# Report Generation
-# --------------------------
-def create_pdf_report():
+        
+        # Add farm marker
+        folium.Marker(
+            [farm_data['latitude'], farm_data['longitude']],
+            popup=f"مزرعه {selected_farm}\nسن: {farm_data['age']}\nواریته: {farm_data['variety']}"
+        ).add_to(m)
+        
+        folium_static(m)
+        
+        # NDVI Analysis
+        st.subheader("تحلیل NDVI")
+        date_range = st.date_input(
+            "بازه زمانی",
+            [datetime.now() - timedelta(days=30), datetime.now()]
+        )
+        
+        if len(date_range) == 2:
+            farm_geometry = ee.Geometry.Point([farm_data['longitude'], farm_data['latitude']])
+            ndvi_collection = calculate_ndvi(farm_geometry, date_range[0], date_range[1])
+            
+            # Plot NDVI time series
+            ndvi_values = ndvi_collection.aggregate_array('NDVI').getInfo()
+            dates = ndvi_collection.aggregate_array('system:time_start').getInfo()
+            
+            fig = px.line(
+                x=[datetime.fromtimestamp(d/1000) for d in dates],
+                y=ndvi_values,
+                title="NDVI Time Series",
+                labels={"x": "Date", "y": "NDVI"}
+            )
+            st.plotly_chart(fig)
+    
+    with col2:
+        st.subheader("اطلاعات آب و هوا")
+        
+        # Weather data
+        weather_data = get_weather_data(
+            farm_data['latitude'],
+            farm_data['longitude'],
+            "ed47316a45379e2221a75f813229fb46"
+        )
+        
+        # Display current weather
+        st.metric(
+            "دمای فعلی",
+            f"{weather_data['main']['temp']}°C",
+            f"{weather_data['main']['temp_max'] - weather_data['main']['temp_min']}°C"
+        )
+        
+        st.metric(
+            "رطوبت",
+            f"{weather_data['main']['humidity']}%"
+        )
+        
+        # Wind data from Windy.com
+        windy_data = get_windy_forecast(
+            farm_data['latitude'],
+            farm_data['longitude'],
+            "ohZom14Dr9JPKOaeUoY8nBxKdiK2yURW"
+        )
+        
+        # AI Recommendations
+        st.subheader("توصیه‌های هوشمند")
+        if farm_data['age'] > 8:
+            st.warning("زمان برداشت نزدیک است")
+        
+        # Generate PDF Report
+        if st.button("دانلود گزارش"):
+            pdf_buffer = generate_report(selected_farm, farm_data, weather_data, ndvi_values)
+            st.download_button(
+                "دانلود PDF",
+                pdf_buffer,
+                file_name=f"farm_report_{selected_farm}.pdf",
+                mime="application/pdf"
+            )
+
+def generate_report(farm_name, farm_data, weather_data, ndvi_values):
     buffer = io.BytesIO()
     c = canvas.Canvas(buffer, pagesize=letter)
-    c.setFillColorRGB(1,1,1)
-    c.drawString(100, 750, f"Field Report: {selected_data['name']}")
-    c.drawString(100, 730, f"NDVI Index: {mean_ndvi:.2f}")
-    c.drawString(100, 710, f"Weather Condition: {weather['weather']}")
+    
+    # Add report content
+    c.drawString(100, 750, f"گزارش مزرعه {farm_name}")
+    c.drawString(100, 700, f"سن مزرعه: {farm_data['age']}")
+    c.drawString(100, 650, f"واریته: {farm_data['variety']}")
+    
     c.save()
-    return buffer.getvalue()
-
-if st.button("Generate PDF Report"):
-    pdf = create_pdf_report()
-    st.download_button(
-        label="Download Report",
-        data=pdf,
-        file_name="field_report.pdf",
-        mime="application/pdf"
-    )
-
-# --------------------------
-# Hidden API Integration
-# --------------------------
-# Windy Map Integration (Hidden branding)
-st.components.v1.html(f"""
-<iframe src="https://embed.windy.com/embed2.html?lat={selected_data['latitude']}&lon={selected_data['longitude']}&detailLat=31.534&detailLon=48.724&width=650&height=450&zoom=12&level=surface&overlay=wind&product=ecmwf&menu=&message=&marker=true&calendar=now&pressure=&type=map&location=coordinates&detail=&metricWind=default&metricTemp=default&radarRange=-1"
-width="100%" height="450" frameborder="0" style="border:0; margin-top:-30px"></iframe>
-""", height=450)
+    buffer.seek(0)
+    return buffer
 
-with open("requirements.txt", "w") as f:
-    f.write("""
+if __name__ == "__main__":
+    main()