Update app.py

app.py

@@ -27,7 +27,7 @@ st.set_page_config(
     initial_sidebar_state="collapsed"
 )
 
-# Professional CSS with horizontal button menu
+# Modern and vibrant CSS with graphical menu
 st.markdown("""
 <style>
     @font-face {
@@ -38,32 +38,54 @@ st.markdown("""
     body {
         background: linear-gradient(135deg, #e8f5e9 0%, #c8e6c9 100%);
         color: #1a3c34;
+        margin: 0;
+        padding: 0;
+    }
+    .top-bar {
+        background: linear-gradient(90deg, #1b5e20 0%, #4caf50 100%);
+        padding: 20px;
+        text-align: center;
+        color: white;
+        font-size: 1.2rem;
+        box-shadow: 0 4px 15px rgba(0,0,0,0.3);
+        border-radius: 0 0 20px 20px;
+        animation: slideDown 0.8s ease-out;
     }
     .top-menu {
         display: flex;
         justify-content: center;
-        gap: 15px;
+        gap: 20px;
         padding: 20px;
-        background: linear-gradient(180deg, #1b5e20 0%, #4caf50 100%);
-        border-radius: 0 0 20px 20px;
-        box-shadow: 0 10px 30px rgba(0,0,0,0.2);
-        animation: slideDown 0.8s ease-out;
+        background: rgba(255, 255, 255, 0.1);
+        backdrop-filter: blur(10px);
+        border-radius: 20px;
+        box-shadow: 0 8px 32px rgba(0,0,0,0.2);
+        margin: 20px auto;
+        max-width: 1200px;
     }
     .top-menu button {
-        background: linear-gradient(45deg, #1b5e20, #66bb6a);
+        background: linear-gradient(135deg, #2e7d32 0%, #81c784 100%);
         color: white;
         border: none;
-        border-radius: 30px;
-        padding: 0.8rem 2rem;
-        font-size: 1.1rem;
-        box-shadow: 0 5px 15px rgba(0,0,0,0.2);
-        transition: all 0.4s ease;
+        border-radius: 15px;
+        padding: 15px 25px;
+        font-size: 1.2rem;
+        font-weight: bold;
+        box-shadow: 0 5px 15px rgba(0,0,0,0.3);
+        transition: all 0.3s ease;
         cursor: pointer;
+        display: flex;
+        align-items: center;
+        gap: 10px;
     }
     .top-menu button:hover {
         transform: translateY(-5px) scale(1.05);
-        box-shadow: 0 8px 25px rgba(0,0,0,0.3);
-        background: linear-gradient(45deg, #2e7d32, #81c784);
+        box-shadow: 0 10px 25px rgba(0,0,0,0.4);
+        background: linear-gradient(135deg, #388e3c 0%, #a5d6a7 100%);
+    }
+    .top-menu button:active {
+        transform: translateY(2px);
+        box-shadow: 0 3px 10px rgba(0,0,0,0.2);
     }
     .card {
         background: rgba(255, 255, 255, 0.97);
@@ -160,14 +182,16 @@ if 'heights_df' not in st.session_state:
         'Variety', 'Age', 'Area', 'Channel', 'Administration'
     ])
 
-# Convert Gregorian to Jalali date
-def to_jalali(date_str):
-    try:
-        greg_date = datetime.strptime(date_str, '%Y-%m-%d')
-        jalali_date = jdatetime.date.fromgregorian(date=greg_date)
-        return jalali_date.strftime('%Y/%m/%d')
-    except:
-        return date_str
+# Convert Gregorian to Jalali date and get weekday
+def get_jalali_date_and_day():
+    greg_date = datetime.now()
+    jalali_date = jdatetime.date.fromgregorian(date=greg_date)
+    weekdays = ["دوشنبه", "سه‌شنبه", "چهارشنبه", "پنجشنبه", "جمعه", "شنبه", "یکشنبه"]
+    weekday = weekdays[jalali_date.weekday()]
+    return jalali_date.strftime('%Y/%m/%d'), weekday
+
+jalali_date, current_day = get_jalali_date_and_day()
+st.markdown(f'<div class="top-bar">تاریخ: {jalali_date} | روز: {current_day}</div>', unsafe_allow_html=True)
 
 # Enhanced Earth Engine Map Function
 @st.cache_data
@@ -222,6 +246,60 @@ def create_map(farm_id, date_str, layer_type="NDVI"):
         st.error(f"خطا در ایجاد نقشه: {e}")
         return None
 
+# Cached Scoring Function
+@st.cache_data
+def calculate_farm_scores(date_str, farms):
+    if not earth_engine_initialized:
+        return None
+    scoring_data = []
+    date_obj = datetime.strptime(date_str, '%Y-%m-%d')
+    start_date = (date_obj - timedelta(days=7)).strftime('%Y-%m-%d')
+    end_date = (date_obj - timedelta(days=1)).strftime('%Y-%m-%d')
+    
+    for _, farm in farms.iterrows():
+        farm_id = farm['نام']
+        lat, lon = farm['عرض جغرافیایی'], farm['طول جغرافیایی']
+        region = ee.Geometry.Point([lon, lat]).buffer(1500)
+        
+        s2 = ee.ImageCollection('COPERNICUS/S2_SR') \
+            .filterDate(start_date, end_date) \
+            .filterBounds(region) \
+            .sort('CLOUDY_PIXEL_PERCENTAGE') \
+            .first()
+        
+        if s2:
+            ndvi = s2.normalizedDifference(['B8', 'B4']).rename('NDVI')
+            ndvi_value = ndvi.reduceRegion(reducer=ee.Reducer.mean(), geometry=region, scale=10).get('NDVI').getInfo() or 0
+            
+            ndmi = s2.normalizedDifference(['B8', 'B11']).rename('NDMI')
+            ndmi_value = ndmi.reduceRegion(reducer=ee.Reducer.mean(), geometry=region, scale=10).get('NDMI').getInfo() or 0
+            
+            if ndvi_value < 0.1:
+                growth_status = "بدون پوشش گیاهی"
+            elif ndvi_value < 0.3:
+                growth_status = "تنش‌دار"
+            elif ndvi_value < 0.6:
+                growth_status = "نرمال"
+            else:
+                growth_status = "خوب"
+            
+            if ndmi_value < -0.2:
+                moisture_status = "تنش‌دار"
+            elif ndmi_value < 0.2:
+                moisture_status = "نرمال"
+            else:
+                moisture_status = "خوب"
+            
+            scoring_data.append({
+                'مزرعه': farm_id,
+                'NDVI': ndvi_value,
+                'وضعیت رشد': growth_status,
+                'NDMI': ndmi_value,
+                'وضعیت رطوبت': moisture_status
+            })
+    
+    return pd.DataFrame(scoring_data)
+
 # Machine Learning Model
 @st.cache_resource
 def train_growth_model(_heights_df):
@@ -237,16 +315,16 @@ def train_growth_model(_heights_df):
 growth_model = train_growth_model(st.session_state.heights_df)
 
 # Top menu with buttons
-menu_options = ["داشبورد", "ورود اطلاعات", "گزارش‌گیری", "تحلیل داده‌ها", "پیش‌بینی رشد", "امتیازدهی مزارع", "تنظیمات"]
+menu_options = ["📊 داشبورد", "✍️ ورود اطلاعات", "📈 گزارش‌گیری", "🔍 تحلیل داده‌ها", "🔮 پیش‌بینی رشد", "⭐ وضعیت مزارع", "⚙️ تنظیمات"]
 cols = st.columns(len(menu_options))
 for i, option in enumerate(menu_options):
     with cols[i]:
         if st.button(option, key=f"menu_{option}"):
-            st.session_state['selected_tab'] = option
+            st.session_state['selected_tab'] = option.split(' ')[1]
 
 # Dashboard
 if st.session_state.get('selected_tab') == "داشبورد":
-    st.markdown('<h1 class="header">🌿 داشبورد هوشمند پایش</h1>', unsafe_allow_html=True)
+    st.markdown('<h1 class="header">🌿 مطالعات کاربردی کشت و صنعت دهخدا </h1>', unsafe_allow_html=True)
     st.markdown('<div class="card">', unsafe_allow_html=True)
     
     heights_df = st.session_state.heights_df
@@ -549,69 +627,28 @@ elif st.session_state.get('selected_tab') == "امتیازدهی مزارع":
     else:
         date = st.date_input("انتخاب تاریخ", datetime.now(), key="score_date")
         date_str = date.strftime('%Y-%m-%d')
-        start_date = (date - timedelta(days=7)).strftime('%Y-%m-%d')
-        end_date = (date - timedelta(days=1)).strftime('%Y-%m-%d')
         
-        if st.button("محاسبه امتیازها", key="score_btn"):
-            with st.spinner("در حال محاسبه امتیازها..."):
-                scoring_data = []
-                for _, farm in coordinates_df.iterrows():
-                    farm_id = farm['نام']
-                    lat, lon = farm['عرض جغرافیایی'], farm['طول جغرافیایی']
-                    region = ee.Geometry.Point([lon, lat]).buffer(1500)
-                    
-                    # NDVI for growth
-                    s2 = ee.ImageCollection('COPERNICUS/S2_SR') \
-                        .filterDate(start_date, end_date) \
-                        .filterBounds(region) \
-                        .sort('CLOUDY_PIXEL_PERCENTAGE') \
-                        .first()
-                    ndvi = s2.normalizedDifference(['B8', 'B4']).rename('NDVI')
-                    ndvi_value = ndvi.reduceRegion(reducer=ee.Reducer.mean(), geometry=region, scale=10).get('NDVI').getInfo() or 0
-                    
-                    # NDMI for moisture
-                    ndmi = s2.normalizedDifference(['B8', 'B11']).rename('NDMI')
-                    ndmi_value = ndmi.reduceRegion(reducer=ee.Reducer.mean(), geometry=region, scale=10).get('NDMI').getInfo() or 0
-                    
-                    # Scoring based on NDVI
-                    if ndvi_value < 0.1:
-                        growth_status = "بدون پوشش گیاهی"
-                    elif ndvi_value < 0.3:
-                        growth_status = "تنش‌دار"
-                    elif ndvi_value < 0.6:
-                        growth_status = "نرمال"
-                    else:
-                        growth_status = "خوب"
-                    
-                    # Scoring based on NDMI
-                    if ndmi_value < -0.2:
-                        moisture_status = "تنش‌دار"
-                    elif ndmi_value < 0.2:
-                        moisture_status = "نرمال"
+        filtered_farms = coordinates_df[coordinates_df['نام'].isin(farms_df[farms_df['روز'] == current_day]['مزرعه'])]
+        if filtered_farms.empty:
+            st.warning(f"هیچ مزرعه‌ای برای روز {current_day} یافت نشد.")
+        else:
+            if st.button("محاسبه امتیازها", key="score_btn"):
+                with st.spinner("در حال محاسبه امتیازها..."):
+                    scoring_df = calculate_farm_scores(date_str, filtered_farms)
+                    if scoring_df is not None and not scoring_df.empty:
+                        st.subheader(f"امتیازدهی مزارع روز {current_day} (هفته قبل از {to_jalali(date_str)})")
+                        st.dataframe(scoring_df, use_container_width=True)
+                        
+                        growth_counts = scoring_df['وضعیت رشد'].value_counts()
+                        moisture_counts = scoring_df['وضعیت رطوبت'].value_counts()
+                        st.write("**خلاصه وضعیت رشد:**")
+                        for status, count in growth_counts.items():
+                            st.write(f"{status}: {count} مزرعه")
+                        st.write("**خلاصه وضعیت رطوبت:**")
+                        for status, count in moisture_counts.items():
+                            st.write(f"{status}: {count} مزرعه")
                     else:
-                        moisture_status = "خوب"
-                    
-                    scoring_data.append({
-                        'مزرعه': farm_id,
-                        'NDVI': ndvi_value,
-                        'وضعیت رشد': growth_status,
-                        'NDMI': ndmi_value,
-                        'وضعیت رطوبت': moisture_status
-                    })
-                
-                scoring_df = pd.DataFrame(scoring_data)
-                st.subheader(f"امتیازدهی مزارع (هفته قبل از {to_jalali(date_str)})")
-                st.dataframe(scoring_df, use_container_width=True)
-                
-                # Summary
-                growth_counts = scoring_df['وضعیت رشد'].value_counts()
-                moisture_counts = scoring_df['وضعیت رطوبت'].value_counts()
-                st.write("**خلاصه وضعیت رشد:**")
-                for status, count in growth_counts.items():
-                    st.write(f"{status}: {count} مزرعه")
-                st.write("**خلاصه وضعیت رطوبت:**")
-                for status, count in moisture_counts.items():
-                    st.write(f"{status}: {count} مزرعه")
+                        st.warning("داده‌ای برای امتیازدهی در این بازه زمانی یافت نشد.")
     st.markdown('</div>', unsafe_allow_html=True)
 
 # Settings